Deep learning methods have made significant progress in medical image analysis, particularly in the segmentation of knee cartilage MRI images. However, the cartilage regions are of relatively small size and they are susceptible to background interference. As a result, traditional segmentation methods are prone to practical limitations such as class imbalance and background noise. The boundaries of knee cartilages are often unclear, especially in pathological conditions where the morphology of the cartilage may change, making it difficult to segment the boundaries of the cartilages accurately. To address these issues, this paper proposes an innovative two-stage approach that leverages the functions of object detection and segmentation (D2MSeg). At the first stage, an object detection module is used to precisely localize the knee cartilage region, thereby reducing background interference and the influence of non-target areas, effectively tackling the class imbalance problem. At the second stage, a memory-augmented module is proposed to help the model learn the fine details of the cartilage boundary by guiding the network to focus on fine-grained information. Experimental results on our dataset show that D2MSeg achieves a Dice score of 83.34%, outperforming most existing methods, including U-Net and TransUNet under comparable settings. These results highlight the effectiveness of our approach in capturing fine cartilage structures and its strong potential for clinical application.

This paper presents an adaptive residual generator-based co-estimation algorithm for multi-sinusoidal signals with unknown components. By modeling the multi-sinusoidal signal, a residual generator algorithm integrated with the adaptive estimation mechanism is constructed. The proposed algorithm can simultaneously estimate the amplitude, frequency, and phase of every sinusoidal component online with stability and convergence guarantees. The performance of the proposed co-estimation approach is evaluated with a simulation experiment.

This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model’s topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability.

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.

This article proposes a game-based distributed decision optimization method for heterogeneous multiagent systems with unknown nonlinear dynamics. Due to the information exchange between agents in the network, the unknown nonlinear dynamics lead to the degradation of local and all-agent control performance, which causes the strategies of all agents to deviate from the Nash equilibrium under a given goal. To address this problem, an adaptive distributed algorithm is designed to seek Nash equilibrium by combining two optimization levels. Specifically, the decision layer uses a distributed consensus algorithm to achieve benefit evaluation and a gradient algorithm to generate reference signals. Then, the control layer uses the virtual reference signal from the decision layer and the neural network estimation information to design an adaptive control algorithm. The proposed method performs real-time adaptive optimization of the strategies and control performance of the decision and control layers, ensuring the successful implementation of the distributed Nash equilibrium search. The convergence of the proposed algorithm is proved in the Lyapunov sense. Finally, simulation examples demonstrate the performance and effectiveness of the proposed method.

Equipment operating in intermittent industrial processes generates complex system coupling and unequal-length sensor data, which are considered significant challenges for fault diagnosis in industrial production. To address these challenges, a generic fault diagnosis approach, the dynamic time warping Gaussian spatiotemporal self-attention network (DGSSANet), is proposed. dynamic time warping combined with Gaussian blur is employed to handle sensor signal complexity, transforming unequal-length data into warping matrices of equal dimensions. The inclusion of spatiotemporal self-attention, with the multiscale temporal attention module and multielement spatial self-attention module, enhances the ability of DGSSANet to capture spatial and temporal relationships. DGSSANet is validated through benchmark experiments on bearing datasets and applied to excavator cases, with its interpretability analyzed via visualization. The results demonstrate that DGSSANet addresses the challenges of fault diagnosis in intermittent production processes effectively.

This article proposes a subspace-aided distributed monitoring and control performance optimization integrated framework and the corresponding distributed monitoring and optimization approaches equivalent to centralized designs. It effectively realizes the online global control performance optimization and solves the predesigned controller parameter adjustment limitation. The main contributions of this article are as follows. First, the proposed distributed monitoring and optimization modules can cooperate to establish a subspace-aided distributed integrated framework. The framework effectively addresses the issue of separate design in monitoring and optimization, achieving modularization that facilitates the expansion and maintenance of interconnected systems. Second, the proposed subspace-aided control performance optimization approach breaks the limitations of existing methods that require predesigned controller parameter adjustments, which can achieve distributed control performance optimization while ensuring closed-loop stability of interconnected systems. Third, the proposed optimization approach can automatically adjust the iterative step size, avoiding the disadvantage of manually setting the step size in the traditional optimization algorithm. It shortens the optimization time and reduces the design difficulty. The new methodologies have been evaluated against the current techniques and validated using an interconnected dc motor system, which holds significant engineering importance.

