This article presents two fault-tolerant control (FTC) frameworks for high-order fully actuated systems (HOFASs) with actuator faults, sensor faults, and measurement noise. After analyzing the observable architecture corresponding to each measurement, actuator faults are compensated through fusion observers, and sensor faults are rejected through redundant observability. The first FTC framework with traditional fusion observers can merely yield an ultimately uniformly bounded (UUB) error system. To further suppress measurement noise, a dead-zone fusion observation strategy is applied to the FTC redesign. Especially in a linear HOFAS model, the noise suppression performance of the novel FTC framework is proved to be superior. In more general systems, two comparative cases experimentally illustrate trajectory tracking results and noise suppression performance.

When disturbances or nonlinearities couple the observer and the controller, implementing active fault-tolerant control (AFTC) via the separation principle (SP) becomes challenging. This article proposes a novel AFTC framework with the goal of decoupling design for a class of uncertain nonlinear systems, thereby recovering the use of SP in AFTC design. An observer is developed for fault diagnosis and state estimation based on the system outputs, and the boundedness of the estimation errors is guaranteed. Next, an active fault-tolerant controller integrated with an adaptive mechanism is constructed for fault accommodation using the obtained fault information. To mitigate bidirectional influences between the observer and controller designs, all estimation errors and disturbances are treated as new disturbances in the AFTC system (AFTCS), and adaptive updating terms are designed to compensate for these disturbances. The stability of the AFTCS is analyzed, ensuring that all signals in the closed-loop system remain bounded and that the output tracking error converges to a neighborhood around zero. The effectiveness of the proposed approach is illustrated through a numerical simulation example.

This paper is concerned with the active fault diagnosis (AFD) problem for a class of linear stochastic systems with unknown prior probabilities of modes. In AFD within the Bayesian decision framework, the unknown prior probabilities typically make diagnosis performance compromised. A novel robust AFD approach is developed to settle the AFD problem under uncertainties in prior probabilities of modes for linear stochastic systems. The input design goal is to minimize the misdiagnosis probability on the basis of the robustness for uncertainties in prior probabilities of modes. The input design problem is characterized as a min-max optimization problem. As there is no closed-form expression for the misdiagnosis probability, its a novel upper bound family is deduced as an alternative. The extensively utilized Bhattacharyya upper bound is proven to be included in this upper bound family. The tightness of the upper bounds in the proposed family is analyzed. A novel two-layer optimization method is proposed to solve the input design problem to global optimality. Two numerical examples are presented to illustrate the effectiveness and superiority of the developed robust AFD approach. Note to Practitioners—The majority of the existing stochastic AFD results are developed within the Bayesian decision framework, where prior probabilities of system modes are required to be known. However, the prior probabilities are difficult to obtain in practice, which makes these AFD approaches impractical. A major challenge in stochastic AFD is to evaluate the probability of misdiagnosis due to the presence of multivariate integrals in its expression. An extensively utilized solution is to exploit its Bhattacharyya upper bound as an alternative. Unfortunately, there is looseness in the Bhattacharyya upper bound under certain circumstances, which is likely to make the misdiagnosis probability evaluated inaccurately and render diagnosis performance unsatisfactory. In this paper, the authors develop a novel robust AFD approach with a new upper bound family on the misdiagnosis probability to improve diagnosis performance for linear stochastic systems with unknown prior probabilities of modes. The developed AFD approach is robust for uncertainties in prior probabilities of modes. The input design problem is characterized as a min-max optimization problem, which is solved to global optimality by a novel two-layer optimization method. The upper bounds in the proposed family are tighter than the Bhattacharyya upper bound under certain conditions. For nonlinear stochastic systems with unknown prior probabilities of modes, the proposed robust AFD may not be applicable. In future research, we will focus on the robust AFD problem for nonlinear stochastic systems with unknown prior probabilities of modes.

With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition characteristics pose significant challenges to traditional fault diagnosis methods. In this context, multi-condition fault diagnosis has gradually become a key area of research, attracting extensive attention from both academia and industry. This paper aims to provide a systematic and comprehensive review of existing research in the field. Firstly, the mathematical definition of the problem is presented, followed by an overview of the current research status. Subsequently, the existing literature is reviewed and categorized from the perspectives of single-model and multi-model approaches. In addition, typical real-world application scenarios are then summarized and analyzed. Finally, the key challenges and prospects in the field are thoroughly discussed.

Real-time safety assessment is a critical process for identifying and analyzing potential safety hazards in industrial applications. Active learning has been widely recognized as an effective technique for addressing such issues by utilizing small labeled samples to achieve high evaluation performance. However, in real-world scenarios, the performance of query strategies can be significantly impacted by the data environment. Therefore, it is crucial to co-design a strategy using different criteria to ensure reliable and stable safety assessment results for system operation. In this paper, we propose a contrastive preference-guided active learning approach to tackle chunk-level real-time safety assessment tasks in non-stationary environments. Firstly, we construct negative ranking lists and random lists. Then, we introduce Jeffrey divergence to measure pairwise ranking correlation. By leveraging contrastive preference relationships, we can effectively obtain the value of samples in data chunks with preference aggregation procedures. To verify the effectiveness of the proposed method, we conduct numerous experiments using realistic data from the JiaoLong deep-sea manned submersible. The results demonstrate that our approach outperforms most existing advanced methods in terms of performance stability and accuracy.Note to Practitioners—In situations where a significant number of samples must be processed concurrently to promptly identify and mitigate potential hazards, it is vital to consider the responsibilities of real-time safety assessment (RTSA). The proposed approach offers a resolution to the difficulty of acquiring annotations for all samples due to the continual stream of data. Multiple query criteria can be seamlessly integrated, which enhances the stability and superiority of learning performance simultaneously. The approach’s scalability, flexibility, and effectiveness render it a valuable instrument for guaranteeing a prompt response to emergent procedural threats and safety. It can accommodate any number and type of advanced single query criteria, making it easy to adapt to various scenarios. The proposed CPRC approach signifies a significant breakthrough in the field of real-time safety assessment, with the potential to enhance safety outcomes in industrial settings. Multiple experiments involving the realistic JiaoLong deep-sea manned submersible were conducted, and the outcomes demonstrate the benefits of this approach for practical RTSA applications.

