Federated learning (FL) faces significant challenges in medical image analysis due to data heterogeneity among clients, where balancing personalization and generalization is challenging. Existing methods often struggle to achieve both objectives simultaneously, as excessive personalization reduces generalization, while over-generalization weakens adaptation to client-specific features. To address these challenges, we propose a Perceptual Hashing Hypernetwork Personalized Federated Learning (PHH-FL) to enhance both personalization and generalization. PHH-FL first uses a perceptual hashing algorithm to construct a similarity matrix that captures data distribution differences among clients and employs a hypernetwork to generate personalized parameters for each client. Meanwhile, the shared hypernetwork is introduced to promote knowledge transfer between clients, thereby enhancing the generalization ability of the local model. By selectively generating parameters for the initial layers of the target network, PHH-FL reduces computational and communication costs while maintaining performance. Experiments on medical image classification and segmentation tasks show that PHH-FL outperforms state-of-the-art methods. Ablation studies further demonstrate that the proposed framework effectively balances personalization and generalization.

Noting that single mode fault diagnosis methods have limitations in complex fault detection, and single fault diagnosis models have low accuracy and robustness, this article proposes a multimode fault diagnosis method for rotating mechanical equipment based on feature-level dynamic fusion and decision-level improved Dempster–Shafer (DS) evidence theory. First, by introducing time-domain, frequency-domain, and time-frequency domain data, three fault diagnosis submodels: convolutional neural network, long short-term memory network, and random forest are constructed for extracting and processing three different modal input signals. Second, a dynamic fusion strategy is developed during feature-level fusion to gradually optimize the features of different modalities, to improve the robustness and accuracy of fault diagnosis. Preliminary fault classification results for each submodel are obtained through different feature-level fusion methods. Third, the improved DS evidence theory is used to fuse the fault classification information of each submodel, and the evidence is corrected from both the performance and fault type aspects of the submodels, to obtain the final fault diagnosis result after fusion. Finally, the superiority of the proposed method is verified through experiments.

Under varying operating conditions, the operating state of rotating equipment is influenced by multiple factors, presenting the characteristics of complex and changeable data distribution and high nonlinearity. Deep belief networks (DBN) possess powerful feature learning capabilities, making them suitable for fault diagnosis under such complex conditions. However, traditional DBNS have limitations such as structural design relying on experience and being prone to getting stuck in local optima. At the same time, the adaptability to the differences in distribution of data under different working conditions is insufficient, resulting in limited accuracy of fault diagnosis and model generalization ability. To cope with these issues, a fault diagnosis method based on improved DBN is presented in this paper. Firstly, a multiple kernel principal component analysis (MKPCA) is proposed. The radial basis function(RBF) kernel and the polynomial function kernel are combined, and the weights of the kernel functions are dynamically adjusted according to different characteristics to improve the processing ability of KPCA for complex data. Secondly, the Particle Swarm Optimization algorithm (PSO) is introduced to improve the network structure and parameters of the DBN. Then, the shortcomings of traditional DBN structure design that relies on experience and is prone to fall into local optimum is overcame, achieving adaptive optimization of network structure and parameters. In addition, considering the differences in the distribution of data under different working conditions, this paper combines semi-supervised domain adaptation with DBN and introduces the multiple kernel maximum mean difference (MK-MMD), forcing DBN to learn general features, reducing the distribution differences between domains, and transferring the knowledge learned in the source domain to the target domain, thus solving the problem of insufficient generalization ability of the model caused by changes in working conditions. Finally, through experimental verification, the results indicate that the method introduced in this paper significantly enhances the accuracy and generalization ability of fault diagnosis of rotating equipment under varying operating conditions.

This paper investigates the event-triggered guaranteed-performance security control problem for nonlinear systems subject to hybrid cyber-attacks (denial-of-service (DoS) attacks and deception attacks). First, a fuzzy-model-based description approach is employed to characterize the nonlinear system, effectively capturing its complex dynamic characteristics. Second, to alleviate the communication burden in bandwidth-constrained networks, a history-dependent event-triggering mechanism incorporating historical transmission data is designed, dynamically optimizing information transmission efficiency between sensors and controllers. Third, a security control framework with prescribed performance metrics is constructed. By integrating Lyapunov functional theory and matrix decomposition techniques, sufficient conditions ensuring system stability are rigorously derived. This guarantees that the proposed guaranteed-performance security control scheme can effectively counteract malicious impacts induced by cyber-attacks. Finally, comprehensive simulation case studies validate the effectiveness of the proposed security control method, demonstrating its robustness against hybrid attack patterns while maintaining desired control performance. The results show that, compared to the traditional event-triggered mechanism (ETM) and the dynamic event-triggered mechanism (DETM), the dynamic memory event-triggered mechanism (DMETM) proposed in this paper reduces the number of triggers by approximately 63.87$\%$and 18.49$\%$, respectively; Compared to the recent Adaptive Memory Event Triggering Mechanism (AMETM), it further reduces communication overhead by approximately 8.12$\%$. Furthermore, the system maintains exponential mean-square stability under mixed attacks and achieves the specified$H\_{\infty }$performance level.

Multimodal emotion recognition in conversations is inherently challenging due to ambiguous cues, modality conflicts, and temporal dynamics, all of which contribute to complex and diverse uncertainty sources. While some recent methods incorporate uncertainty modeling, they often focus on overall prediction confidence, without explicitly distinguishing the different sources of uncertainty introduced by underlying factors. To address these challenges, we propose a novel Emotion-Specific Evidence Decomposition framework (ESED) that leverages evidential deep learning to explicitly model and disentangle multimodal emotional uncertainty. Rather than directly fusing features, ESED decomposes each modality's evidence into three interpretable components: (1) emotion-consistent evidence, capturing shared emotional cues across modalities; (2) emotion-specific evidence, highlighting the unique emotional role of each modality; and (3) dynamic evidence, modeling utterance-level temporal variations. These components are adaptively weighted based on emotional intensity, ambiguity, and dynamicity, quantified via prediction entropy, inter-modal divergence, and temporal variance. The final prediction is obtained through an adaptive fusion of these weighted components. Extensive experiments demonstrate that ESED outperforms the state-of-the-art methods on the MELD and IEMOCAP datasets, demonstrating the effectiveness of our proposed method.

The operating conditions in complex industrial processes are often dynamic, unpredictable, and difficult to label, leading to significant challenges in predicting industrial quality indices. To address these challenges and improve prediction accuracy under Multiple Operating Conditions (MOC), we propose a Coarse-Fine Meta-learning Framework (CFMF). Initially, multiple Coarse Models are established using historical operating condition data. We then introduce a MOC-Dynamic Time Warping (DTW) strategy, which utilizes small-batch data from new operating conditions to identify similar time-series characteristics from historical conditions. These similar conditions data are used to train a meta-learning model for the Coarse Models based on Stacking, ultimately resulting in a fine model for quality index prediction. In industrial experiments, we compare the CFMF with classical multi-model learning strategies, and the results demonstrate that the proposed CFMF achieves superior prediction performance on the target domain test set.

Ultrasound is an important routine screening modality for breast cancer. Breast ultrasound screening is a dynamic process, and clinical practice involves radiologists recording representative frames during dynamic breast scanning for subsequent diagnosis. However, existing computer-assisted diagnosis methods often concentrate on dull diagnostic results by analyzing these representative frames and ignore the valuable information in the dynamic examination process that facilitates diagnosis. Moreover, breast lesions could exhibit various characteristic differences during scanning, and effective learning of lesion representations is challenging and may affect the clinical interpretability of the methods. To this end, we draw insights from the behavior of radiologists during the dynamic breast examination and leverage the knowledge of breast anatomy to propose a clinical knowledge-aware framework for lesion detection and classification of breast lesions in ultrasound videos. It is equipped with global-local attentive aggregation and a dynamic allocation mechanism that simulates the behavior of radiologists searching for diagnostic clues, thus integrating local localization and global semantic information from the video into the feature representation of the lesion. An anatomically-aware transformer is also designed to refine the lesion feature representation using spatial relationships within and across different anatomical layers of the breast anatomy. Extensive experiments show that the proposed framework can achieve competitive performance in both lesion detection and video classification tasks while exhibiting good clinical availability and interpretability, with an average precision of 40.80% and an AUC of 85.86% on our constructed breast video dataset and an average precision of 39.79% and an AUC of 87.04% on a publicly available dataset.

Multivariate time series forecasting estimates future development by capturing variable relationships and constructing temporal regular, which is widely used in many scenarios, including industrial production, economic development, and disease prediction. Although the existing deep learning methods have achieved impressive results in multivariate time series forecasting tasks, the existing methods only emphasize the prediction performance and ignore the widespread issue of missing data in the real world. This article proposes a robust multivariate correlation self-distillation Transformer framework for incomplete time series data forecasting. The proposed method first decouples the interinference of historical series and the exter-inference of future series into two stages. The first stage focuses on the reconstruction of historical series, while the second stage focuses on the prediction of future series. Then, a novel multivariate correlation Transformer is designed as the basic component of the network, which can perform feature inference from both multivariate relationships and single-variate temporal regular. Finally, a variable correlation self-distillation method is proposed to self-distill the more complete variable relationship from the exter-inference stage to the interinference stage. The proposed method is verified on eight real-world datasets, and both qualitative and quantitative results show that the proposed method has good performance.

