In this study, a novel optimal tracker is developed for the linear quadratic tracking (LQT) problem using an output–feedback adaptive dynamic programming (ADP) framework. By leveraging the minimal polynomial of the exosystem matrix, we parameterize the steady-state input, state, and output, and incorporate them into the performance index of the LQT formulation. Unlike existing studies on LQT, in this study, we introduce a high-order filter to make the signals related to the high-order derivatives of the system output and input observable. Subsequently, we develop an incremental data ADP algorithm to learn the optimal dynamic output–feedback tracker, eliminate the impact of high-order filtering error and state reconstruction error on the iterative learning equation (ILE) within a specified time. Finally, the comprehensive simulation results show the effectiveness of the proposed output–feedback tracker.

Industrial fault diagnosis (FD) often faces challenges due to the scarcity of labeled data and the inability of rule-based systems to handle high-dimensional nonlinearities. This study proposes expert system-assisted neural networks (ES-Nets), a novel hybrid framework featuring ES-guided decision-space sparsification to bridge symbolic reasoning with neural networks (NNs). Unlike traditional data-driven models, this knowledge-preconditioned architecture embeds domain-specific logic into a comprehensive knowledge base before training. Specifically, the optimization process constrains the gradient descent trajectory to a knowledge-consistent subspace, effectively regularizing the parameter updates based on symbolic expert logic rather than purely on statistical gradients. Advanced embedding techniques pre-configure the model, enabling early operational performance and significantly reducing training requirements. Validation on the Tennessee Eastman (TE) process and a real-world petrochemical plant case demonstrates the framework’s superiority in accuracy and operational efficiency. This study provides an efficient and interpretable solution, facilitating effective human–machine collaboration in complex industrial environments.

Graph Neural Networks (GNNs) are effective and popular techniques for representation learning of graph data, significantly relying on message passing mechanism. Most GNNs utilize graph convolution with low-pass filtering to update the representation in a coupled way, ignoring the potential interference between attribute and topology. To solve this challenging issue, this study proposes a Flexible Graph Representation Learning (FGRL) method, which adheres to a decoupling and then fusion framework. Specifically, the FGRL utilizes a two-way representation learning scheme to improve the flexibility of message passing by disentangling information in the attribute space and topology space. Beyond low-pass filtering, high-pass filtering information is crucial for node-specific characteristic preservation and also extracted simultaneously. The FGRL mitigates interference between attribute and topological representations by enhancing complementarity with cross-frequency diversity exploration. A more comprehensive and flexible graph embedding representation could be obtained by adaptively fusing attribute, low-pass, and high-pass information. Experimental results demonstrate that the proposed FGRL achieves superior performance in node classification tasks, verifying its discriminative ability in graph representation learning.

Due to its powerful capability of self-supervised representation learning and clustering, contrastive attributed graph clustering (CAGC) has achieved great success, which mainly depends on effective data augmentation and contrastive objective setting. However, most CAGC methods utilize edges as auxiliary information to obtain node-level embedding representation and only focus on node-level embedding augmentation. This approach overlooks edge-level embedding augmentation and the interactions between node-level and edge-level embedding augmentations across various granularity. Moreover, they often treat all contrastive sample pairs equally, neglecting the significant differences between hard and easy positive-negative sample pairs, which ultimately limits their discriminative capability. To tackle these issues, a novel robust attributed graph clustering (RAGC), incorporating hybrid-collaborative augmentation (HCA) and contrastive sample adaptive-differential awareness (CSADA), is proposed. First, node-level and edge-level embedding representations and augmentations are simultaneously executed to establish a more comprehensive similarity measurement criterion for subsequent contrastive learning. In turn, the discriminative similarity further consciously guides edge augmentation. Second, by leveraging pseudo-label information with high confidence, a CSADA strategy is elaborately designed, which adaptively identifies all contrastive sample pairs and differentially treats them by an innovative weight modulation function. The HCA and CSADA modules mutually reinforce each other in a beneficent cycle, thereby enhancing discriminability in representation learning. Comprehensive graph clustering evaluations over six benchmark datasets demonstrate the effectiveness of the proposed RAGC against several state-of-the-art CAGC methods. The code of RAGC could be available at https://github.com/TianxiangZhao0474/RAGC.git.