This article proposes a novel multicontroller-based fault tolerance method to cope with a class of high-order sub-fully actuated systems (sub-FASs) with nonlinear uncertainties and actuator faults. As a promising control-oriented theory, the FAS approach is a convenient and powerful tool for nonlinear control. However, the stabilization of sub-FASs, whose input matrix function is not globally invertible, is more sophisticated and challenging due to the issue of control singularity. To address the global fault-tolerant stabilization of uncertain sub-FASs, a high-order nonlinear system model with both multiplicative and additive actuator faults is considered. By introducing the concepts of linear singular set and singularity function, the entire state space is analytically divided into several regions. Then, according to the initial states of system, three different control strategies are developed to overcome singularity and achieve global stabilization, including a FAS-based stabilizing control law, a singularity-avoid tracking control strategy, as well as a singularity-free switching control strategy. The closed-loop response of the faulty system is proven to be ultimately uniformly bounded in all cases, and the effectiveness of proposed method is illustrated through a numerical example.

Effective fault diagnosis is crucial for maintaining the reliability and safety of industrial systems. Incremental learning, which enables models to continuously update and adapt to new data or emerging fault classes without complete retraining, has recently gained attention as a promising solution for addressing nonstationary data streams in fault diagnosis applications. Nevertheless, most existing review articles on fault diagnosis adopt a broad perspective, primarily discussing general techniques such as deep learning and transfer learning, without providing a dedicated focus on incremental learning strategies. To the best of our knowledge, it is the first review focusing specifically on incremental learning-enabled fault diagnosis methods. In this work, state-of-the-art incremental learning-enabled fault diagnosis are systematically reviewed. These methods are categorized into distinct groups based on their incremental learning strategies and application contexts. In addition, major challenges associated with applying incremental learning to fault diagnosis, including concept drift and catastrophic forgetting, are discussed, along with emerging solutions proposed to address these issues. A novel taxonomy and perspective on incremental learning-enabled fault diagnosis approaches is presented, providing a timely and comprehensive reference for researchers and practitioners in this evolving field.

This article investigates the distributed secure state estimation problem for a heterogeneous sensor network monitoring a dynamical system while under false-data injection attacks. Different from existing literature, the attacker is capable of corrupting a time-varying subset of sensors and altering their measurements. Based on the upper bound estimation technique, a novel distributed secure estimation method is proposed, which can provide an upper bound for estimation error and detect compromised sensors. The sufficient condition for the boundedness of the estimation error is provided and the feasibility of the estimator is further analyzed. Simulations are provided to demonstrate the effectiveness of the proposed estimator.

This article is concerned with the unified design problem of active fault diagnosis (AFD) and optimal control (OC) for a class of stochastic systems. In AFD, the input is designed to minimize the misdiagnosis probability, whereas the control performance is optimized by designing the input in OC. By exploiting a finite-horizon constrained optimization formulation, a novel unified design framework with normalization strategies is developed for AFD and OC to balance the diagnosis and control performances. The control task is treated as a constraint of the optimization problems to ensure that the task is fulfilled. The Holder divergence family is proposed to construct a new input design criterion for AFD with multifault modes, whose maximization is proven to minimize an upper bound on the misdiagnosis probability. The input design problem under the constructed design criterion for AFD and the input design problem for OC can be solved to global optimality. Based on this global optimality, normalization strategies are utilized to adjust the magnitudes of the objective functions for AFD and OC into the same scale. The quantitative balance between the diagnosis and control performances can be achieved within the developed unified design framework. The effectiveness and superiority of the proposed approach are demonstrated by simulation results on a four-tank system.

This article investigates the active fault diagnosis (AFD) problem for a class of multiple-input multiple-output (MIMO) stochastic systems. A novel AFD framework is proposed based on the Neyman-Pearson framework, within which the input is designed to improve diagnosis performance by maximizing the fault detection rate (FDR) under a given false alarm rate (FAR). The analytical relationship between the FDR and the FAR is deduced to construct a new input design criterion for AFD, whose maximization is proven to result in the minimum of the misdiagnosis probability and the maximum of the FDR under a given FAR. The constructed design criterion allows the input design optimization problem to be solved to global optimums. By injecting the globally optimal input into the system, the AFD decision is made based on a newly established test statistic and the corresponding decision rule according to the real-time measurement output. The AFD decision rule within the Neyman-Pearson framework is proven to be equivalent to that within the Bayesian decision framework with given loss factors. Numerical experiment results demonstrate the effectiveness and superiority of the proposed AFD approach, moreover, the diagnosis performance can be adjusted between the FDR and the FAR according to practical requirements by changing the detection threshold.

Ensuring the operational safety of complex systems often stands as a top priority, and employing data-driven safety assessment offers a promising way to achieve this goal. However, as systems often operate across various modes, models trained for one mode may not be applicable to others. Moreover, conducting the operational safety assessment task in open environments, where unknown scenarios can emerge unexpectedly, remains a challenging issue. Unsupervised domain adaptation allows for transferring models from a source domain with labeled data to a target domain with only unlabeled data. Yet, the effectiveness of such models diminishes when faced with unknown scenarios not observed in the source domain. Hence, this article introduces a novel problem termed open active domain adaptation for the safety assessment task, which addresses task-related unknown scenarios in the target domain and introduces a limited labeling budget to enhance model performance. To tackle this problem, an adversarial weighted active domain adaptation scheme is proposed, which incorporates an active labeling process and a weighting mechanism. This scheme leverages adversarial training in both the weighting mechanism and the domain adaptation process. Specifically, it identifies representative unlabeled data capable of approximating the target data distribution for label annotation. Furthermore, instance-level weights are generated for the target data based on an unknown separation module utilizing adversarial training, facilitating the adaptation to unknown scenarios and alleviating their adverse impacts on feature alignment. Experiments conducted on two bearing datasets illustrate the effectiveness and practicality of the proposed scheme.