Different from most previous design methods of stochastic feedforward systems, the decentralized nested saturation control strategy is developed in this article to stabilize a kind of large-scale stochastic systems with strict feedforward structure. To analyze the boundedness of system states, an improved criterion for the boundedness of the solution of stochastic system is established. The decentralized controller constructed for each subsystem adopts the form of nested saturation feedback with the adjustable saturation levels. Under these saturation levels that satisfy some suitable conditions, a bottom–up analysis technique is applied to recursively prove that the decentralized controllers can ensure that all states and inputs of stochastic closed-loop system are not only bounded almost surely, but also eventually globally asymptotically stable in probability. The efficiency of decentralized nested saturation control is validated through the ball-beam system with stochastic disturbance and a numerical example.

Thanks to its excellent nonlinear representation ability, deep neural network (DNN) has been designed to aid canonical correlation analysis (CCA) for stable kernel representation. These DNN-aided CCA methods make use of correlation as the optimization objective and exploit its change to distinguish system states. However, the maximum correlation-based training strategy lacks robustness to effectively tackle system false alarms caused by changes in operating conditions. To this end, this work proposes a self-attention sliding window enhanced CCA (SaECCA) for incipient fault detection in dynamic industrial processes. The main novelties of this work include the following: first, with the aid of DNNs, an adaptive weighting mechanism with self-attention is developed to amplify the incipient fault information; second, residual estimation-based, a novel and robust optimization objective for nonlinear CCA is formulated; third, an SaECCA-based fault detection algorithm is designed, whose convergence and detectability are illustrated via theoretical analysis. Studies on a three-tank system simulation and a multiphase flow industrial process are presented to verify the effectiveness of the proposed SaECCA method.

This article designs a data-driven security defense and monitoring approach that involves encrypted transmission and attack detection to defend cyber-physical systems (CPS) against stealthy attacks. The approach starts by using subspace theory to achieve data-driven coprime factorization of the closed-loop CPS. Based on this, the physical dynamics are encrypted from a control perspective to minimize the risk of information leakage and hinder the creation of stealthy attacks. Meanwhile, the security monitoring approach is designed using the same offline-learned coprime factorization. This approach is effective in detecting and distinguishing between cyber-physical attacks and machine-induced faults, which enables effective maintenance measures to be taken for different anomalies. The proposed encrypted transmission and security monitoring approach provides a comprehensive defense against nonstealthy and stealthy attacks. The effectiveness of our work is illustrated through a numerical example and experimental results on a Mecanum-wheeled vehicle platform.

Quality prediction is essential for optimizing operations and making timely decisions in industrial processes. However, the dynamic nature of industrial data, characterized by different sampling periods, presents significant challenges for quality prediction. The relationship between quality indices and various industrial data with differing sampling periods is complex and dynamically correlated in both spatial and temporal dimensions. To address this issue, we propose an adaptive multiresampling learning (AMRL) network that performs deep spatial-temporal feature mining and extraction from dual-scale data, facilitating multistep industrial quality prediction. The AMRL network leverages principal component scores of fast-scale process data and an adaptive multiresampling module to construct multiple fast-scale resampling channels adaptively. The cross-scale deep convolutional neural network and the multichannel self-attention module are then employed to capture spatial-temporal features and selectively focus on critical regions within the multiresampling sequences. We compared and evaluated the proposed method against eight state-of-the-art methods using real industrial datasets. The comparison results demonstrate the superior performance of the AMRL in multistep industrial quality prediction.

Implementing artificial intelligence (AI) in safety-critical systems comes with challenges that are also common in the implementation of AI in other domains. However, the consequences are distinct due to the inherent nature of safety-critical systems, where failure can lead to potential harm to people and the environment. This necessitates special consideration in AI design. When deploying AI in safety-related systems, it is necessary to ensure that the AI’s performance aligns with its training results and remains within acceptable bounds during operation to uphold the system’s safety. This paper focuses on addressing the quality assurance of AI during operation within safety-critical systems. Through a literature review and analysis, we aim to provide a comprehensive overview of common challenges encountered during operation and propose an extended framework of the ISO/IEC TR 5469 for a quality assurance process of AI within safety-critical systems. The extension adds monitoring of the AI component and a component for updating the AI, as well as several adjustments that are connected to those additions. As part of the monitoring, we introduce an indexing framework for the degradation of AI, based on a traffic-light design. The results, together with a comprehensive road map for future work, provide a foundation for further research and developing a holistic quality assurance of AI during operation.

This article designs a data-driven unsupervised defense scheme for nonlinear systems by proposing a machine learning approach called gate recurrent unit-based modified denoising and stable image representation-aided autoencoders. The proposed scheme decomposes original data into two subspaces through orthogonal projection. For secure transmission, information related to the system's dynamics, which is in the image space of the controlled system, is hidden through filtering, whereas only the dynamic-independent information is plaintext for transmission, which supplements the cryptographic encryption methods from a control perspective. Moreover, attack detection for nonstealthy and stealthy attacks is achieved simultaneously under the same framework. A case study is conducted for validation on the a hardware-in-the-loop platform with a mecanum-wheeled vehicle. The comparative experiments with well-known unsupervised data-driven methods show the high detection accuracy of the proposed defense scheme for nonstealthy and stealthy attacks and the excellent encryption capability.

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for energy supply systems. In conventional data-driven RUL prediction approaches, the battery's degradation mechanism is difficult into incorporate in the RUL prediction. Furthermore, there are notable limitations in reflecting the significance of different time instances, and the uncertainty in the degradation process. Consequently, a novel data-model interactive RUL prediction approach based on particle filter-temporal attention mechanism-bidirectional gated recurrent unit (PF-BiGRU-TSAM) is proposed. Specifically, BiGRU-TSAM is trained offline through historical data, which assigns corresponding significance to battery capacities at different time instances. Moreover, regarding the interactive data-model for the online prediction phase based on PF-BiGRU-TSAM, the advantages of data-driven and model-based approaches are integrated, which accomplishes the purpose of modifying each other. The proposed PF-BiGRU-TSAM approach is validated with a real-world battery dataset. Experimental results demonstrate the proposed approach is better than some published approaches. Taking the 50th operational cycle of the four batteries B0005, B0006, B0007, and B0018 in the dataset as an instance, the absolute errors of the proposed PF-BiGRU-TSAM are 0, 1, 3, 3, respectively, which represents the proposed approach has an excellent performance.

In this article, an adaptive neural containment control for a class of nonlinear multiagent systems considering actuator faults is introduced. By using the general approximation property of neural networks, a neuro-adaptive observer is designed to estimate unmeasured states. In addition, in order to reduce the computational burden, a novel event-triggered control law is designed. Furthermore, the finite-time performance function is presented to improve the transient and steady-state performance of the synchronization error. Utilizing the Lyapunov stability theory, it will be shown that the closed-loop system is cooperatively semiglobally uniformly ultimately bounded (CSGUUB), and the followers' outputs reach the convex hull constructed by the leaders. Moreover, it is shown that the containment errors are limited to the prescribed level in a finite time. Eventually, a simulation example is presented to corroborate the capability of the proposed scheme.

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks. Based on these observations, this article proposes a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. Specifically, in the unsupervised condition, a bearing fault detection approach based on modified denoising autoencoder (DAE) with self-attention mechanism for bottleneck layer (MDAE-SAMB) is proposed in the integrated scheme, which only uses the healthy data for training. The self-attention mechanism is introduced into the neurons in the bottleneck layer, which can assign different weights to the neurons in the bottleneck layer. Moreover, the transfer learning based on representation learning is proposed for few-shot fault classification. Only a few fault samples are used for offline training, and high-accuracy online bearing fault classification is achieved. Finally, according to the known fault data, the unknown bearing faults can be effectively identified. A bearing dataset generated by rotor dynamics experiment rig (RDER) and a public bearing dataset demonstrates the applicability of the proposed integrated fault diagnosis scheme.

As digital twins gain prevalence across various industries, the need for a structured selection process becomes crucial. This paper proposes using the Quality Function Deployment (QFD) and Analytic Hierarchy Process (AHP) to help users determine the suitable quality level of digital twin models based on their actual needs. Our proposed methodology integrates the quality goals and existing resources of organizations to provide a comprehensive and systematic approach to the selection of digital twin models. This approach guides organizations to identify the importance of Engineering Characteristics (ECs), enabling efficient resource allocation for the design and operation of digital twins. Further, it facilitates comparative performance analysis against other models, thus enriching the understanding of digital twins' capabilities. This methodology fills a significant gap in the current research landscape and has the potential to improve the quality and effectiveness of business operations. Future research directions include the validation and enhancement of the methodology through case studies and an exploration of additional influencing factors in digital twin design.