Accurate prediction of protein and peptide functions from amino acid sequences is essential for understanding biological processes and advancing biomolecular engineering. Due to the limitations of experimental methods, computational approaches, particularly machine learning, have gained significant attention. However, many existing tools are task-specific and lack adaptability. Here, we propose a BERT-BiLSTM-Attention-TCN Protein Function Prediction Framework (BBATProt), a versatile framework for predicting protein and peptide functions. BBATProt leverages transfer learning with a pretrained bidirectional encoder representations from transformer model to capture high-dimensional features. The custom network integrates bidirectional long short-term memory and temporal convolutional network to align with proteins' spatial characteristics, combining local and global feature extraction via attention mechanisms to achieve more precise predictions. Evaluations demonstrate that BBATProt consistently outperforms state-of-the-art models in tasks such as hydrolytic catalysis, peptide bioactivity, and post-translational modification (PTM) site prediction. Specifically, BBATProt improves accuracy by 2.96%-41.96% in antimicrobial peptide (AMP) prediction and by 0.64%-23.54% in PTM prediction tasks. In terms of area under the receiver operating characteristic curve, improvements range from 0.71% to 40.51% for AMP prediction and 0.62%-27.82% for PTM prediction. Visualizations of feature evolution and refinement via attention mechanisms validate the framework's interpretability, providing transparency into the feature-extraction process and offering deeper insights into the basis of property prediction.

This study investigates the state estimation problem of multi-agent systems under a homologous sensor attack. A distributed filter is proposed to achieve a minimum variance unbiased (MVU) estimation of system states and attacks in the presence of measurement noise. A gain matrix selection method for implementing the MVU estimation is also provided. The proposed filter can be used for positioning corrections affected by global positioning system (GPS) meaconing attacks. This study treats the positioning offset caused by GPS meaconing attacks as a zero-mean white random variable and verifies the validity of this hypothesis through experiments with real GPS signals. Moreover, this study comprehensively analyses the integration of the filters into practical systems. Finally, the effectiveness of the proposed results is verified using simulation examples. Note to Practitioners–This study introduces a filter that can achieve GPS positioning calibration under meaconing attacks. This filter treats the true positioning of the system as a state and the deviation caused by meaconing attacks as a homologous attack. The filter employs a collaborative filter to reconstruct the state, thereby enabling positioning calibration. Additionally, the study explores the relationship between the homology of meaconing attacks and the estimation error of filters. This result reveals that as the homology of attacks increases, the performance of filters also improves.

Unmanned aerial vehicle (UAV) networks, which carry vital information, are prone to various attacks, and hence security issues are a major concern. In this paper, we design and implement a novel covert attack detection and secure control scheme, which operates between the UAV (the physical layer) and ground control station (GCS) (the cyber layer). Covert attacks can alter the UAV states, and yet cause the signals seen by the controller to appear unchanged, resulting in these attacks being more difficult to detect, and hence more dangerous compared to other types of attacks. To unmask the covert attacks, we construct and inject auxiliary signals to both the controller output and the UAV input. The auxiliary signals cause information of the attack to appear in the controller input, which is then fed to a detection observer to detect the attack. Next, we propose an integrated estimation and secure control scheme, comprising a reconstruction observer (which is a sliding mode observer (SMO)) that estimates the system states and attack signal, and an output-feedback controller that utilizes the estimated signals. We perform a series of transformations to the system, such that the design parameters of both reconstruction observer and secure controller are placed in a framework that is solvable using Linear Matrix Inequalities (LMIs). We also prove that the proposed integrated secure controller causes the output tracking errors to satisfy an${{\mathcal {H}}}_{\infty }$performance index. We also rigorously analyze the system performance, and present the necessary conditions for the scheme to be feasible. Finally, simulations are conducted to verify the effectiveness of the proposed scheme. Note to Practitioners—This paper presents a method to detect covert attacks in the UAV network, and to mitigate against those attacks. Covert attacks are more malicious since they are difficult to detect. The proposed method in this paper consists of auxiliary signal injection and a detection observer that will expose and detect the attacks, and a reconstruction observer and secure controller that will estimate the attacks and mitigate its effect on the plant. The controller and observers are designed using Linear Matrix Inequalities to minimize the${{\mathcal {H}}}_{\infty }$gain from the attack on the plant performance. In addition, this paper also investigates the conditions that the plant must satisfy such that the proposed scheme is feasible, and presents them in an easily verifiable form. Finally, the paper also analyses the performance of the proposed scheme in all scenarios - namely before the attack occurs, when the attack occurs but is not yet detected, and after the attack is detected.