A novel sensor fault detection framework based on zonotopic set-membership estimation is proposed for autonomous underwater vehicles in the context of unknown but bounded perturbations. First, the zonotopic propagation and intersection properties are utilized to derive the prediction state set and the measurement state set. Then, two methods, namely, projection and polytopic conversion, are provided to examine whether there is an intersection between these two sets. The intersection checking could be used to ascertain the occurrence of sensor faults. To analyze the detection performance of the proposed methods, a minimum detectable fault set is introduced. Finally, pool experiments are conducted to validate the effectiveness of the proposed methods.

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.

In the current time series classification (TSC) field, shallow concatenation, deep fusion, and hybrid ensemble multichannel frameworks (MCF) represented by convolution-based, deep learning, and hybrid methods have achieved competitive TSC performance. However, the massive kernels, deep fusion, and heterogeneous ensemble mechanisms, which are the core of the three frameworks, respectively, lead to overfitting risks. Therefore, in this article, a novel convolution-based TSC algorithm multichannel-based multiview shallow fusion (MC-MSF) within a new shallow fusion ensemble-based MCF is proposed. MC-MSF enhances feature diversity, quality, and classifier diversity while suppressing the overfitting risks via three shallow components. For feature diversity, the original series is mapped to the connected multichannel series spaces, and then diverse pooling features are extracted via a single-layer convolution with fewer kernels. For feature quality, the power of proportion of positive values (PPPV) features with adaptive powers are extracted based on alternating gradient descent, and the multiview shallow feature fusion is implemented to generate fused features. For classifier diversity, diverse linear classifiers are trained on the combined multiview feature vectors to ensemble homogeneously. The state-of-the-art TSC accuracy is achieved by MC-MSF via the sequential operation of three effective shallow components, as verified by comparative experiments on the public UCR and real excavator fault diagnosis application datasets.

Currently, deep learning methods in the analysis and processing of Magnetic Resonance Imaging (MRI) images have found widespread use in medical research. Due to the highly uniform features of human organs or tissues in medical images, pathological conditions are often distinguished only by subtle differences. The manifestations of knee-joint edema are complex and varied, possibly caused by various factors including injury, inflammation, and degenerative diseases, among others. The diversity in edema characteristics in images increases the difficulty of classification. Therefore, the task of pathological identification of knee joint edema poses significant challenges. This paper constructs a neural network model for extracting features of the knee joint and establishes a corresponding classification model. We propose a novel Multi-dimensional knee-joint edema classification network based on attention mechanism (MKEA), which includes a one-dimensional feature extraction network, a two-dimensional feature extraction network, and a feature fusion classification network. In the 2D feature extraction network, a channel attention mechanism module is embedded. we integrate these components to develop a network model for classifying knee edema images from the NYU fastMRI Initiative database. By using well-designed attention models, the network can selectively focus on key regions in medical images, eliminating the need for expensive annotations like bounding boxes or partial labeling. Our method was compared with state-of-the-art approaches on the knee-joint edema dataset. Experimental results show that our approach surpasses the current state-of-the-art methods in terms of performance.

Aiming at the problem of colored noise in the signal, this article proposes a subspace frequency estimation approach under colored noise with application to fault diagnosis of motor rolling bearings. First, a nonlinear discrete-time system is described to generate colored noise. An extended I/O model with parameters of a nonlinear discrete-time system is given by the subspace method. Then, the gap metric-aided system order determination approach is developed for extended observability matrix identification. Then, the data-driven diagnostic observer parameter identification approach and the fast approximate power iterative subspace method are adopted to realize online monitoring for frequency change detection. Eventually, a data-driven design scheme of residual generator is proposed for the implementation of fault detection. The effectiveness of the proposed methods is verified for fault diagnosis performance through numerical simulations and the experimental measurements from the dynamic motor rolling bearing experiment rig.