Amid the growth of Industry 4.0 and digital transformation, Smart Maintenance and AI-assisted maintenance solutions are gaining significant traction. However, most studies focus on the application of sophisticated AI algorithms in maintenance optimization while overlooking how AI can directly assist maintenance and reliability (M&R) professionals in a more comprehensible manner. This study fills this gap by investigating the potential of language models, specifically the GPT-3.5 and GPT-4 models developed by OpenAI, in aiding M&R practitioners. We used the Certified Maintenance & Reliability Professional (CMRP) exam, accredited by the American National Standards Institute (ANSI) to assess aptitude within 5 Pillars of the Society for Maintenance & Reliability Professionals Body of Knowledge, as a testing ground to evaluate and compare the performance and reasoning capabilities of the two AI models to that of human experts. Our findings suggest that while GPT-4 outperformed GPT-3.5 in providing self-consistent explanations and overall scoring, both models exhibited potential weaknesses, particularly in arithmetic operations and handling domain-specific terminologies. Despite these challenges, our results demonstrate the potential of these AI models as tools to support various M&R tasks, paving the way for more practical and comprehensive AI assistance in smart maintenance.

With the development of Cyber-Physical Systems (CPSs), many industrial facilities have realized remote control and monitoring. However, the widespread of CPSs has brought new issues and challenges in terms of security. Attackers can exploit vulnerabilities induced by network communication, tamper with transmitted data, and cause serious accidents through carefully designed covert attacks. This paper proposes a residual-driven comprehensive defense scheme based on the coprime factorization technique to address the threat posed by concealed CPS attacks. The novel scheme protects CPS from stealth cyber-physical attacks through secure transmission and attack detection. In particular, a secure transmission method is first introduced to prevent information leakage from the source. The pivotal idea is to convert confidential transmission control and measurement signals into non-essential filtered residual signals. It contributes to the reduction of information leakage and helps reduce the risks of stealth attacks. Then, under the same residual-driven framework, a stealth attack detection approach is put forward. It can eliminate false alarms caused by system faults, and therefore, achieve superior efficacy in detection accuracy under stealth attacks. Finally, simulation research is conducted on the F-404 engine to verify the effectiveness and performance of the proposed scheme and approach.

Bearings are essential to the reliable operation of rotating machinery in manufacturing processes. There is a rising demand for accurate bearing remaining useful life (RUL) predictions. The data-driven technique for predicting RUL of bearing has demonstrated promising prospects to facilitate intelligent prognostics. This article proposes a new data-driven prediction framework for bearing RUL utilizing an integration of empirical mode decomposition, random forest (RF), and Bayesian optimization. The proposed framework consists of two main phases: 1) feature extraction and 2) RUL prediction. The first phase of this framework focused on decomposing the empirical input signals using empirical mode decomposition into distinct frequency bands to filter out irrelevant frequencies and determine the fault characteristics of the signals. In the second phase, the RUL prediction is then carried out by an RFs-based model with its hyperparameters tuned by Bayesian optimization. The proposed approach is validated using datasets obtained from an actual run-to-failure experiment of roller bearings. The experiment results significantly improved compared to the standard data-driven and stochastic approaches.

Knee segmentation and landmark localization from 3D MRI are two significant tasks for diagnosis and treatment of knee diseases. With the development of deep learning, Convolutional Neural Network (CNN) based methods have become the mainstream. However, the existing CNN methods are mostly single-task methods. Due to the complex structure of bone, cartilage and ligament in the knee, it is challenging to complete the segmentation or landmark localization alone. And establishing independent models for all tasks will bring difficulties for surgeon's clinical using. In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. We use a shared encoder for feature extraction, then SDMT utilizes the spatial dependence of segmentation results and landmark position to mutually promote the two tasks. Specifically, SDMT adds spatial encoding to the features, and a task hybrided multi-head attention mechanism is designed, in which the attention heads are divided into the inter-task attention head and the intra-task attention head. The two attention head deal with the spatial dependence between two tasks and correlation within the single task, respectively. Finally, we design a dynamic weight multi-task loss function to balance the training process of two task. The proposed method is validated on our 3D knee MRI multi-task datasets. Dice can reach 83.91% in the segmentation task, and MRE can reach 2.12 mm in the landmark localization task, it is competitive and superior over other state-of-the-art single-task methods.

In this article, the problem of distributed finite-time consensus control for a class of stochastic nonlinear multiagent systems (MASs) (with directed graph communication) in the presence of unknown dynamics of agents, stochastic perturbations, external disturbances (mismatched and matched), and input saturation nonlinearities is addressed and studied. By combining the backstepping control method, the command filter technique, a finite-time auxiliary system, and artificial neural networks, innovative control inputs are designed and proposed such that outputs of follower agents converge to the output of the leader agent within a finite time. Radial-basis function neural networks (RBFNNs) are employed to approximate unknown dynamics, stochastic perturbations, and external disturbances. To overcome the complexity explosion problem of the conventional backstepping method, a novel finite-time command filter approach is proposed. Then, to deal with the destructive effects of input saturation nonlinearities, the finite-time auxiliary system is designed and developed. By mathematical analysis, it is proven that the mentioned MAS (injected by the proposed control inputs) is semiglobally finite-time stable in probability (SGFSP) and all consensus tracking errors converge to a small neighborhood of the zero during a finite time. Finally, a numerical simulation onto a group of four single-link robot manipulators is carried out to illustrate the effectiveness of the suggested control scheme.

High-performance and reliable control of systems that are highly dynamic and open-loop unstable is challenging but of considerable practical interest. Thus, this article investigates the performance optimization and fault tolerance of highly dynamic systems. First, an incremental control structure is proposed, where a controller gain system is attached to the predesigned controller, and by reconfiguring the controller gain system, the performance can be equivalently optimized as configuring the predesigned one. The incremental attachment of the controller gain system does not modify the existing control system, and it can be easily attached via various communication channels. Second, a structure integrating fault-tolerance strategy and hardware redundancy is proposed. Under this structure, command fusion and fault-tolerance strategies are developed where the control commands from different control units are optimally fused, and each control unit can be reconfigured w.r.t. the performance of the other ones. Furthermore, Q -learning algorithms are developed to realize the proposed structures and strategies in real-time model-freely. As such, varying operational conditions of the highly dynamic system can be tackled. Finally, the proposed structures and algorithms are validated case by case to show their effectiveness.

Driven by the rapid development of modern industrial processes, cyber-physical systems (CPSs), which tightly conjoin computational and physical resources, have become ever-more prevalent during recent years. However, due to the intrinsical vulnerability of the cyber layer, the system performances of CPSs are easily degraded by malicious false data injection (FDI) attacks, which are launched by adversary. In this article, the issue of secure reconstruction is considered for linear CPSs with simultaneous sparse actuator and sensor attacks. First, an adaptive counteraction searching strategy is proposed to identify the potential combinational attack mode. In this way, malicious FDI attacks are excluded. Second, by constructing a descriptor switched sliding mode observer, the sparse FDI attacks and the system state are reconstructed effectively. Meanwhile, sufficient conditions of the error convergence can be derived. Finally, a numerical simulation is utilized to illustrate the applicability of the proposed theoretical derivation.

This article proposes a prescribed adaptive backstepping scheme with new filter-connected switched hysteretic quantizer (FCSHQ) for switched nonlinear systems with nonstrict-feedback structure and time-delay. The system model is subjected to unknown functions, unknown delays, and unknown Bouc-Wen hysteresis nonlinearity. The coexistence of quantized input and actuator hysteresis may deteriorate the shape of hysteresis loop and, consequently, fail to guarantee the stability. To deal with this issue, a new FCSHQ is introduced to smooth the input hysteresis. This adaptive filter also provides us a degree of freedom at choosing the desired communication rate. The repetitive differentiations of virtual control laws and existing a lot of learning parameters in the neural network (NN)-based controller may result in an algebraic loop problem and high computational time, especially in a nonstrict-feedback form. This challenge is eased by the key advantage of NNs' property where the upper bound of the weight vector is employed. Then, by an appropriate Lyapunov-Krasovskii functional, a common Lyapunov function is presented for all subsystems. It is shown that the proposed controller ensures the predefined output tracking accuracies and boundedness of the closed-loop signals under any arbitrary switching. Finally, the proposed control scheme is verified on a practical example where simulation results demonstrate the effectiveness of the proposed scheme.

Threats of cyberattacks have penetrated from disclosing critical user information to destroying/manipulating industrial control systems. Study on data security during network transmission has raised increasing attention in the systems and control community, which is found very necessary and timely in the context of Industry 4.0. In most existing approaches, the protection of the transmitted data from eavesdropping attacks and the detection of malicious integrity attacks are usually carried out separately. In this study, an integrated data-driven framework applicable at the control level is proposed to deal with secure transmission and attack detection simultaneously. In the framework, a secure correlation-based encryption/decryption approach and a trustworthiness judgement approach are proposed. Comprehensive discussions are made regarding the analysis of the sensitivity to attacks, the introduced time delay, and the design degree-of-free. Executable algorithms are presented, corresponding to which hardware is modularized and can work standalone independent from the configuration of the monitoring and control systems or any third-party authentication agencies. Evaluation results on a simulated two-area frequency-load control power grid system are provided to show the effectiveness and performance of the proposed approaches.