Abnormal working conditions often occur in modern industrial processes. Subsequently, if maintenance is not carried out in a timely manner, such abnormal working conditions can cause changes in system output variables, resulting in a decrease in product quality and even production accidents. This study proposes a self-healing control framework that extracts system input and output variable historical data under normal conditions. Then, based on simple correlation analyses and canonical correlation analyses, the correlation between historical input and output data is constructed, and the self-healing control law is calculated using the current batch of system output. The setpoint of the underlying controller and process variables are adjusted according to the control law to restore the system output variables to normal or near normal, thus ensuring the stability of product quality. This study demonstrates the convergence of system output variables and analyzes the computational complexity of the two algorithms. Finally, applied to water storage and continuous stirring reactor systems, the proposed self-healing control framework is validated to have a significant effect in restoring system output variables to normal or near normal, and presents significant safety and economic value in industrial production. Note to Practitioners—Abnormal conditions and fault conditions often occur in traditional complex industrial production processes, such as temperature, pressure fluctuations and pipeline blockages. When abnormal working conditions occur, certain key variables will fluctuate, which will have a huge impact on system safety and stable production. Therefore, this paper proposes a self-healing control system. The system can timely detect the occurrence of abnormal working conditions and make corresponding self-healing control decisions for abnormal working conditions. This system is compatible with several types of algorithms, and two different algorithms are designed in this article. Both algorithms can maintain the stability of key system output variables, and each has its own characteristics of adjustment and operation. In order to explore the application scope of the system, this paper carried out tests in various application scenarios and abnormal working conditions. Experimental results show that the proposed self-healing control system has significant self-healing effects in various industrial production processes.

In this study, a novel adaptive resilient control scheme is proposed to solve the prescribed-time stabilization problem of switched nonlinear systems under false data injection (FDI) attacks. A switched nonlinear state observer (SNSO) is designed to reconstruct unmeasurable system states. Additionally, a novel coordinate transformation and a Nussbaum function are introduced to address the challenges posed by FDI attacks on feedback control channels. The command filter is incorporated into the backstepping control framework, resulting in an adaptive resilient controller to ensure the stability and robustness of the system. Compared with the existing stability results of switched nonlinear systems, this study introduces an innovative prescribed-time scale transformation function into the SNSO and the adaptive resilient controller, thereby yielding a more relaxed criterion for user-defined settling time in the absence of prior information. The proposed piecewise switched adaptive laws reduce the conservatism of the designed controller in the presence of arbitrary switching. Finally, the feasibility of the proposed control theory is validated via simulations.

Multiview subspace clustering (MSC) maximizes the utilization of complementary description information provided by multiview data and achieves impressive clustering performance. However, most of them are inefficient or even invalid among large-scale scenarios due to expensive computational complexity. Recently, anchor strategy has been developed to address this, which selects a few representative samples as anchor points for representation learning and anchor graph construction. However, most of them only explore single cross-view correlation, i.e., cross-view consistency from the global aspect or cross-view complementarity from the local aspect, which provides insufficient semantic correlation understanding and exploration for complex multiview data. To effectively address this issue, this study proposes a fast multiview subspace clustering (FMSC) with local-global anchor representation collaborative learning. FMSC integrates the discriminative anchor points learning and anchor graph construction with optimal structure into a joint framework. Furthermore, local (view-specific) and global (view-shared) anchor representations are learned collaboratively under two interaction strategies at different levels, providing beneficial guidance from global learning to local learning. Thus, the proposed FMSC can maximize the exploration of the complementarity-consistency among multiview data and capture a more comprehensive semantic correlation. More importantly, an effective algorithm with linear complexity is designed to solve the corresponding optimization problem of FMSC, making it more practical in large-scale clustering tasks. Extensive experimental results confirm the superiority of the proposed FMSC in both clustering performance and computational efficiency.

Anomaly detection (AD) is typically regarded as an unsupervised learning task, where the training data either do not contain any anomalous samples or contain only a few unlabeled anomalous samples. In fact, in many real scenarios such as fault diagnosis and disease detection, a small number of anomalous samples labeled by domain experts are often available during the training phase, which makes semi-supervised AD (SAD) more appealing, though the related study is quite limited. Existing semi-supervised AD methods directly add optimization terms of anomalous samples to the optimization objective of unsupervised AD (UAD), where the effects of the limited labeled anomalous data on the optimization process become trivial and they cannot fully contribute to the detection task. To cover the shortage, in this work, we propose a novel semi-supervised AD method to fully use the limited labeled anomalous data and further to boost detection performance. The proposed method learns a nonlinear transformation to project normal data into a compact target distribution and simultaneously to project exposed anomalous samples into another target distribution, where the two target distributions do not overlap each other. The goal is difficult to achieve because of the scarcity of anomalous samples. To address this problem, we propose to generate a large number of intermediate samples interpolating between normal and anomalous data and project them into a third target distribution lying between the aforementioned two target distributions. Empirical results on multiple benchmarks with varying domains demonstrate the superiority of our method over existing supervised and semi-supervised AD methods.