Real-time safety assessment (RTSA) of dynamic systems holds substantial implications across diverse fields, including industrial and electronic applications. However, the complexity and rapid flow nature of data streams, coupled with the expensive label cost and pose significant challenges. To address these issues, a novel confusion-based learning framework, termed confusion-and-detection method plus (CADM+), is proposed in this article. When drift occurs, the model is updated with uncertain samples, which may cause confusion between existing and new concepts, resulting in performance differences. The cosine similarity is used to measure the degree of such conceptual confusion in the model. Furthermore, the change of standard deviation within a fixed-size cosine similarity window is introduced as an indicator for drift detection. Theoretical demonstrations show the asymptotic increase of cosine similarity. In addition, the approximate independence of the change in standard deviation with the number of trained samples is indicated. Finally, the extreme value theory (EVT) is applied to determine the threshold of judging drifts. Several experiments are conducted to verify its effectiveness. Experimental results prove that the proposed framework is more suitable for RTSA tasks compared with state-of-the-art algorithms. The source code is available at https://github.com/THUFDD/CADM-plus.

In response to the increasing complexity of tasks in artificial intelligence, broad learning systems (BLSs) have emerged as essential tools, especially given the limitations of deep neural networks, such as their extensive training and computational demands. This study addresses the computational inefficiencies and numerical instabilities inherent in traditional BLS when handling complex tasks in dynamic environments. To mitigate these challenges, we propose an enhanced version of BLS incorporating QR factorization (QRF), referred to as QRBLS, which is known for improving numerical stability. This framework replaces the traditional method of computing output weights, which typically relies on the Moore-Penrose pseudoinverse. The primary contribution of this article is the integration of QRF into the BLS architecture, thereby improving stability when processing large-scale datasets. QRBLS also features a dynamic updating mechanism that adjusts model parameters efficiently with new data, enabling continuous learning without the need for full-model re-evaluation. In addition, a time-dependent structure (TDS) enhances the model's responsiveness to temporal data changes, increasing its utility in dynamic environments. Validation through numerical experiments demonstrated that QRBLS outperformed traditional BLS, exhibiting superior stability and adaptability in handling data anomalies and rapid updates. The integration of QRF and TDS significantly improves the adaptability and computational efficiency of BLS, providing a robust solution for large scale and dynamic AI applications. QRBLS effectively addresses challenges related to numerical instability and continuous learning, offering practical improvements in real-world settings.

Incipient faults would evolve into permanent ones that lead to serious consequences without in-time detection and proper handling. Closed-loop control further complicates the task as it tends to compensate for incipient faults and further weaken their impact. Thus, in this article, a closed-loop process monitoring scheme for incipient faults is proposed. Inspired by the analysis of the fault impact in closed-loop process, correlation-based features for the closed-loop dynamic latent variable model is proposed for enhancing performance for detecting incipient faults. The features simultaneously shrink the nominal data variations and highlight the fault impact. A process monitoring strategy for incipient fault is, thus, proposed with the feature, and two case studies on the CSTR and the thruster system of the “Jiaolong” deep-sea submersible are carried on. The results show that the proposed method could detect incipient faults in closed-loop processes efficiently and prevent them from evolving to severe permanent faults, which helps ensure personnel safety and improve the maintainable level of the processes.

This article proposes an intermittent optimal learning control strategy for nonlinear discrete-time systems under time-varying pass lengths and actuator faults. The target of the problem is to minimize the timewise tracking error and the input drifts, which are combined by a time-iteration-dependent factor. By searching the nearest available pass at each time instant for the current iteration, the optimal control gain can be obtained. Theoretical analysis indicates that the tracking error converges asymptotically in spite of the actuator fault and the robustness against the shifted initial state is further proven. Numerical simulations illustrate the effectiveness and robustness of the presented method.

In the field of fault detection, active fault detection for dynamic systems is an emerging research topic. By redesigning the control input, active fault detection can enhance fault detection performance. In this study, the online active fault detection problem is investigated for over-actuated systems, where the control input is designed by considering both the prescribed control performance and the fault detection performance. A two-stage input design architecture is constructed, where the first stage is a virtual controller design and the second stage is a control allocation design. In the first stage, the virtual control input is designed by utilizing a prescribed performance function to fulfill the control requirement. In the second stage, a differential evolution method is adopted to obtain an optimal control allocation algorithm for improving the fault detection performance. The designed control input can both improve fault detection performance and achieve the prescribed control performance. Two cases, including a numerical example and a manned submersible propulsion system, are studied to support theoretical results. Note to Practitioners—In this study, the aim is to design a control input for over-actuated systems to obtain better fault detection performance while achieving prescribed control performance. To achieve it, a two-stage control input design architecture is constructed, where the first stage is a virtual controller design and the second stage is a control allocation design. In the first stage, choose a proper prescribed performance function and a sliding mode function to accomplish the transient and steady behavioral bounds on the tracking errors. In the second stage, design a special observer to generate the system residual signal, which is used for indicating system faults. Based on the residual signal, construct an index for the control allocation algorithm. Differential evolution algorithm is applied to obtain the optimal control allocation algorithm by maximizing the index. Thus the control allocation algorithm which can improve fault detection performance is designed. In this way, the input can improve fault detection performance without affecting system behavior. The proposed technique of this study can be applied to dynamic systems with input redundancy.