The challenging problem on the design of a large-angle attitude tracking controller for rigid satellites without angular velocity measurements is investigated in this article. An efficient and practical angular velocity-free control strategy with a simple, yet efficient structure is proposed. The attitude tracking maneuver is accomplished with the desired attitude pointing accuracy ensured despite disturbances. Compared with the existing observer-based velocity-free schemes, no observer is embedded into the control scheme. The developed approach can be implemented online and in real time. It does not require expensive online computation, enabling its convenient application to practical large-angle attitude tracking maneuvers. The presented control solution is numerically and experimentally validated on a rigid satellite testbed.

This article proposes a fault-tolerant compensation control approach against nonlinearity, simultaneous additive, and multiplicative actuator faults in Markov jump systems. In this article, we first exploit the fuzzy logic system (FLS) to approximate the nonlinear functions, which have no available knowledge. Then, by utilizing the adaptive backstepping technique, a FLS-based adaptive fault-tolerant compensation controller is proposed, which can completely compensate for the adverse effects, arising from the additive actuator faults, the multiplicative actuator faults, and the mismatched nonlinearity simultaneously. The stability of the closed-loop system can be guaranteed by the proposed FLS-based adaptive controller with the adaptation laws. The novelty of this article lies in the fact that the additive and multiplicative actuator faults, and mismatched nonlinearity are considered simultaneously. Besides, the renown sliding mode control approach has limitations to deal with the FTC problem considered in this article because the considered nonlinearity is a mismatched one. The proposed control approach can cope with the challenging case. Finally, a practical wheeled mobile manipulator system is used to demonstrate the effectiveness and validity of the proposed approach.

In the conventional approaches to the design of fault diagnosis systems, little effort is usually paid to the selection of the parity vectors. As a result, the systems' performance can be significantly affected. In this article, novel approaches are proposed to derive the parity vectors that construct optimized residual generators for linear and nonlinear systems. Based on the analysis on the parity space dimension, a novel parameterization of all parity relation-based residual generators is proposed. An iterative procedure that guarantees minimal regression error is then employed in the search for the optimal parameters. Considering that the traditional parity relation-based approaches are only suitable for linear systems, in this work, the proposed approach is also generalized to deal with strong nonlinearities, with the aid of data-driven Hammerstein function estimation. Furthermore, optimized residual generation algorithms are summarized for offline design and online implementation, the performance of which is evaluated thoroughly with a three-tank system, a numerical nonlinear example, as well as a case study on an industrial hot rolling mill process. Results show that residuals generated by the proposed approaches can significantly improve the sensitivity to small faults, and thus, the fault detection rate is improved compared with the traditional nonoptimized approach.

Retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain. While automatic vessel image segmentation is essential, it is also a challenging task because the retinal vessels have complex topological structures, and the retinal vessels vary in size and shape. In recent years, image segmentation based on the deep learning technique has become a mainstream method. Unfortunately, the existing methods cannot make the best use of the global information, and the model complexity is high. In this article, a convolutional neural network integrated with the attention mechanism is proposed. The overall network structure consists of a basic U-Net and an attention module, and the latter is used to capture global information and to enhance features by placing it in the process of feature fusion. Experiment results on five public datasets show that the proposed scheme outperforms other existing mainstream approaches, and most of the performance indicators are in the leading positions. More importantly, the proposed method has a significant reduction in the number of parameters.

Skeletal fluorosis is a form of endemic disease caused by the excessive intake of fluoride. Bone deformation and periosteal calcification are the typical manifestations that can be observed in the images and are usually served as a basis of pathological grading. In the current medical systems, the diagnosis of skeletal fluorosis fully relies on doctors' knowledge and experience, and no research effort has been made in automatic image information diagnostic systems. According to the image information, the shape of the lesion is irregular, the boundary is unclear and the lesion related pixels only occupy a small part of the image. We take the lead in proposing a two-stage scheme that can achieve automated X-ray image diagnosis and severity grading. In the first stage, a Dense U-Net is proposed for reliable lesion determination, and a multitype feature fusion approach passes effective and comprehensive features to the subsequent stage. In the second stage, a novel classifier is designed with the integration of ensemble learning and multiple instance learning, which can ensure classification accuracy in case that the feature for diagnosis only takes up a small proportion of the whole image. Through plenty of experiments on the actual data collected from the hospitals, it is verified that the proposed strategy can achieve satisfactory results on skeletal fluorosis image diagnosis and severity grading.

The fault-tolerant control (FTC) issue is considered in this article for Markovian jump systems (MJSs) in which both nonlinearity and actuator faults exist simultaneously. The existed nonlinearity in the considered MJSs means that there exist limitations to employ the renown sliding mode control (SMC) method directly. In this work, the radial basis function (RBF) neural network (NN) technique is exploited to model the nonlinearity on which no knowledge whatsoever is available. Then, with the help of the adaptive backstepping method, an NN-based FTC approach is proposed to overcome the considered challenging case. The adverse effects, arising from the nonlinearity and the actuator faults can be completely compensated by the proposed adaptive controller. With the proposed controller and the adaptation laws, the bounded stability of the considered closed-loop plant can be guaranteed. Furthermore, only two types of adaptive parameters are adopted in the proposed approach to achieve the purpose of FTC, and this reduces the computational burden and thus extends its applicability. Finally, the effectiveness of the developed approach is demonstrated on a practical system: a wheeled mobile manipulator.

The fourth industrial revolution is elevating the overall flexibility and controllability of the production processes, leading to better consistency in quality and improved feasibility of personalization. However, an evident downside is observed from the boosting system openness because the security and safety protocols are not ready for the potential impacts and threats. In this work, an in-depth analysis about the novel challenges is firstly presented. Then, the mechanism of false data injection attack on the industrial cyber-physical systems is studied. The target is to reveal the condition when the existing fault diagnosis systems fail to detect the false data injection attack on the distributed and networked systems. A theorem is proposed to support the discussions. It is elaborated that establishing interconnections between the subsystems will help prevent undetectable attacks, and thus improving the systems' safety.

Lithium-ion batteries are widely used in many fields of modern life, e.g. wearable devices, electric vehicles and electric grids, etc. The safety and reliability of the lithium-ion battery are critical issues during the battery operation, where the battery management system (BMS) plays a key role. An accurate estimation of the state-of-charge (SOC) of the battery is essential for the BMS. However, due to the intrinsic nonlinearity of the lithium-ion battery, the accurate estimation of the SOC is technically challenging and has drawn lots of attention both from academic and industrial fields. In order to tackle this difficulty, many SOC estimation approaches have been proposed, in which an identification method for the parameters of the battery is normally implemented. However, the additional parameter identification approach greatly reduces the efficiency of SOC estimation and the bias from identification may significantly affect the accuracy of the SOC estimation. This paper proposes a novel data-driven SOC estimation approach based on the adaptive residual generator, which realizes integrating the parameter identification and the SOC estimation into a simultaneous procedure, where the convergences for both the parameter identification and SOC estimation are guaranteed. The proposed adaptive residual generator can estimate the SOC of the battery accurately due to real-time parameter identification that proactively minimizes the modeling error. The effectiveness and the performance of the proposed method are demonstrated through the case studies on a battery simulator. Also, owing to accurately identified parameters, the SOC of the battery is estimated accurately with almost 0% SOC estimation error.

Cup anemometers are widely used instruments for wind turbines to measure wind speed in wind farm. Aimed to reduce the adverse impact on wind energy resource estimation, this paper proposes a data-driven fault diagnosis approach for assessing the anemometer health status. Auto-associative netural network (AANN) is developed to reconstruct the anemometer measurement data after data pre-processing, and residual analysis is performed between the anemometer measurement data and the AANN reconstruction data. In addition, the quantitative indicators that can reflect the health status of the anemometer gained from residuals are obtained through the K-Means clustering algorithm, based on which the faulty anemometers in the wind farm can be identified. The approach can provide guidance for the production and operation of the wind farm.

The safety, stability and reliability of the modern complex processes have always been the focus of the industry. An abnormity can lead to failures in the production and manufacturing processes or even dramatic accidents. The fault diagnosis techniques aim to enhance the aforementioned aspects by detecting the system's deviations from the normal operating conditions and providing early warnings. By mining the hidden system features in the historical data, complex physical modeling procedures and the dependence on large amounts of prior knowledge can be avoided. In many practical scenarios, data-driven fault diagnosis algorithms are more suitable for modern industrial diagnosis. In this paper, a novel approach is proposed which integrates both multivariate statistical analysis and deep neural network to deal with the nonlinearities in the complex systems. Based on the theory of traditional data-driven methods, deep learning methods and the newly proposed method, a MATLAB-based fault diagnosis toolbox is developed and published online. Plentiful function libraries are provided to the researchers to analyze those algorithms and satisfy the need of practical industrial applications. By applying the developed toolbox, the characteristics of those algorithms are also compared, especially on the time-consumption feature and the fault discrimination feature.