The emergence of new operating modes due to changes in manufacturing demands or production technologies leads to the occurrence of multimode industrial processes. To address the multimode problem, the dictionary learning method is widely utilized due to its excellent sparse representation technique capability. Despite the abundance of offline data enabling dictionary learning to model various modes, consolidating dictionaries from individual models considerably increases model complexity. Therefore, a method called adaptive importance coding dictionary learning is proposed in this study on the basis of dictionary learning. This approach utilizes dictionary learning techniques to determine the importance of dictionary atoms by encoding frequencies and selecting significant dictionary atoms to form a new dictionary, remarkably reducing the complexity of the dictionary model. In accordance with this idea, a comprehensive framework for monitoring multimode processes, including online fault detection, mode classification, fault isolation, and new mode updates, is introduced. Experimental validations are conducted through numerical examples, simulations of continuous stirred tank reactors, and application in an actual petrochemical process. The experimental results demonstrate the effectiveness and feasibility of the proposed method in monitoring multimode processes.

This article explores the issue of fault-tolerant optimal pursuit strategies in a nonlinear pursuit-evasion (PE) game involving multiple pursuers and a single evader. The main challenge lies in ensuring the successful capture of the evader despite the presence of actuator partial loss of effectiveness and bias faults within the pursuers group. To overcome this challenge, a two-layer control architecture is proposed. At the control layer, an integral sliding-mode controller is developed to mitigate the impact of bias faults, and an adaptive estimation mechanism is incorporated to identify the fault parameters. At the decision-making layer, performance index functions for both the pursuers and the evader are formulated based on the PE state error and their respective control strategies, and optimal pursuit and evasion strategies are derived by solving the associated Hamilton–Jacobi–Isaacs (HJI) equations. Furthermore, adaptive dynamic programming (ADP) is employed, with each participant using a critic network to approximate the optimal strategies for the pursuers and the evader. The proposed control mechanism is theoretically proven to ensure that all closed-loop signals remain uniformly ultimately bounded, allowing the faulty pursuers to successfully capture the evader. Finally, the effectiveness of the approach is validated through two simulation examples.

The self-attention mechanism has advantages in analyzing the internal characteristics of data and capturing local information. Therefore, it is applied to fault detection. Since complex industrial processes usually contain a mixture of properties, such as nonlinearity, dynamics, and non-Gaussianity, this places higher demands on fault detection methods. To further improve the performance of the self-attention mechanism for fault detection in complex industrial processes, an innovative self-attention mechanism integrating the adaptive double subspace (ISA-ADS) method is proposed for fault detection. First, the query matrix, the key matrix, and the value matrix of the self-attention mechanism are designed so that they can better focus on local information through the adaptive sample weight allocation strategy. At the same time, this design reduces the interference of nonlinear and noisy information and amplifies the impact of important fault information in the sample. Second, considering the problem of Gaussian and non-Gaussian distributions of the data and the different degrees of influence of different subspaces on the final process monitoring results, an adaptive Gaussian and non-Gaussian double subspace fault detection model based on the self-attention output matrix is established, which adaptively assigns different weights to different subspaces by determining their importance. The statistics of different subspaces are fused using the Bayesian inference and a new adaptive monitoring statistic is constructed to obtain a more accurate monitoring of the process state. Finally, the ISA-ADS is validated using the Tennessee Eastman (TE) process and the actual polyethylene production process. The results show that ISA-ADS has a low false alarm rate and a high fault detection rate, verifying the effectiveness of its fault detection performance.

Nonlinear and non-Gaussian characteristics are common in industrial processes. Artificial neural correlation analysis (ANCA) is a good nonlinear process monitoring algorithm, which combines classical correlation analysis with artificial neural networks. However, its performance is not very satisfactory for industrial processes with non-Gaussian characteristics. To solve non-Gaussian problems, almost all the existing process monitoring algorithms only consider the effect of kurtosis. Nevertheless, both kurtosis and skewness affect the data distribution. To improve the limitations of existing algorithms, this study proposes a new process monitoring algorithm named Jarque–Bera-based ANCA. This new algorithm makes many improvements to ANCA scheme, and the designed loss function combines the influence of both kurtosis and skewness on the data distribution, which not only maintains the advantages of the ANCA algorithm in solving nonlinear problems, but also provides superior monitoring performance in non-Gaussian processes. Furthermore, the superior performance of the proposed new algorithm is verified through simulations using non-Gaussian and nonlinear numerical examples, the Tennessee Eastman process, and catalytic cracking units.