This paper discusses the problem of fault-tolerant tracking control for discrete-time nonstrict-feedback nonlinear systems in the presence of stochastic noises and actuator faults. The system is characterized by a discrete-time nonstrict-feedback structure and multiplicative stochastic noise related to the states, which poses a challenge for the design and analysis of the fault-tolerant controller. Moreover, the considered actuator faults include multiplicative and additive faults without prior information. By integrating the properties of the backstepping framework and neural networks, and by introducing an adaptive fault compensation term, a novel fault-tolerant tracking control strategy is proposed. The effects of faults are compensated and the difficulties caused by the system structure are overcome, avoiding the algebraic loop problem and overcoming the causal contradiction. Given specific parameters, the designed fault-tolerant controller can ensure that the tracking error converges to an adjustable region regarding the origin and that all system signals are uniformly bounded concerning the mean-square sense. The simulation examples illustrate the effectiveness of the designed fault-tolerant control method.Note to Practitioners—As system complexity has been increasing, practical systems are often affected by faults when performing tracking control. Fault-tolerant control can provide acceptable robustness and improve system safety. On the one hand, many practical systems are nonlinear, difficult to model mathematically and accurately, and affected by stochastic noises. On the other hand, with digital control, the object is a discrete-time system. In practical applications, the upper bounds of the faults and the noises are often unknown. In this paper, a novel adaptive fault-tolerant control method is proposed for the more general uncertain discrete-time nonlinear systems, i.e., nonstrict-feedback nonlinear systems, with stochastic noises and actuator faults. Neural networks and an adaptive fault compensation term are integrated into the fault-tolerant controller. By adjusting the controller parameters properly, it is ensured that the tracking error converges to a small neighborhood of zero in the event of a fault occurring. In future research, sensor faults, input constraints, optimal control design, and applications in multi-agent systems will be considered.

Predefined-time control has made great progress in recent years, but its current techniques have limitations in their applicability to systems experiencing mismatched disturbances, and has yet to resolve some issues regarding fault-tolerant control (FTC). This paper is concerned with a practical predefined-time fault-tolerant tracking control problem for high-order strict-feedback nonlinear systems subject to actuator faults and unknown mismatched disturbances. In this study, the exact information of actuator faults and the bound of the disturbances are unknown. By applying a predefined-time stability theory, a novel backstepping-based predefined-time fault-tolerant tracking control approach is developed. Incremental vectors about disturbances are constructed, and adaptive laws are designed to compensate for the effects of the disturbances and faults. To further handle the problem of the computational explosion, a novel command-filtered FTC scheme of predefined-time tracking is employed to decrease the calculation burden. With the proposed fault-tolerant controllers, all signals in the closed-loop system are bounded, and the output tracking error is guaranteed to converge into a user-defined set within a predefined interval. Simulation studies are carried out to demonstrate the performance of the designed controllers.Note to Practitioners—Numerous practical systems, such as manipulator arms, circuit systems, and electromechanical systems, among others, can be modeled as strict-feedback nonlinear systems. These systems are vulnerable to external disturbances and actuator faults during operation. To maintain system efficiency after a fault occurrence or to fulfill specific task requirements, tracking control must satisfy specific constraints on time response. In this paper, a practical predefined-time FTC method is proposed for strict-feedback nonlinear systems with actuator faults and mismatched disturbances. Furthermore, a predefined-time FTC scheme with a low computational effort is developed, specifically tailored for high-order systems. The designed controllers are suitable for practical systems that can be modeled in strict-feedback form with known dynamics. For more complex nonlinear systems with unknown dynamics and strong nonlinearity, the approaches proposed in this paper may no longer be suitable. Future research will focus on designing and applying predefined-time fault-tolerant controllers for specific complex practical systems, taking into account their characteristics and uncertainties.

Diagnosis of compound faults is meaningful and challenging in actual industrial applications. Generally, it is impractical to obtain a sufficient amount of labeled data for the compound faults, which limits the performance of existing methods. The characteristics of compound faults should be fully considered. Hence, reasonably introducing prior knowledge of fault information is of great significance to improve the practicability of diagnostic methods. In this paper, several unsupervised representation extraction techniques are firstly exploited to extract the fault information in the latent space, which is beneficial to alleviate the negative effects of noise disturbance in real-life scenarios. A discrimination-guided active learning method based on marginal representations, termed DGMR, is then proposed. In this case, samples with compound fault information are more likely to be iteratively queried for expert annotation. Several experiments are conducted using realistic experimental platform data. The experimental results show that the proposed DGMR can achieve high diagnostic accuracy for industrial compound fault diagnosis with a small number of annotated samples and shallow classifiers.Note to Practitioners—Compound faults widely exist in complex industrial equipment due to device coupling, which tends to be more diverse and generally exhibits more complex characteristics. In addition, even if the device passes the consistency test, the distribution of data collected by the test device before and after long-term use may have certain deviations due to performance degradation and other factors. Moreover, expert annotation is necessary but cost-sensitive. With the proposed scheme in this paper, vibration signals can be used to construct a fault database through several feature extraction techniques. Compound and unknown fault samples are more likely to be selected. Engineers can provide labels for these small number of fault samples based on methods such as frequency domain analysis, which can reduce the cost of annotations. High diagnostic accuracy can be obtained only using shallow classifiers in this case. Several experiments of a real rotating machinery fault diagnosis test rig are carried out. Experimental results demonstrate that the proposed method outperforms some advanced methods.