MOTIVATION: Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance. RESULTS: We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data. AVAILABILITY AND IMPLEMENTATION: R code available at https://github.com/S-YIN/MIXnorm. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, fault and state estimation issue for wheeled mobile manipulators with time delay, using a novel sliding mode observer (SMO) approach has been discussed. It is well-known that the traditional SMO method, i.e., the equivalent output error injection technique cannot be simply employed to Markov jump systems (MJSs) to estimate actuator faults. In this paper, we propose a novel SMO to reconstruct the actuator and sensor faults for MJSs with time delay. First, we decouple the actuator faults from the first subsystem by utilizing coordinate transformation technique. Then, construct an augmented plant for the new system and propose a novel reduced-order SMO. By the proposed novel SMO, the state and fault can be reconstructed simultaneously. Finally, an illustrative wheeled mobile manipulator example is given to show the effectiveness of the proposed approach.

Due to the recent rapid developments in communication and networking technologies as well as computer science, the complexity of automatic control systems has increased significantly. To ensure the safety and the reliability of such systems under continuous operation, real-time supervision and control systems have now to run in parallel. These developments challenge scientists and engineers to come up with advanced fault diagnosis (FD) and fault-tolerant control (FTC) approaches that can monitor the abnormal changes in automatic control systems promptly. The objective is to maintain safe operating conditions that avoid severe performance degradation. The tasks involved in meeting the objective consist of fault detection, estimation, localization, isolation, feasible control strategies, and maintenance actions.

A data-driven disturbance decoupling FTC for system with deterministic disturbance is proposed in this paper. The algorithm of subspace identification and modified Partial least square is aided to improve the limit of dynamic linearization based predictive control. Compared with the existing dynamic linearization based predictive control, the proposed control strategy enhances its control performance by reducing its sensitivity to noise and decoupling the disturbance. The efficiency of the proposed FTC approach compared to the ynamic linearization based predictive controller is suggested in the simulation of a DC motor.

In the control theory, after the stability of the control system is guaranteed, improving the performance of the system has always been a hot topic. In this paper, based on the Plug-and-Play process monitoring and control architecture (PnP-PMCA), without changing the previously designed controller, new actuators plugged into original system will be presented. In addition, feedforward controller (one of the components of PnP-PMCA) related to tracking performance will also be designed. After the new actuator is inserted into the original system, the feedforward controller is adjusted by iterative method to achieve the desired tracking performance and the original controller is not modified. For the implementation of plug-and-play actuators and the effectiveness of the iterative feedforward controller, the three tank system benchmark experiment will be used to verify the feasibility of the above study.

Process safety, system reliability, and product quality are becoming increasingly essential in the modern industry. As a result, prognosis and fault diagnosis of the complex systems have gained a substantial amount of research attention. In order to evaluate the influence of the detected faults to systems' behavior, there is a pressing need to design prognosis and diagnosis systems oriented to the key-performance-indicators (KPIs). Dedicated to this requirement, we have recently developed a MATLAB toolbox data based key-performance-indicator oriented fault detection toolbox (DB-KIT), which realizes a series of effective algorithms, to provide a systematic and illustrative material to the peer researchers. This paper investigates the recent advances in the multivariate statistical analysis based approaches. Formulations based on the optimization problems are proposed to better clarify the ideas behind different solutions and to study them in a unified data-driven framework. Theoretical fundamentals of some selected algorithms in the DB-KIT are elaborated. Moreover, new evaluation results on dataset defects are presented, which compare the algorithms' robustness and demonstrate the power of DB-KIT. The open-source code and the demonstrative simulations can be regarded as baseline and resources for innovation research, comparative studies, and educational purposes.

Contribution plots and reconstruction-based contribution (RBC) are efficient linear diagnosis tools in multivariate statistical process monitoring. Unfortunately, they cannot be directly applied to nonlinear fault diagnosis with kernel-based methods due to kernel function covers up the information of the original process variables. Although existing kernel gradient-based approaches have solved this problem to a certain extent, they are still far from suitable for practical applications because they require extremely huge amounts of computation. Their calculations cannot be obtained in a tolerable time unless expensive hardware costs are involved. This paper will thoroughly address this issue by revealing a hidden but important equivalent relationship between the variance-covariance matrix of a centralized process variables matrix and the centralized kernel matrix. Based on this relationship, the nonlinear detection index can be transformed into an explicit quadratic form of variables sample, such that contribution plots and RBC can be directly applied to kernel-based fault diagnosis with a very limited amount of computation, just as their usages in the linear cases. Simulation results obtained from two industrial examples demonstrate the effectiveness of the new method.

This paper addresses a long-standing yet well documented open problem on task-space trajectory tracking control of robotic manipulators subject to both uncertain dynamics and uncertain kinematics. The main contribution is to establish a theoretical framework for designing an observer-based controller to achieve exponential tracking control. Two observers are designed for precisely estimating the uncertain kinematics and dynamics. It is theoretically proved that the entire observer-controller system is proved to be globally exponentially stable. Both the estimation errors and the trajectory tracking error can globally exponentially converge to their stable equilibrium points, respectively. To the best knowledge of the author, this works may be the first result for robot exponential tracking control. The tracking performance is, therefore, more robust to system uncertainties. The settling time of the closed-loop tracking error system can be tuned to be small arbitrarily. Experimental tests are also conducted to validate the effectiveness of the designed control framework.

This paper investigates the robust identification of nonlinear systems in state-space setting with output measurements contaminated with outliers and part of output measurements missing at random. The problems of outliers and missing observations are often encountered in practical industrial processes and are taken into consideration comprehensively in this work. The robust Student's t-based observation model is built to model the output measurements with stochastic outliers, and the impact of outliers imposed on nonlinear system identification can be suppressed through inference of the degree of freedom in Student's t-distribution. The proposed robust nonlinear system identification method with an incomplete dataset is derived with the expectation-maximization (EM) algorithm, and the particle filter is employed to numerically approximate the Q-function of the EM algorithm. A numerical example and a chemical process are utilized to demonstrate the superiority of the proposed strategy.

Aiming at establishing reliable and flexible data-driven designs of process monitoring and control systems, this paper presents the latest study on the identification of the data-driven realization of the stable image representation (SIR) in the plug-and-play process monitoring and control architecture (PnP-PMCA). The core of this study is the identification of the multiplication operators from the reference signal to the control input and the system output measurement. This work is essential to the future research on the data-driven PnP process monitoring and control system designs. The correctness and the effectiveness of the proposed identification approach have been verified and demonstrated through randomly generated system and designed closed-loop.

This paper presents the study on the data-driven process monitoring system design for the dynamic processes with periodic rectangular wave disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into the row subspace of the periodic rectangular wave disturbance. With the help of the projection, the kernel subspace of the system can be further determined. Based on the identified data-driven SKR, fault detection are developed. The performance and effectiveness of the proposed scheme is verified and demonstrated through the numerical study on randomly generated systems.

This paper focuses on the design of diagnostic observer based residual generator (DORG) for fault detection purposes. The property of the existing Multiple- Input-Single-Output (MISO) DORG is firstly discussed, followed by a parity vector based solution. Then, a novel Multiple- Input- Multiple-Output (MIMO) DORG is proposed through rigorous mathematical derivations. Compared with existing approaches, the proposed approach and algorithms retain the correlation information in the output variables, and reduce the offline design complexity and the online implementation efforts. Simulation studies on a numerical example show that the proposed approach has better fault detection performance than the MISO DORG based approach.

Industrial systems are always subjected to the deterministic disturbance due to some inherited factors, which is likely to degrade the control and monitoring performance to some extent. This paper presents an approach to the closed-loop subspace identification of the data-driven stable kernel representation (SKR) with the deterministic disturbance. The essence is that we extend the CSIMPCA algorithm by introducing the deterministic disturbance and subsequently separate the part corresponding to the SKR of the system from the obtained parity space. The inspiration for the idea mainly stems from the necessity for the identification and process monitoring of practical closed-loop systems. The effectiveness of the proposed method is demonstrated and illustrated through randomly generated 4-order MIMO discrete-time LTI systems. Furthermore, the identified SKR is finally applied to the fault detection and related experimental results show a decent detection performance.

This paper proposes a new method to estimate the stability margin of a system by factorizing it into system's data-driven stable kernel representation (SKR) and controller's stable image representation (SIR). To this end, a coprime factorization technology is applied to the closed-loop system firstly. By analyzing the relations between the reference signal and internal signal, a new approach is adopted to calculate SKR of the system by the least square (LS) method. Furthermore, the data-driven realization of stability margin is calculated through system's SKR and controller's SIR. An example is given in the last part to testify the correctness of methodologies proposed in this paper.

This work focuses on the fault estimation (FE) problem for Markov Jump Systems (MJS) with sensor and actuator faults, and a novel reduced-order observer-based FE method is proposed. Firstly, an augmented plant in standard form is considered and a new theorem is derived to decouple the augmented fault vectors $d(t)$ from $\bar{x}_{1^{(1)(t)}}$ which is the state vector after the first time coordinate transformation. Then, the novel reduced-order observer is investigated and the Theorem 2 is derived to ensure the asymptotically reconstruction of $x(t)$. Compared with other existing observer-based method for MJS with faults, the advantage is that the FE and state estimation can be obtained directly without any supplementary design. Finally, simulations are provided to demonstrate the effectiveness of the proposed observer approach.