Attributed graph clustering with auto-encoder (AE) and graph convolutional network (GCN) has achieved promising performance by fusing node attribute feature and structural graph information. However, there are some limitations: (i) structural information from pre-defined graph is inaccurate and insufficient for graph representation learning; (ii) graph embedding of last layer only contains partial information for clustering which inevitably deteriorates clustering performance. To address these issues, we propose the Improved Attributed Graph Clustering method with Representation and Structure Augmentation (IAGC-RSA). The representation augmentor with multi-scale and multi-source representation attention fusion and structure augmentor with adaptive graph learning are designed for information augmentation from structure level and feature level. Thus, IAGC-RSA could learn a more comprehensive and discriminative graph embedding representation for subsequent clustering task. Experimental results conducted on some benchmark datasets demonstrate the effectiveness of IAGC-RSA for node clustering task.

As a novel multivariable regression method, orthonormal subspace analysis (OSA) divides input data and key performance indicators (KPIs) data into three orthonormal subspaces, resulting in effective detection of KPI-related faults in industrial processes. However, the detection of incipient faults is still a challenging problem. To this end, a deep OSA model is established to extract incipient fault information and detect it in this article. First, the correlation matrix (CM), based on the KPI-related information and input-related information that are completely orthogonal to irrelevant information, is calculated to accurately describe the correlation between input and KPIs. Second, the deep singular value decomposition is performed on CM to divide KPI-related information into multiple subspaces, deeply separate KPI-related information and, not least, mine incipient features of data. Furthermore, the detection statistics of multiple subspaces, fault detectability, and complexity are presented. Finally, a numerical example and an actual thermal power plant are employed to confirm the validity of the proposed method. The results demonstrate that the proposed deep OSA achieves much better detectability for incipient KPI-related fault in theoretical and engineering application than some other existing methods.

With the development of urban water supply systems, the leakage detection of water supply pipe networks is of great significance for the safe operation of urban water supply systems. In practice, due to the short of the important data, traditional detection models often fail to achieve good detection results. Therefore, this article proposes a novel pipeline attention integrating the long short-term memory (LSTM) to detect the leakage. The density-based spatial clustering of applications with noise (DBSCAN) method divides the water supply network into several regions according to the leakage characteristics of pipelines. Then, the LSTM extracts dynamic time-varying hydraulic features of pipelines, and the pipeline attention extracts the dynamically changing features between pipelines. Finally, the Attention-LSTM model is used to detect the leak region of urban water supply systems. Compared with multilayer perceptron classifier,$K$neighbors classifier, decision tree classifier, support vector machine classifier, multiscale fully convolutional network, one-dimensional multichannel convolution neural network, and variational autoencoder, the F1-Score of the Attention-LSTM is greatly improved, which are 116%, 120%, 254%, 180%, 21%, 26%, and 27%, respectively. Therefore, the proposed model can accurately detect the leakage of water supply network and effectively reduce the waste of resources.

Fault detection has long been a hot research issue for industry. Many common algorithms such as principal component analysis, recursive transformed component statistical analysis and moments-based robust principal component analysis can deal with static processes only, whereas most industrial processes are dynamic. Therefore, dynamic principal component analysis and recursive dynamic transformed component statistical analysis have been proposed to deal with dynamic processes by expanding the dimensions. The computational complexity of these algorithms are greatly increased, and these algorithms cannot divide the data space accurately. In this paper, we propose a novel algorithm called recursive innovational component statistical analysis (RICSA), which estimates the dynamic structure of the data, accurately divides the data space into dynamic components and innovational components. In unsteady state process, the statistical characteristics of data will change, and RICSA can classify these characteristics into dynamic components by dividing the data space, instead of treating them as faults, thereby reducing the false alarm rate. Through a series of comparative experiments, especially on practical coal pulverizing system in the 1000-MW ultra-supercritical thermal power plant, Zhoushan Power Plant, we found the recursive innovational component statistical analysis to realize a higher accuracy rate and a lower false alarm rate and detection delay, which verifies its superiority. We also discuss the reduced computational complexity associated with the recursive innovational component statistical analysis. Note to Practitioners—Aiming at the dynamic processes, the recursive innovational component statistical analysis algorithm proposed in this paper can divide the data space into dynamic components and innovational components by estimating the dynamic structure of the data. In addition, in the monitoring process, computational complexity is also a key point. Compared with recursive dynamic transformed component statistical analysis, recursive innovational component statistical analysis has lower computational complexity and higher accuracy. After multiple sets of experiments, it can be verified that recursive innovational component statistical analysis has a great monitoring effect in the actual industrial process, and can provide early warning of faults.