In order to ensure the safe and reliable operation of the energy system, real-time fault diagnosis technology is indispensable. Energy systems are typically complex systems consisting of multiple subsystems that are coupled with each other. Before and after the occurrence of a fault, the system is generally in an abnormal or even harsh environment, which may cause a large number of randomly missing measurement data and make the application of fault diagnosis technology extremely difficult. In this paper, the graph attention network (GAT) is improved by a Gaussian mixture model (GMM) for incomplete-data representation. The iteratively updated expectation of the GMM serves as the characterization of missing data, which significantly improves the ability to fill in missing data. The GAT fuses multi-source data according to the topology structure so as to comprehensively exploit the spatial information. The gated recurrent units (GRU) extract dynamic fault information from embedded spatial features and classify the time series into various fault types. Moreover, we propose a loss function in the form of weighted focal loss so that the fault-class imbalance issue brought by the data deficiency can be solved. The proposed uniform spatial-temporal graph neural network classification framework together with the GMM (GM-STGNN) can effectively improve fault diagnosis performance and is applied on an experimental platform of an authentic industrial estate. Results of comparative experiments under different conditions of both sufficient and deficient data illustrate the efficiency and advancement of the proposed method.Note to Practitioners—This paper presents a fault diagnosis method for large-scale energy systems with massive missing data. The proposed GM-STGNN framework can be applied in complex energy networks consisting of coupling subsystems, such as power grids, heating networks, and gas networks. With an incomplete-data representation mechanism, the proposed method utilizes topology information to comprehensively exploit spatial features, it also recurrently transmits historical embedded features and extracts dynamic fault characteristics. Therefore, it can effectively improve energy-network fault identification accuracy when more than half of the sample exists vacant values randomly. In the training procedure, after pre-setting the model scale, data acquired by multi-source sensors is put into the model according to the real topology structure, and corresponding fault labels serve as the supervision. The statistical characteristics of missing data are learned with neural-network parameters until the loss converges. In practical application, the sampling data is divided by a time window of a few seconds. The missing data is mitigated by the estimated expectation of the GMM. Therefore, real-time fault classification results can be obtained with high accuracy. The effectiveness of the proposed method is illustrated by fault diagnosis of a typical distributed heating network under the noise influence. Benefiting from the ability to learn fault knowledge, the proposed method can be easily applied to new scenarios where the process data and topology structure of the system are known.

Active fault diagnosis (AFD) techniques can improve fault diagnosis performance by designing a set of appropriate auxiliary inputs and injecting them into the system to stimulate fault characteristics. The AFD problem for uncertain linear parameter-varying (LPV) systems with bounded external disturbances is studied based on a set-membership approach in this paper. Based on zonotopes, a set-membership observer is designed to estimate system states to reduce the influence of external disturbances, which aims to reduce conservatism. A$F_{W}$-radius-based criterion is minimized to get the optimal observer gain matrix. Because of the system uncertainties, the generator matrices of the output sets will have elements associated with the auxiliary input. A method is proposed to eliminate the relationship between the auxiliary input and the generator matrices, and a mixed-integer quadratic program (MIQP) is constructed to get the auxiliary input. By solving the optimization problem, the auxiliary input is designed for the considered finite kinds of faults to achieve fault diagnosis. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed approach.Note to Practitioners—This paper studies the AFD problem for uncertain LPV systems. Most existing AFD methods are proposed for linear time-invariant systems. The uncertainty of the LPV systems makes the existing AFD methods no longer applicable. In addition, most of the existing AFD methods for uncertain LPV systems are based on the following framework: the auxiliary input is designed at the initial time and injected directly into the system. This framework does not adjust the auxiliary input according to the real-time output of the system during the diagnosis process, which leads to the conservatism of the method. To handle these challenges, this paper proposes an AFD method based on a set-membership approach. A set-membership observer is designed to estimate the system state set based on the system output information. Then the auxiliary input is recalculated according to the estimated system state by solving a MIQP problem at each step. Simulation results suggest that the proposed method is feasible, but it has a large computational burden when the system is complex. Our future work is mainly to reduce the computational complexity and the impact of auxiliary inputs on system performance.

This article presents a novel self-healing fault accommodation framework for high-order fully actuated systems (HOFASs) with sensor faults. Starting from the HOFAS model with nonlinear measurements, a q -redundant observation proposition is derived from an observability normal form based on each individual measurement. On the heels of the ultimately uniformly bounded error dynamics, a definition of sensor fault accommodation is determined. After a necessary and sufficient accommodation condition is highlighted, a self-healing fault-tolerant control strategy is proposed, which can be applied in steady-state processes or transient processes. The main results are proved theoretically and illustrated experimentally.

In this article, a finite-time fault-tolerant controller based on the fully actuated system (FAS) theory is presented to realize system stabilization and trajectory tracking. Paralleling to first-order nonlinear state space theory, the high-order FAS (HOFAS) theory contains rich controller design approaches. The existing FAS approaches can only give general global asymptotic stability results. In order to enhance the applicability of FAS approaches in fast control systems, a parameterized FAS stabilization controller based on the homogeneity principle is established for global finite-time stability. Moreover, a finite-time FAS tracking controller based on a finite-time observer is proposed for a HOFAS model with process faults. The proposed observer can yield zero-value convergence of state estimation error and fault estimation error in a finite time, and the proposed fault-tolerant controller can yield zero-value convergence of tracking error in a finite time. The main results are proved theoretically and illustrated experimentally.

Active fault detection (AFD) is the newest frontier in the field of fault detection and has drawn increasing amounts of research attention. AFD technology can enhance fault detection performance by injecting a predesigned auxiliary input signal for a specific fault. In most existing studies, system control objectives are not fully considered in the auxiliary input design of AFD. This article investigates a new reconciliatory input design problem for both achieving control objectives and improving fault detection performance. An exemplary algorithm for the reconciliatory input design is proposed, by using a trajectory optimization approach. The proposed algorithm consists of three parts: 1) residual generation; 2) trajectory optimization; and 3) input design. A state observer is designed to obtain residual signals used as fault indicators. Considering the optimization index composed of the fault indicators, a trajectory optimization technique is carried out to find an optimal system trajectory which can improve the fault detection ability to the greatest extent. The control input is designed to track this optimal trajectory while complying with system physical constraints. In order to demonstrate the effectiveness of the proposed methodology, simulation cases on an underwater manipulator are conducted.