This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.

Heart-disease diagnosis is widely studied by researchers all over the world, since it is the primary cause of deaths. There exist many challenges in heart-disease diagnosis, such as huge amount of data, high data dimension, large noise interference, etc, which point to the suitability of using data-driven approaches. This paper presents two dimension-reduction methodologies based on support vector machine (SVM), to diagnose heart disease. The most relevant features for diagnosis are achieved by support vector machine-recursive feature elimination (SVM-RFE) method. Then principal component analysis-support machine (PCA-SVM) is also used for heart-disease diagnosis. The best classification accuracy 88.24% is obtained by PCA-SVM via Radial Basis Function (RBF) kernel using only 6 principal components.

This paper investigates a difficult problem of reconstructing actuator faults for robotic manipulators. An intelligent approach with fast reconstruction property is developed. This is achieved by using observer technique. This scheme is capable of precisely reconstructing the actual actuator fault. It is shown by Lyapunov stability analysis that the reconstruction error can converge to zero after finite time. A perfect reconstruction performance including precise and fast properties can be provided for actuator fault. The most important feature of the scheme is that, it does not depend on control law, dynamic model of actuator, faults' type, and also their time-profile. This super reconstruction performance and capability of the proposed approach are further validated by simulation and experimental results.

The cyber-physical systems (CPSs) are the central research topic in the era of Industrial 4.0. Such systems interact intensively between physical entities and abstract information, and commonly exist in the industrial processes and people's daily lives. This paper investigates the practical difficulties of the vehicular CPSs online implementation, and based on that proposes a fault diagnosis and control architecture with modular units and reserved extendibility. It is elaborated that the systems' adaptability could be enhanced by either the online tracking techniques or the ensemble learning schemes. For the onboard deployment of automobile CPSs, the requirement of real-time capacity is in focus. A new recursive total principle component regression based design and implementation approach is proposed for efficient data-driven fault detection. Simulation tests were carried out on the Carsim to compare the proposed approach with multiple existing methods.

Stimulated by the recent development of fog computing technology, in this paper, a fog-computing-aided process monitoring and control architecture is proposed for large-scale industrial processes, which enables reliable and efficient online performance optimization in each fog computing node without modifying predesigned control subsystems. Moreover, a closed-loop data-driven method is developed for the process monitoring system design and an adaptive configuration approach is proposed to deal with the problems caused by the changes of process parameters and operating points. The feasibility and effectiveness of the proposed design approaches are verified and demonstrated through the case study on the Tennessee Eastman benchmark system.

This paper focuses on the fault-tolerant control problem of Markov jump systems (MJS) with Itô stochastic process and output disturbances. Such a problem widely exists in practical systems such as mobile manipulator systems. Since MJS can suitably describe mobile manipulator systems, in this paper, a new approach based on the MJS model is proposed. First, a proportional-derivative sliding mode observer (SMO) and an observer-based controller are designed and synthesized. Two new theorems are derived to ensure the close-loop stochastic stability and the reachability of the sliding mode surface. Compared with the existing works, the system model is more general, which could describe a larger variety of plants or processes. The controller design procedure is simplified by solving the sliding mode parameters and the controller gain simultaneously with only one linear matrix inequality problem. In addition, the augmented fault vector can be reconstructed by employing a descriptor SMO. Simulations are provided to demonstrate the validity of the derived theorems and the effectiveness of the proposed algorithm.

An intelligent data-driven predictive control strategy is proposed in this paper. The predictive controller is designed by combining predictive control and local weighted projection regression. The presented control strategy needs less prior knowledge and has fewer parameters that are hard to determine compared to other data-driven predictive controller, e.g., the one in dynamic partial least square (PLS) framework. Furthermore, the proposed predictive controller performs better in the control of nonlinear processes and is able to update its parameters based on the online data. The predictive model validity and intelligence of the control strategy are guaranteed by the online updating strategy to a certain degree. The control performance of the proposed predictive controller against the model predictive control (MPC) in dynamic PLS framework is illustrated through the simulation of a typical numerical example and the benchmark of a continuous stirred tank heater system. It can be observed from the simulation that the proposed MPC strategy has higher prediction precision and stronger ability in coping with nonlinear dynamic processes which are quite common in practical applications, for instance, the industrial process.

A data-driven model predictive control (MPC) that combines modified partial least squares (PLSs) and MPC is proposed in this paper. A theoretical comparison among traditional MPC, MPC in PLS framework and in modified PLS framework is presented, which demonstrates that the proposed MPC approach has high prediction precision and the ability in coping with dynamics in the process compared to MPC in traditional PLS framework. Furthermore, the proposed MPC requires no prior knowledge, and the simplicity in computation makes it possible to update the prediction model online. The model validity and intelligence of the control strategy are guaranteed by the model updating strategy to a certain degree. Steady-state performance and dynamic response of the proposed MPC is testified through a tracking control simulation of the benchmark of a continuous stirred tank heater system, which illustrates that the advantages of the proposed MPC.

The robust parameter and output estimation for linear parameter varying (LPV) time-delay system with output data contaminated with outliers and subjected to randomly missing measurements are considered in this paper. The outliers, missing data, and the time-delay are widely existed in practical industry and have imposed extra difficulties on complex process modeling. The robust probability model to describe the LPV time-delay system is constructed with the student's t -distribution and the estimation problems are formulated in the framework of generalized expectation-maximization algorithm. The time-delay and parameter varying process properties, the outliers, and randomly missing measurements are taken into consideration comprehensively in the derivations of proposed algorithm and the unknown model parameters, scale parameter, degree of freedom parameter, the time-delay, and the noise-free output data are estimated simultaneously. The numerical example and a practical chemical process are used to present the efficacy of proposed algorithm.

In this paper, after a brief introduction on the proposed plug-and-play (PnP) process monitoring and control system, two online configuration approaches are proposed for the observer-based residual generator. Firstly, an adaptive residual generator is developed based on the adaptive observer scheme. Aiming at higher feasibility and reliability of industrial application, an iterative learning approach is later developed. The well established three-tank benchmark system is utilized for the demonstration of the effectiveness of the proposed approaches.

A new MATLAB toolbox DB-KIT was recently developed for the design and implementation of fault diagnosis systems. For the purpose of key performance indicator (KPI) oriented fault detection, over the past few years, a series of test statistics and the corresponding thresholds were derived based on the modified data structures originating from the existing multivariate statistical analysis tools. These data-driven approaches are numerically reliable, efficient and of high fault detection performance. Especially, the false alarm rates (FARs) under KPI-unrelated fault scenarios are suppressed with great efforts, which is the central task of the KPI-oriented fault detection problem. DB-KIT was firstly introduced at the 2016 IEEE Industrial Electronics Conference, and the initial results on algorithm efficiency and fault detection performance were reported in Comparison of KPI related fault detection algorithms using a newly developed MATLAB toolbox: DB-KIT with simulation tests on the Tennessee Eastman Process benchmark. This paper reports more recent results on a widely used numerical test and on a close-loop configured three-stage hot rolling mill process to reveal the performance of the algorithms at the extreme faulty conditions, and demonstrates the performance under the plant-wide performance supervised framework.

In this paper, a data driven sensor fault tolerant strategy is proposed for nonlinear systems. The core of the proposed strategy is just-in-time learning based soft sensor. When sensor fault occurs, the value of soft sensor is adopted as the redundancy instead of the real faulty sensor value. Meanwhile, the influence of the sensor fault can be tolerated. Due to the complexity of mechanism model for nonlinear system, a kind of just-in-time learning method is employed for soft sensor. The indexes will be predicted by just-in-time learning method online. And only historical sample data will be used for prediction. Instead of considering system global model as normal soft-sensor approaches, just-in-time learning methods only consider the approximate system at current time. Thus JITL owns strong capacity of on-line implementation. Two nonlinear systems, a typical numerical one and a benchmark of wastewater treatment system, are employed for experiments. The experiment results verify the accuracy and implementability of the proposed scheme.

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

In this paper, the fuzzy adaptive control problem for a class of switched stochastic nonlinear systems in pure feedback form with output constraint is addressed. By proposing a nonlinear mapping, the constrained system is transformed into an unconstrained one, with equivalent control objective. All signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded. Meanwhile, the output constraint is satisfied and the output tracking error converges to an arbitrarily small neighborhood of zero. Finally, the applicability of the proposed controller is verified by a simulation example.

This article considers the issue of quality-related process monitoring. A novel kernel direct decomposition (KDD) algorithm is proposed and a KDD-based nonlinear quality-related fault detection approach is designed. The proposed KDD algorithm first maps original process variables into feature space to deal with the nonlinearities among these variables. Feature matrix is then directly decomposed into two orthogonal parts according to its full correlation with output matrix without building any regression model. Compared with conventional nonlinear methods, the KDD-based approach has the following advantages: 1) it is simpler in design as it omits the steps of constructing a regression model like kernel partial least squares (KPLS); 2) its performance is more stable because it extracts the full correlation information of feature matrix unlike KPLS-based methods which only use the partial correlation information of several selected latent variables; and 3) it has a simpler diagnosis logic since it only uses two statistics to determine the type of fault while most existing methods need four. Simulations on a literature example and a simulated industrial process are used to demonstrate the advantages of the new method.