Effective fault diagnosis can be obtained by using multiblock global orthogonal projections to latent structures (MBGOPLS), but there exist certain limitations in this method in terms of block division intelligence and model robustness to outliers. Although these issues can be addressed by developing correlation analysis, such as mutual information and copula-correlation, the complex coupling relationship between variables has not been fully examined. In this study, a robust MBGOPLS is proposed to intelligently diagnose faults using a relatively stable model. First, a double hierarchical clustering method is established, in which internal hierarchical clustering is performed based on Euclidean distance to discretize the samples. Subsequently, external hierarchical clustering is adopted based on mutual information distance to divide variables into different blocks, which results in intelligent block division while suppressing the influence of outliers. Second, a robust block regression coefficient matrix (BRCM) is obtained by employing joint$\ell _{2,1}$-norm minimization on the BRCM and prediction error of the correlation between the block input and output. Furthermore, BRCM is integrated into the MBGOPLS framework. Therefore, the proposed method is robust to outliers while retaining the diagnostic properties of MBGOPLS. Finally, the proposed method is applied in a numerical case and an actual thermal power plant. The results verify the method’s applicability and superiority.Note to Practitioners—With increasing large-scale, complex and intelligent to achieve anticipant performance, thermal power plant is prone to faults that can lead to unplanned outages. Meanwhile, complex operation mechanism and environment, and diverse data acquisition sensors make the coupling relationship between variables complex, and collected data often contain numerous outliers, which happens frequently in modern industrial process. Therefore, fault diagnosis is critical to modern industrial process, and the diagnosis accuracy and robustness are the main challenges. This forces us to ensure the block division accuracy and model robustness when using multiblock-based fault diagnosis technology. This paper proposes a robust MBGOPLS to intelligently diagnose faults of thermal power plant using a relatively stable model. Additionally, the proposed method can be extended to fault diagnosis for other large-scale processes.

This paper proposes a novel active fault tolerant control (FTC) scheme for a 3-degree-of-freedom (3-DOF) helicopter with sensor faults. As a challenge, only attitude angles are considered available, so that when the sensors measuring the elevation/travel angles are faulty, the system with respect to the remaining healthy outputs is not detectable. To circumvent this issue, a new interval observer (IO) with adaptive parameters is formulated, providing good estimates of both disturbances and unmeasurable states. This IO acts not only as a state estimator for nominal controller but also as a fault detection and isolation (FDI) observer for the fault occurrence and location. After the fault location is determined, two different fault estimation (FE) schemes are developed according to whether or not the system is detectable. Using the fault estimates, a fault tolerant controller is constructed to ensure the acceptable performance of the faulty system. Finally, experiments on the 3-DOF helicopter platform are conducted to verify the effectiveness of the proposed scheme.

Accurate process monitoring plays a crucial role in thermal power plants since it constitutes large-scale industrial equipment and its production safety is of great significance. Therefore, accurate process monitoring is very important for thermal power plants. The vigorous nature of the production process requires dynamic algorithms for monitoring. Since the common dynamic algorithm is mainly based on data expansion, the online computing complexity is too high because of data redundancy. Accordingly, this article proposes an innovative, dynamic process monitoring algorithm called autocorrelation feature analysis (AFA). AFA mines the dynamic information of continuous samples by calculating the correlation between the current time and past time features. While improving the monitoring effect, the AFA algorithm also has extremely low online computational complexity, even lower than common static algorithms, such as principal component analysis. Furthermore, this study exhibits the general form of dynamic additive faults for the first time and verifies the reliability of the algorithm through fault detectability analysis. Conclusively, the superiority of the AFA algorithm is verified on a numerical example, continuous stirred tank reactor (CSTR), and real data measured from a 1000-MW ultrasupercritical thermal power plant.