Chunk-level real-time safety assessment of dynamic systems is a critical component of industrial processes, which is essential to prevent hazards and reduce the risk of injury or damage to equipment and facilities, especially in nonstationary environments. In this context, multiple real and complex concept drifts are inevitable in industrial settings, making it crucial to understand their detection and adaptation processes. The incremental learning scheme should also be well considered. However, existing methods have certain limitations in dealing with such issues. In this article, a dynamic model interpretation-guided online active learning scheme, termed a dynamic model interpretation-guided learning scheme (DMI-LS), is proposed. Specifically, the model update strategy with chunk data is designed based on the implementation of the broad learning system. A novel query strategy is then investigated to consider the ranking preference difference, which relies on the interpretation generated by the explainable artificial intelligence method. Several experiments based on the JiaoLong deep-sea manned submersible data are conducted to verify the effects of the proposed DMI-LS. The results show that it outperforms the other advanced existing approaches with different settings in most scenarios.

For active fault-tolerant control (AFTC) of nonlinear systems, inaccurate fault estimation caused by mismatched disturbances can result in compensation errors in the fault-tolerant controllers, making it difficult to achieve asymptotic tracking. This article proposes a novel AFTC strategy featuring an adaptive mechanism for compensating estimation errors for a class of strict-feedback nonlinear systems subject to mismatched disturbances. The strategy introduces an estimator, decoupled from the controller, to perform fault detection, fault estimation, and state estimation using measured output. By treating estimation errors and system disturbances as new process disturbances, adaptive compensation terms are constructed, while an active fault-tolerant controller is designed using the command-filtered backstepping method, ensuring the asymptotic convergence of the tracking errors. A prominent benefit of this strategy is that only bounded estimation needs to be implemented, thus weakening the bidirectional influence between the observer and the controller, and relaxing the accurate estimation demands often associated with existing AFTC methods. The effectiveness and suitability of the proposed AFTC strategy are demonstrated through a simulation example involving an underwater manipulator.

Operational changes in industrial production can alter system operating modes, which complicates real-time fault diagnosis by affecting sensor data and fault characteristics. In addition, fault diagnosis tasks encounter the challenge of fault feature drift, which causes a decline in the performance of previously trained models on new data. This article presents a novel approach for real-time multimode fault diagnosis called the evidential ensemble preference-guided approach to tackle these issues. During the offline stage, we extract ensemble preferences of fault information across different operating modes based on the structure of the broad learning system. Subsequently, a parameter iterative update rule is developed that utilizes an evidential reasoning technique to emphasize the preferences during the online stage. The effectiveness of our approach is evaluated by constructing a real-time multimode fault diagnosis dataset using the Tennessee Eastman process and conducting multiple experiments. The results demonstrate that our proposed approach effectively identifies operating modes and diagnoses faults simultaneously, surpassing existing advanced methods.

In the operation process of the rotating machinery, compound faults have various combination forms and are difficult to reproduce, which results in the scarcity of training samples of various compound faults. In this article, a method of partial-label learning and classification fusion is proposed to investigate the above problems. Wavelet packet transformation and dimensionless parameterization are utilized to extract features in high-dimensional space. A label-specific feature learning architecture is proposed to make up for the shortage of handcrafted features. An improved focal loss is exploited for learning prominent compound-fault characteristics and imbalanced fault samples. Basic probability assignments about fault classes are generated from each partial classifier and combined to obtain a final inference about fault types. Experimental results of single-fault and compound-fault classification illustrate the effectiveness of the proposed method.

The feature of multienergy flows of the integrated energy system (IES) causes the relationship among different types of subsystems to be complex. In order to handle the compound-fault diagnosis problem of the IES with small sample sizes of compound faults, in this article, a novel multiscale spatial-temporal graph neural network (MSSTGN) is proposed for fault detection and compound-fault identification. The label-specific fault features are learned by multiscale graph operators and gated recurrent units in the spatial and temporal domains, respectively. The deficiency of limited compound fault samples is mitigated by fusing partial inferences by base MSSTGN classifiers trained for paired faults. Constant data features of each fault class are enhanced by the proposed loss functions with a center loss. The advantages of the proposed method are illustrated by comparative experiments exploiting the process data from an IES under multiple situations of missing data and noise influence.

Real-time safety assessment of dynamic systems is of paramount importance in industrial processes since it provides continuous monitoring and evaluation to prevent potential harm to the environment and individuals. However, there are still several challenges to be resolved due to the requirements of time consumption and the non-stationary nature of real-world environments. In this paper, a novel online dynamic hybrid broad learning system, termed ODH-BLS, is proposed to more fully utilize the co-design advantages of active adaptation and passive adaptation. It makes effective use of limited annotations with the proposed sample value function. Simultaneously, anchor points can be dynamically adjusted to accommodate changes of the underlying distribution, thereby leveraging the value of unlabeled samples. An iterative update rule is also derived to ensure adaptation of the assessment model to real-time data at low computational costs. We also provide theoretical analyses to illustrate its practicality. Several experiments regarding the JiaoLong deep-sea manned submersible are carried out. The results demonstrate that the proposed ODH-BLS method achieves a performance improvement of approximately 8% over the baseline method on the benchmark dataset, showing its effectiveness in solving real-time safety assessment tasks for dynamic systems.

The design of real-time safety assessment (RTSA) approaches in nonstationary environments is meaningful to reduce the possibility of significant losses. However, several challenging problems are needed to be well considered. The performance of existing approaches will be negatively affected in the settings of imbalanced drifting streams. In this case, the model design with the incremental update should also be explored. Furthermore, the query strategy should also be well-designed. This article investigates a dynamic submodular-based learning strategy to address such issues. Specifically, an efficient incremental update procedure is designed with the structure of the broad learning system (BLS), which is beneficial to the detection of concept drift. Furthermore, a novel dynamic submodular-based annotation with an activation interval strategy is proposed to select valuable samples in imbalanced drifting streams. The lower bound of annotation value is also proven theoretically with a novel drift adaption mechanism. Numerous experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. The experimental results show that the proposed approach can achieve better assessment accuracy than typical existing approaches.