This paper addresses a difficult problem of high-accuracy control for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbance force and unknown disturbance torque. An observer-based full control scheme is presented. In the strategy, two observer-based estimators are first designed to estimate external disturbance force and torque, respectively. With the application of the precise estimation value, a nonlinear tracking controller is then proposed with compensated disturbance. It is shown by the Lyapunov stability analysis that the entire controller-observer system is asymptotically stable. The key feature of the scheme is that it not only has the superior capability to attenuate unknown external disturbance torque and external force generated by the wind, but also it is able to achieve full control (i.e., six degrees-of-freedom) of the quadrotor UAVs with position and attitude successfully controlled. The effectiveness of the approach is verified on a quadrotor UAV example.

This paper addresses the problems of robust global identification and fast-rate output estimation for linear parameter varying (LPV) dual-rate systems with output measurements subjected to random time-delays and outliers in statistical framework. In practical industry, the process data are often dual-rate sampled, and the output data are usually contaminated with outliers and may be subjected to uncertain time-delays due to lab analysis, long-distance or network transmission, etc. The LPV dual-rate model is given and the robust global identification and output estimation problems are formulated in statistical scheme with the Laplace distribution. The robust identification algorithm to estimate all the unknown parameters and output data are derived in the generalized expectation-maximization algorithm framework and the random time-delays and outliers in output data are handled adaptively in identification process. The proposed algorithm is presented and verified through numerical simulation and a practical chemical process.

This paper presents an adaptive neural network (NN)-based fault-tolerant control approach for the compensation of actuator failures in nonlinear systems with time-varying delay. The novelty of this paper lies in the fact that both the lock in place and loss of effectiveness faults, unmodeled dynamics, and dynamic disturbances are catered for simultaneously. Furthermore, this is achieved by the adaptation of only one parameter, which simplifies the computation of the control effort, and therefore extends its applicability. In the approach, the Razumikhin lemma and a dynamic signal are employed. It is shown that the output of the system converges to a neighborhood of the reference signal and the semiglobal boundedness of all signals is guaranteed. A simulation example is used to illustrate the validity and efficacy of the approach.

The global asymptotic stabilization problem is investigated for a class of stochastic nonlinear time-varying delay systems under the weaker condition on nonlinear functions. The new parameter-dependent state and output feedback controllers are, respectively, proposed. Based on the stochastic time-delay system stability criterion, by tactfully introducing a suitable Lyapunov-Krasovskii functional, the globally asymptotically stable in probability of the closed-loop system is guaranteed by rigorous proof. As a practical application, the stochastic model of a two-stage chemical reactor system is established by reasonably introducing the Gaussian white noise. The developed approach is applied to the control design for this practical system. The simulation results demonstrate the efficiency of the proposed design approach.

This paper focuses mainly on the approximation-based fuzzy adaptive fault-tolerant control problem for nonlinear systems with unmodeled dynamics and unknown control directions. With the Nussbaum gain technique and a dynamic signal introduced, the difficulties from the unknown control directions and unmodeled dynamics are successfully overcome. Then, by taking advantage of the adaptive fuzzy control method and backstepping technology, we develop a fuzzy adaptive failure compensation control strategy and guarantee the semi-global boundedness for all signals. A simulation example is carried out to demonstrate the validity of the theoretical findings.

Data driven fault diagnosis has been of real interest for the industry in recent years. Optimization of the existing fault detection algorithms with respect to significant monitoring indices are practically and economically preferred. In this paper, a recently developed Data Based Key-performance-Indicator-related fault detection Toolbox (DB-KIT) is described. The aim of DB-KIT is to provide a general platform for key performance indicator (KPI) related fault detection algorithms validation and comparison, and for further studies as well as educational purposes. Considering that MATLAB is probably one of the most popular programming environments in the fault diagnosis community, the related functions and a user-friendly graphical interface are programmed in MATLAB. Moreover, a demonstration of the 14 integrated linear and nonlinear algorithms in the toolbox is provided. The simulation is run with the Tennessee Eastman process dataset, which is a widely accepted benchmark challenge in the fault diagnosis field. Finally, the evaluation indices, including the false alarm rates, fault detection rates and algorithms efficiency, are further compared and discussed.

In this paper, a novel monitoring approach is developed for nonlinear processes based on available measurements. To cope with complicated process nonlinearity, we implement total projection to latent structures (T-PLS) in each local model created by the locally weighted projection regression (LWPR) algorithm. Under the framework of locally weighted learning, four improved test statistics are established to detect potential process faults. The test statistics are not only capable to monitor the abnormal changes in the relevant subspaces, but also with thresholds suitable for non-Gaussian measurements. The effectiveness of our proposed approach is further demonstrated by a numerical nonlinear case.

This work aims to study the fault classification problem in complicated industrial processes. Two modified multi-classification methods of Support Vector Machine (SVM), i.e., Principal Component Analysis based Support Vector Machine (PCA-SVM) as well as Kernel Principal Component Analysis based Support Vector Machine (KPCA-SVM), are respectively proposed to classify multi-fault for the underlying process. The continuous stirred tank heater (CSTH) benchmark is adopted in simulation to validate the effectiveness of the proposed approaches. Simulation results indicate that compared with the original PCA-SVM, KPCA-SVM generates a higher classification rate for the underlying process at the cost of larger computation loads.

Fault detection technique plays an important role in industrial systems. However, conventional model-based methods have unavoidable troubles in obtaining physical models of complex industrial processes. Data-driven fault detection approaches provide effective tools to solve them. Nevertheless, most of data-based approaches are focused on linear systems. It is still a challenging research direction for nonlinear processes. In this paper, a data-based fault detection scheme for non-linear systems is proposed. It provides a potential tool to fault detection of complex industrial systems in data-based manner. The presented algorithm uses the idea of model-based approaches for reference, employs the just-in-time learning (JITL) scheme to estimate the system output, and receives final fault detection results according to the decision rule of residual analysis. The algorithm owns inherent online adaptation and is easy to implementation. Three experiment examples, a numerical nonlinear system, a wastewater treatment system benchmark and a DTS200 three-tank system, are employed to prove the high accuracy, strong applicability and practical significance of the proposed method.

The target of the article is to solve the problem of fault detection for a multirate sampled-data system with frequency specifications. And the multirate condition considered in this article is that different channels of both the inputs and the outputs have different sampling rates. This paper aims to solve this fault detection problem with using a fault detection filter to make the residual system satisfy a given disturbance attenuation level over a restricted frequency range. The problem is solved via reformulating the multirate sampled-data system with extended inputs and outputs and utilizing the generalized Kalman-Yakubovič-Popov lemma to deal with the restricted frequency specifications. Finally, we use an example to illustrate the design procedure and show the advantages of the proposed method.

The articles in this special issue present the latest developments and achievements in industrial cyber-physical systems (ICPSs). The papers in this issue cover key areas on architecture, design,enabling technologies, and applications of ICPSs. Additionally, they present emerging trends and visions of ICPSs for future investigations.

In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.

In this paper, an approximated-based adaptive fuzzy control approach with only one adaptive parameter is presented for a class of single input single output strict-feedback nonlinear systems in order to deal with phenomena like nonlinear uncertainties, unmodeled dynamics, dynamic disturbances, and unknown time delays. Lyapunov-Krasovskii function approach is employed to compensate the unknown time delays in the design procedure. By combining the advances of the hyperbolic tangent function with adaptive fuzzy backstepping technique, the proposed controller guarantees the semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system from the mean square point of view. Two simulation examples are finally provided to show the superior effectiveness of the proposed scheme.

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

The problem of fuzzy observer-based controller design is investigated for nonlinear networked control systems subject to imperfect communication links and parameter uncertainties. The nonlinear networked control systems with parameter uncertainties are modeled through an interval type-2 (IT2) Takagi-Sugeno (T-S) model, in which the uncertainties are handled via lower and upper membership functions. The measurement loss occurs randomly, both in the sensor-to-observer and the controller-to-actuator communication links. Specially, a novel data compensation strategy is adopted in the controller-to-actuator channel. The observer is designed under the unmeasurable premise variables case, and then, the controller is designed with the estimated states. Moreover, the conditions for the existence of the controller can ensure that the resulting closed-loop system is stochastically stable with the predefined disturbance attenuation performance. Two examples are provided to illustrate the effectiveness of the proposed method.

This paper investigates the problem of output feedback robust ℋ∞control for a class of nonlinear spatially distributed systems described by first-order hyperbolic partial differential equations (PDEs) with Markovian jumping actuator faults. The nonlinear hyperbolic PDE systems are first expressed by Takagi-Sugeno fuzzy models with parameter uncertainties, and then, the objective is to design a reliable distributed fuzzy static output feedback controller guaranteeing the stochastic exponential stability of the resulting closed-loop system with certain ℋ∞disturbance attenuation performance. Based on a Markovian Lyapunov functional combined with some matrix inequality convexification techniques, two approaches are developed for reliable fuzzy static output feedback controller design of the underlying fuzzy PDE systems. It is shown that the controller gains can be obtained by solving a set of finite linear matrix inequalities based on the finite-difference method in space. Finally, two examples are presented to demonstrate the effectiveness of the proposed methods.