This study addresses the problem of the estimation of state when heterogeneous multiagent systems are affected by homologous unknown inputs (UIs). Homologous UIs refer to identical UIs affecting different agents. An improved semidistributed filter based on previous research is proposed. The improved filter uses neighbors’ information for UI estimation but not state estimation. A necessary and sufficient condition for the proposed filter to achieve minimum-variance unbiased estimation is presented and proven. Moreover, the asymptotic stability of the filter is analyzed. A sufficient condition of the asymptotic stability is presented and proven. The theoretical and numerical analyses indicate that the proposed filter has less communication pressure, fewer calculation requirements, and better estimation performance compared with the existing solutions.Note to Practitioners—In the industry, homologous unknown inputs (UIs) exist in many different systems. For example, the same ambient temperature affects the performance of every battery in a battery pack. Similarly, the same wind power can affect different aircrafts flying in the same region. Temperature and wind power can be considered the homologous UIs of a multiagent system. Estimation of homologous UIs is important because of the latter’s massive impact on the system. In this study, data transmission delay and packet loss are ignored. Hence, the study is limited to low-rate systems. Moreover, nonlinear filters must be studied further in future work.

In modern industrial production, rotating machinery plays an important role. The gears in this machinery adjust the speed and transmission of torque. Therefore, when the gear fails, it is very important to be able to diagnose the fault quickly and accurately. Gear vibration signals are often used in gear fault diagnosis, but the fault signal is often overwhelmed by noises. To enable the scientific and efficient detection of faults, this study proposes a gear fault diagnosis method based on variational modal decomposition (VMD) and wide+narrow visual field neural networks (WNVNNs), namely VMD-WNVNN. VMD-WNVNN consists of two stages. In the feature extraction stage, VMD and Pearson correlation coefficients are used to decompose and reconstruct the original data to obtain the features of these data in the frequency domain. In the classification stage, WNVNN is used to classify the data based on features. The final results of the gear fault diagnosis experiments show that this method not only has higher classification accuracy but also has higher classification stability than other recently proposed methods. Note to Practitioners—The gearbox is composed of many mechanical parts, such as gears, shafts, and bearings. Therefore, the vibration signal collected by the vibration sensor on the gearbox housing will contain the vibration signal of each part and the noise caused by processing errors. Therefore, if some methods can be used to efficiently extract the characteristic signals required for diagnosis in the data processing stage, the efficiency of fault diagnosis will be greatly improved. This article takes the health of gears as the research object and proposes a method that combines adaptive signal decomposition and deep learning technology. Experimental results show that this method has higher classification accuracy and classification stability than other methods proposed recently.

In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method.

Secure state estimation (SSE) is a problem to defense false-data injection attacks. This study designs an online distributed SSE of heterogeneous multiagent systems under homologous attack. A triple-loop observer is proposed to estimate the state and attack signal simultaneously. The inner loop keeps the estimations of the attack signal the same using average consensus. The middle loop adjusts the estimations via residual information. The outer loop runs when system measurements change. A sufficient condition that estimations asymptotically converge to the real value is obtained and proved. Finally, the proposed observer has been tested on the global positioning system.

Fisher discriminant analysis (FDA) is a widely used tool for fault diagnosis. In addition, many modifications have also been proposed recently in the literature in order to overcome certain limitations of the traditional FDA method. However, the incipient fault diagnosis problem is not well handled by traditional FDA and its variants. In this paper, through the introduction of smoothing techniques, a new method called smoothed FDA (SFDA) is proposed to enhance the fault diagnosis performance for incipient faults. Fault diagnosability analyses of the FDA and SFDA approaches are carried out and compared with each other. It is pointed out through theoretical analysis that SFDA is superior to FDA in terms of incipient fault diagnosis. Simulation studies on a continuous stirred tank reactor process are used to demonstrate the effectiveness of the SFDA method, in comparison with traditional FDA.

In this paper, control performance assessment (CPA) is studied for batch processes controlled by iterative learning control (ILC). A 2-D linear quadratic Gaussian (LQG) benchmark is proposed to assess the performance of ILC in a 2-D framework. Based on the 2-D theory, an ILC-controlled batch process is first converted into a 2-D Roesser model. Subsequently, in order to assess the control performance of the converted 2-D system, the conventional LQG tradeoff curve is upgraded to the LQG performance assessment tradeoff surface. However, the complete knowledge of the system model is required to obtain the LQG tradeoff surface. For system without accurate model knowledge, a novel data-driven CPA method is further proposed. In this case, a novel 2-D closed-loop subspace identification method is proposed to identify the converted 2-D Roesser system. Based on the identified model, the LQG tradeoff surface can be obtained and utilized to assess the control performance. Overall, several simulation examples verified the feasibility and effectiveness of the proposed method.