The degradation of a system's performance poses a significant challenge to the effective application of fault diagnosis methods for dynamic systems. Consequently, the underlying feature distribution changes over time during the actual process, resulting in a decline in the effectiveness of existing diagnosis methods. In this article, we present a real-time adaptive fault diagnosis scheme to address this issue. A latent variable-guided broad learning system (LVGBLS) is proposed to construct the fundamental diagnosis model, which effectively extracts dynamic features from the monitored data. An incremental update procedure is then designed based on pseudolabel learning to adapt to dynamic process changes while minimizing labeling costs. We also introduce the condition detection mechanism (CDM) to detect dynamic changes under the degradation process based on statistical information. To demonstrate the effectiveness of our proposed method, we conduct several comparison experiments and ablation experiments on electrical drive systems and XJTU-SY bearing datasets. The results show that our proposed scheme exhibits superior performance with low labeling costs in most scenarios with performance degradation.

The open environment presents a challenging issue for the online safety assessment of dynamic systems, which means that unknown scenarios may arise unexpectedly. These unknown scenarios can be task-related and result in the within-class distribution mismatch. Addressing this challenge in the semi-supervised safety assessment task, where unlabeled training data contain task-related unknown scenarios, has not been explored. This article investigates this new semi-supervised safety assessment problem. A novel active-labeling-aided semi-supervised learning scheme is proposed to tackle the within-class distribution mismatch between labeled and unlabeled training data. The proposed scheme begins by detecting out-of-distribution unlabeled data through the construction of a deep support vector data description network for each class. Subsequently, an active labeling approach along with its kernel extension is introduced, taking into account both distribution mismatch degree and sample representativeness. The proposed active labeling approach can be seamlessly integrated with any semi-supervised learning algorithm to enhance its performance in handling task-related unknown scenarios. The effectiveness and applicability of the proposed method are demonstrated through case studies based on a bearing dataset and operation data from an actual deep-sea manned submersible.

The degradation starting time is an important variable affecting the accuracy of degradation path prediction, but little work has been considered in existing studies. This article investigates the problem of predicting the performance of rolling element bearings based on early degradation analysis. Based on an improved dual linear structural support vector machine with envelope spectrum algorithm and$\mu +4\sigma$criteria, a new health indicator is proposed to detect the degradation starting time. As well the detected time is sensitive to early anomalies. In addition, according to the degradation starting time, a convolutional neural network prediction model is established to predict the degradation path. Experiments show the effectiveness and superiority of the proposed method.

In this article, the nonaffine high-order fully actuated system (HOFAS) structure is established, and a tracking controller and a robust fault-tolerant stabilization controller for unknown fully actuated systems are proposed. Starting from the unknown nonaffine HOFAS model, a saturated controller dynamic equation based on extended state observer is yielded, which ensures the low-power characteristics of the controller. Both the observation error and tracking error are shown to converge eventually, and the upper error bound can be adjusted to a small neighborhood near zero. Furthermore, for unknown nonaffine HOFASs with multiplicative actuator and sensor faults, a robust fault-tolerant stabilization controller is presented to guarantee the ultimately uniformly bounded stability and the convergence to zero. The main results are proved theoretically and illustrated experimentally.

Intermittent faults (IFs) are characterized by random occurrence and disappearance, making fault diagnosis and fault-tolerant control (FTC) more challenging compared to permanent faults. This article is devoted to active FTC (AFTC) for full-state-constrained nonlinear systems with unknown mismatched disturbances and actuator IFs. By integrating fault diagnosis and controller design, a novel fault-diagnosis-based FTC strategy is proposed to compensate for the IF, and a sufficient condition is established for the fault detectability of the proposed scheme. Besides, the fault detection delay and estimation error, considered new external disturbances, are compensated adaptively along with the mismatched disturbances. The fault-tolerant controller is designed to guarantee that all signals in the system are bounded and the tracking error converges into a small neighborhood near the origin in finite time, without violating the predefined asymmetric time-varying constraints of the states. A numerical simulation example and an application to the active steering system (ASS) of vehicle chassis are provided to demonstrate the effectiveness of the developed AFTC strategy.

Fault-tolerant control (FTC) may conceal fault symptoms, thereby increasing the difficulty of fault diagnosis (FD). In this article, a novel framework for the cooperative design of active FD and FTC is proposed to optimize FD performance while maintaining fault-tolerance performance. The proposed framework consists of four steps: 1) controller design; 2) residual generation; 3) performance evaluation; and 4) gain tuning. First, a controller with undetermined gains is constructed, and a fault detection observer is designed to generate residuals that can indicate the fault. Then, the performance of fault detection is evaluated. Finally, suboptimal controller gains are obtained by solving an optimization problem. Within the framework of the collaborative design, the occurring faults can be detected faster and more accurately, and the performance of FTC can be guaranteed at the same time. A simulation study is provided to demonstrate the effectiveness of the developed framework.

This article investigates the distributed active fault-tolerant cooperative control problem for leader-follower multiagent systems (MASs) in the presence of multiple faults, communication delays, and external disturbances. A new distributed consensus protocol is put forward to ensure the state consensus of MASs, which can be served as a nominal controller in fault-free cases with communication delays and external disturbances. A novel distributed time-delay intermediate observer, which can estimate system states and multiple faults simultaneously, is derived based on the time-delay closed-loop system equation. By integrating a fault compensation mechanism into the nominal controller, a distributed active fault-tolerant consensus controller is constructed for the follower agents to eliminate the adverse effects of multiple faults. Simulation examples are provided to demonstrate the effectiveness of the proposed method.