Kernel method has been applied to many multivariate statistical analysis techniques. In this paper, we investigated the regression properties of Kernel Partial Least Squares (KPLS) and compared it to the standard technique. Basic mathematical algorithms and application of KPLS were shown. We further established regression model based on KPLS and demonstrated the model by a numerical case.

The general particle filter (GPF) provides an effective tool of fault detection for non-linear systems with non-Gaussian cases. However, due to the particle impoverishment problem of the GPF, the estimation of hidden state in process may get misleading results and thus lead to undesired conclusion for fault detection purpose. To solve this problem, a fault detection strategy, which employs a novel kind of particle filter, i.e. intelligent particle filter (IPF), is proposed in this paper. The proposed IPF strategy could improve the impoverishment problem of GPF and increase the accuracy of hidden state estimation, thus offers desired results for fault detection. Two numerical examples show superior performance of the proposed IPF for fault detection of nonlinear system with high non-Gaussian noises compared with the GPF-based strategy.

With the rapid development in science and technology, data acquisition, storage and mining technology are widely applied to various fields. All aspects of people's lives are recorded as data. Through the analyzing and arranging of data, people can get a lot of valuable information. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM) and partial least squares (PLS) are respectively used in the field of economic research. Real-time monitoring and forecasting for stock index is vital to the market. The changing trend and index of stocks are predicted according to the analysis to the history data of the stock. By combining particle swarm algorithm (PSO) algorithm and LSSVM algorithm, the parameters in the LSSVM model can be optimized. These algorithms are compared on the basis of their forecasting results.

We are living in an era of data deluge and as a result, the term ``big data'' is appearing in many contexts, from meteorology, genomics, complex physics simulations, biological and environmental research, finance and business to healthcare. As the name implies, big data literally means large collections of data sets containing abundant information. However, it has some special characteristics that distinguish it from “very large data”or “massive data”that are simply enormous collections of simple-format records, typically equivalent to enormous spreadsheets. Big data, being generally unstructured and heterogeneous, is extremely complex to deal with via traditional approaches, and requires real-time or almost real-time analysis. A short definition can therefore be that “big data” refers to data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

Two major challenges in networked control systems are the time-varying networked-induced delays and the packet losses. To alleviate these problems, this study presents a novel fuzzy sliding mode controller, where a fuzzy system is used to estimate the nonlinear dynamical system online, and the networked-induced delay is handled by Pade approximation. The problem of packet losses is handled by viewing them as large time-varying delays in the system. The sliding mode-based design procedure used ensures the stability and the robustness of the proposed controller in the presence of disturbances and time-varying networked-induced time delays. Using an appropriate Lyapunov function, it is proved that the tracking error converges to the neighborhood of zero asymptotically. Furthermore, since the adaptation laws of the parameters are derived by using of the Lyapunov function, these laws are also found to be stable. Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.

Partial least squares (PLS) is an efficient tool widely used in multivariate statistical process monitoring. Since standard PLS performs oblique projection to input space X, it has limitations in distinguishing quality-related and quality-unrelated faults. Several postprocessing modifications of PLS, such as total projection to latent structures (T-PLS), have been proposed to solve this issue. Further studies have found that these modifications fail to reduce false alarm rates (FARs) of quality-unrelated faults when fault amplitude increases. To cope with this problem, this paper proposes an enhanced quality-related fault detection approach based on orthogonal signal correction (OSC) and modified-PLS (M-PLS). The proposed approach removes variation orthogonal to output space Y from input space X before PLS modeling, and further decomposes X into two orthogonal subspaces in which quality-related and quality-unrelated statistical indicators are designed separately. Compared with T-PLS, the proposed approach has a more robust performance and a lower computational load. Two case studies, including a numerical example and the Tennessee Eastman (TE) process, show the effeteness of the proposed approach.

This paper focuses on the performance tracking issue of complex industrial processes in double layer architecture. First, the nonlinear plants in the device layer are modeled by using Takagi-Sugeno (T-S) fuzzy technique, and are controlled by local proportional integral (PI) controller with the H∞performance guaranteed. Then, the outputs and inputs of local plants are sampled and transited to the operation layer to form the economic performance index (EPI), which is used to represent the performance of the tracking of economic objective. Furthermore, the setpoints, which are dynamically changing, are calculated via a compensator based on the error between the objective and the EPI at each step of the operation layer. Finally, the effectiveness of the proposed method is demonstrated by a nonlinear continuous stirred tank reactor (CSTR) model.

This paper presents a possible solution to the problem stated in a recently published paper. The main objective is to enlarge the torque capacity across a prescribed stiffness range so that the performance-stiffness sensitivity can be decreased. A generalized calculation framework for both circular and variable radius pulleys in pneumatic manipulators is proposed, which describes the relationship between the profile function and the dynamic characteristics. Then five practical constraints are introduced and three optimization objectives are suggested. In view of the fact that it is easy for the traditional optimization methods to be trapped into local optima, an optimization approach based on genetic algorithms is used in the paper, in the design of which the execution time of the algorithm is also taken into consideration. The results presented illustrate the effectiveness of the proposed approach.

This paper investigates the problem of robust model predictive control (RMPC) with saturations and packet dropouts. In this model, polytopic uncertainties are adopted to describe the inconsistency arising from the discretization process of sampling, while the occurrence probabilities of packet dropouts are time-varying and saturations are taken into account to describe input and output signals. The problem of exponential RMPC with saturations and packet dropouts is solved and characterized by a convex optimization problem. The developed results of RMPC are then applied to networked flotation processes, which are made up of three layers: direct control layer, set-point control layer, and optimization layer. The RMPC is used for compensating the output information from the optimization layer to the direct control layer such that the desired economic objective can be achieved. Simulations are presented to show the effectiveness of the proposed method.

This paper presents a data-driven method for the task of fault detection in nonlinear systems. In the proposed approach, locally weighted projection regression (LWPR) is employed to serve as a powerful tool for modeling the nonlinear process with locally linear models. In each local model, partial least squares (PLS) regression is performed and PLS-based fault detection scheme is applied to monitor the regional model. The diagnosis for the global process is based on the normalized weighted mean of all the local models. Both conventional and quality-related statistical indicators are designed to compute the test statistics. Two nonlinear systems, a numerical one and a benchmark, are used to demonstrate the effectiveness of the proposed method.

This paper addresses the fault detection filtering issue for a class of discrete system with network-induced nonlinear characteristics. The communication limitations, including measurements quantization, signal transmission delay, and data packet dropout, frequent in many practical networked control systems (NCSs) are considered. By transforming the residual system into an input-output form consisting two interconnected subsystems via a two-term approximation to the state delay variables, sufficient conditions are established. Under these conditions, the residual system is stochastically stable with a prescribed $\mathcal{H}_{\infty}$ level with the help of the scaled small gain (SSG) theorem developed for stochastic systems. Furthermore, the fault detection filter design approach is presented. A numerical example is also provided to demonstrate the effectiveness of the proposed method.

This paper introduces a robust data-driven fault detection method and its application on a wind turbine benchmark. The benchmark is provided by a Simulink Model, which contains nonlinear wind turbine model and complex wind disturbances. The model-based fault detection technique is hardly to be applied to solve this problem because modeling this wind turbine is quite difficult. Besides, the unknown wind disturbances and the large measurement noises are two enormous challenges for most of the fault detection techniques. To overcome these difficulties, this paper applies a robust data-driven fault detection scheme, which is based on a standard residual generation and decision logic structure. In the residual generation step, a robust residual generator with an optimal parity vector is constructed directly from the measurement data. Moreover, a filter algorithm is used in the residual evaluation step to reduce false alarms rate. Simulation results show that the performance and effectiveness of the proposed scheme are satisfied.

In this paper, a design framework of fault-tolerant wireless networked control systems (NCSs) is developed for industrial automation applications. The main objective is to achieve an integrated parameterization and design of the communication protocols, the control and fault diagnosis algorithms aiming at meeting high real-time requirements in industrial applications. To illustrate the design framework, a laboratory wireless fault-tolerant NCS platform is presented.

In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.

The quality or key performance indicator related prediction and diagnosis cover a wide range of practical requirements from industrial applications. Although much effort has been devoted to establishing an analytical model between operating conditions and quality variables based on the first principals, it is still a challenge in practice due to the complexity of large-scale industrial process. To solve this problem, the data-driven quality related prediction and monitoring schemes are proposed in this paper. In order to overcome the drawbacks of standard approach, our focus is firstly concentrated on the modifications of standard partial least squares. Moreover, under industrial operating conditions, a subspace aided data-driven approach is further utilized to construct a soft sensor in the framework of diagnostic observer based residual generator. The proposed approaches are finally applied to quality based prediction and diagnosis on an industrial hot strip mill process. Application results indicate the effectiveness of the proposed methods and demonstrate improvement in performance compared to the standard technique.