This paper addresses the finite horizon H∞fault estimation problem for 2-D linear discrete time-varying systems with bounded unknown input and measurement noise. The main contribution of this paper is the H∞fault estimator for 2-D systems with a necessary and sufficient existence condition. By introducing a partially equivalent stochastic dynamic system in Krein space, the necessary and sufficient condition for the existence of the H∞fault estimator is derived based on innovation analysis and projection formula in Krein space. Then, the solution of the estimator is achieved by means of a Riccati-like difference equation for 2-D systems. Finally, a thermal process example is given to demonstrate the effectiveness of the proposed method.

This paper contributes to a point-to-point iterative learning control problem for stochastic systems without prior information on system matrices. The stochastic approximation technique with gradient estimation by random difference is introduced to design the update law for input. It is strictly proved that the input sequence would converge almost surely to the optimal one, which minimizes the averaged tracking performance index. An illustrative simulation shows the effectiveness of the proposed algorithm.

Higher-order cumulants analysis (HCA) is an up-to-date method that utilizes higher-order cumulants rather than lower-order statistics (e.g., variances) to achieve the state monitoring purpose. Although HCA has a strong capability for state monitoring, it still exhibits many inadequacies for monitoring dynamic processes. Currently, there are various approaches (e.g., dynamic principle component analysis and dynamic independent component analysis) that are applicable to dynamic features. However, the key step of dynamic state monitoring methods is determination of the time lags or the lag structure. Almost all the reported dynamic methods select a single number of time lags for all variables. This simple selection method may not be appropriate since it is generally not possible that all variables have the same lag structure. In order to address this issue, a new lag selection method for each individual variable is proposed in this study. Hence, two dynamic higher-order cumulants analysis (DHCA) approaches are proposed for state monitoring, among which one is based on the conventional lag selection method and another is based on the new lag selection method proposed in this study. The two kinds of DHCA approaches are tested on the Tennessee Eastman process, and are demonstrated to be superior to all the compared methods.

Good glycemic control through insulin administration among intensive care unit (ICU) patients can reduce mortality significantly; however, it remains a big challenge because of scarcity of individualized models for ICU patients. To deal with this challenge, a new combination of particle swarm optimization (PSO) and model predictive control (MPC) has been proposed to identify the model online as well as to optimally design the input, i.e., the insulin delivery rate automatically. According to the population distribution, ten typical linear dynamic models were selected such that any patient's model could be approximated by a linear combination of these ten typical models. PSO was used to update the weight coefficients while MPC was used to design the insulin delivery rate based on the combination model identified by using PSO. The proposed strategy was compared with the Yale protocol on 30 virtual subjects. According to the control-variability grid analysis, the percentage values in A + B zone were, respectively, 100% under the proposed strategy and while 51% under the Yale protocol, which demonstrates the superior performance of the proposed strategy. As a good candidate for the full closed-loop insulin delivery method, this new combination can control the glucose level by bringing it to a safe range promptly thereby reducing the risk of death.

In this paper, an iterative learning control (ILC) algorithm with iteration varying gain is proposed for stochastic point-to-point tracking systems. The general formulation of point-to-point control problem and an associated performance index are given. The almost sure convergence of the given recursive algorithm is strictly proved in term of a modified tracking error. An illustrative example shows the effectiveness of the proposed approach.

Wireless sensor network localization is an essential problem that has attracted increasing attention due to wide requirements such as in-door navigation, autonomous vehicle, intrusion detection, and so on. With the a priori knowledge of the positions of sensor nodes and their measurements to targets in the wireless sensor networks (WSNs), i.e. posterior knowledge, such as distance and angle measurements, it is possible to estimate the position of targets through different algorithms. In this contribution, two approaches based on least-squares and Kalman filter are described for localization of one static target in the WSNs with distance, angle, or both distance and angle measurements, respectively. Noting that the measurements of these sensors are generally noisy of certain degree, it is crucial and interesting to analyze how the accuracy of localization is affected by the sensor errors and the sensor network, which may help to provide guideline on choosing the specification of sensors and designing the sensor network. To this end, we make theoretical analysis for the different methods based on three types of measurement noise: bounded noise, uniformly distributed noises, and Gaussian white noises. Simulation results illustrate the performance comparison of these different methods.