This article proposes a distributed performance recovery method for multiagent systems with actuator faults in noncooperative games. The local agent (player) can only obtain the policy information of neighboring agents through the communication network. The strategies of nonneighbors in the cost function are unknown, and a leader–follower consensus algorithm is introduced to estimate nonneighbors' strategy. When the actuator faults occur in any agents and lead to performance degradation, i.e., the agents' strategy is biased from its optimal strategy. A distributed optimization control method is proposed to recover performance without changing the original control scheme. An observer-based residual feedback plug-and-play optimization method is used to ensure that the strategies of all agents can still converge to the optimal strategy (or close to the optimal strategy). Numerical case studies are applied to demonstrate the performance and effectiveness of the proposed method.

To ensure system reliability and maintain power supply in fault conditions, this article proposes a fault-tolerant sun-pointing controller based on physics-guided neural networks for handling sensor faults. The proposed controller gains the fault tolerance ability by learning from the behavior of the nominal controller, which integrates a physics-based model with a deep learning model to exploit implicit physical insights during the learning process. The proposed controller enhances the intrinsic interpolative nature of the pure deep learning model, thereby improving fault tolerance for unknown faults. Furthermore, a novel loss function that incorporates the physics-based model is proposed. The loss function assigns different loss terms to the fault-free and the fault datasets, facilitating accurate utilization of loss terms. Unlike traditional active fault-tolerant control schemes, the proposed method requires no explicit fault detection and diagnosis module. The effectiveness of the proposed controller is validated through hardware-in-the-loop simulations. The results indicate that the proposed controller outperforms the pure deep learning controller, as evidenced by a shorter sun-pointing convergence time and more precise angular velocity.

The intelligent manufacturing system is a complex, large-scale, interconnected system composed of many intelligent agents, and there may be physical or information space couplings between the agents. A distributed monitoring system and optimization control method are proposed to ensure the system completes its tasks safely and efficiently. The distributed monitoring system based on the average consensus algorithm is equivalent to the centralized design method, in which the submonitoring system only requires local and neighbor subsystem information. The advantage of this design is that it uses local and interactive information to achieve global diagnosis. In addition, sending data from all subsystems to a central computing node is challenging to implement in large-scale manufacturing systems. Based on the centralized plug-and-play (PnP) optimization control method, an average consensus algorithm distributed manufacturing system PnP optimization control method is proposed. Its advantage is that it uses local information and interactive information to achieve global control optimization. On this basis, an integrated architecture for distributed fault detection and optimization control is developed. The simulation results verify the feasibility and effectiveness of proposed method.

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.

Due to limitations in large-area communication and computation, it can be challenging to apply centralized diagnosis and optimization control design approaches to cascaded systems. This work proposes a distributed diagnosis and optimization control approach, which is realized using data-driven techniques. Specifically, an adaptive observer-based subdiagnosis system design approach is proposed for cascaded systems using only the local input/output (I/O) data and the state estimations of adjacent subsystems. The state estimations from neighboring subsystems are treated as known inputs in the local subsystem. In the centralized design approach, the residual signals generated by all subsystem observers need to be sent to the central computing node to reconstruct controller parameters. The learning process of the local optimization controller only needs to be driven by the residual signals from local and adjacent subsystems, avoiding centralized calculation and reducing the computational burden of the central node. The learning process of the locally optimal controller only needs to be driven by residual signals from the local and neighboring subsystems. In the end, the simulation results verify the effectiveness of the proposed distributed approach.

Coverage control describes the optimal deployment problem of multi-agent system with communication sensors, aiming to drive the multi-agent system reach the optimal deployment location. To accomplish coverage tasks, agents are required to communicate with each other. Thus, the connectivity of multi-agent system is a fundamental requirement in case of agent disconnection and disappearance. In this paper, we propose a distributed coverage control algorithm with connectivity optimization. According to the designed cost function, the controller is divided into two parts: coverage controller and connectivity optimization controller, and the method of gradient descent is used to minimize the cost function to get the controller. Finally, the simulation serves to show the effectiveness of the algorithm.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.