In this article, the active fault diagnosis (AFD) problem is investigated for a class of discrete-time stochastic systems. We consider multiple fault modes of the system, and the different types of misdiagnoses of these faults lead to different losses. A novel AFD framework is developed based on the Bayesian minimum risk decision framework. The misdiagnosis risk is derived to characterize the total losses caused by the misdiagnoses of all considered faults. The input is specifically designed to improve diagnosis performance by minimizing the risk of misdiagnosis. Since it is infeasible to express the misdiagnosis risk in the closed form, we deduce an upper bound on it to establish a novel input design criterion based on the Bhattacharyya distance. The optimal input signal can be obtained for AFD via concave quadratic programming. For diagnosis performance enhancement and computation simplification purposes, we propose two new semi-online implementation manners of the input design for stochastic systems with and without multi-fault modes switching, respectively. Given the optimal input signal and the proposed implementation manner, the faults are diagnosed based on the minimum misdiagnosis risk decision principle. The effectiveness and superiority of the proposed AFD method are demonstrated by simulation results on a four-tank system.

In this article, the consensus control problem is investigated for mobile train lifting jack systems (TLJSs) of electric multiple units in the context of distributed industrial systems. First, a kind of global consensus controller is dedicatedly designed to deal with the underlying actuator limitation that is a typical phenomenon in mobile TLJSs and is referred to as actuator saturation. In order to better cater for the impact from the TLJSs, we consider the asymmetric actuator saturations (rather than their symmetric counterparts) whose side effects are later attenuated by means of a novel Lyapunov-function-based method. A series of experiments are conducted on mobile TLJSs so as to illustrate the effectiveness of the proposed consensus control algorithm.

This article is concerned with the data-driven health state assessment task with unknown scenarios. Unknown scenarios are inaccessible in the stage of model training but they can appear unexpectedly during the running stage. A new problem called the within-class distribution mismatch is raised by assuming that unknown scenarios still belong to known classes. To tackle this challenging problem, a novel active incremental learning scheme with a classifier and an out-of-distribution (OOD) detector is proposed. Ak-fast incremental support vector data description (k-FISVDD) model is put forward as the OOD detector to recognize distribution mismatch samples online for label annotation. Specifically, it integrates the clustering algorithm to build local support vector sets, based on which an active query strategy is developed. An incremental learning mechanism is also designed to reduce the labeling cost. Then, the new labeled data can simultaneously refine the classifier and OOD detector. Two cases, including a bearing benchmark dataset and the operation data of a practical deep-sea manned submersible, are studied to demonstrate the effectiveness of the proposed scheme.

Real-time safety assessment of dynamic systems has recently received increasing attention. However, the performance of existing advanced approaches is often negatively affected by realistic requirements such as limited annotations and memory. In this case, how to design reasonable query strategies to select valuable instances and exploit the memory space efficiently is extremely meaningful. This paper proposes a novel memory-triggered submodularity-guided active broad learning approach, termed MTSGABL, to deal with such issues simultaneously. Specifically, the broad learning system is introduced as the basic assessment model to update incrementally. A memory-triggered learning mechanism is then proposed based on the drift detection procedure, which controls the update process to exploit the latest sequential information. Furthermore, a submodularity-guided query strategy is introduced to select a small number of valuable samples sequentially, which is beneficial to alleviate the negative effects of the imbalanced data stream. Numerous comparison and ablation experiments with the realistic JiaoLong deep-sea manned submersible data are conducted to validate its effectiveness. Results show that the proposed approach is superior to the existing typical approaches subject to these constraints.

This paper investigates the problem of path following with prescribed performance for autonomous underwater vehicles subjects to unknown actuator dead-zone nonlinearity. To cope with this practical problem abstracted from hydrodynamic noise measurement, an adaptive command filtered backstepping method with actuator dead-zone compensation is proposed. By introducing a damped exponential barrier functions, new tracking errors are defined to satisfy the prescribed performance requirements. The path following control method is theoretically based on the command filtered backstepping technique for handling the complexity explosion problem attribute to the repeating derivations. Moreover, the filter compensation mechanism is designed to eliminate the negative effect of the filter errors. To deal with the actuator dead-zone nonlinearity, a fuzzy logic system based dead-zone compensation method is developed that dose not need the inverse of the dead-zones. Numerical simulations are conducted to demonstrate the theoretical analysis, and the usefulness and potential of the new design scheme is revealed.

Real-time safety assessment of the complex dynamic systems in nonstationary environments is of great significance for avoiding the potential hazards. In this case, the update procedure with high assessment accuracy and training speed is crucial and meaningful in the dynamic streaming setting. Generally, the performance of most online learning approaches will be negatively affected by limited annotated samples in such a setting. Moreover, the time cost of advanced conventional methods with retaining procedures is relatively high, constraining their practicality. In this article, a novel online active broad learning approach, termed OABL, is proposed. In detail, the effectiveness of the broad learning system in the framework of online active learning is first revealed and verified. A reasonable dynamic asymmetric query strategy is then designed with a limited annotation budget to actively annotate the relatively valuable samples, which is beneficial to mitigating the negative effects of class imbalance. In this context, the advantage of the human-in-the-loop characteristic is also effectively used to control the evolution direction of the learner during the incremental update, which makes it better able to adapt to complex and nonstationary environments. Several related experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. Results show the effectiveness and practicality of the proposal compared with the existing advanced approaches.

This article presents a novel observer-based fault-tolerant controller framework for a class of nonideal time-varying high-order fully actuated systems (HOFASs). The HOFAS theory is an emerging nonlinear dynamical system theory, which can yield global stability. In order to improve the ability of HOFAS theory to handle parameter uncertainties, actuator faults, and measurement noises, a nonlinear extended state observer and low-power fully actuated controller framework is established in this article. Particularly, the linear framework conforms to the generalized separation principle and highlights the advantages of HOFAS parametric design. Moreover, the work takes into account fault tolerance and noise suppression in engineering applications, reducing the dependence of HOFAS theory on model accuracy. The uniformly bounded stability and noise suppression performance are also proved theoretically and illustrated experimentally.

This article investigates the problem of fault-tolerant control (FTC) for nonstrict-feedback (NSF) stochastic nonlinear systems subject to actuator faults and output constraints. Most of the existing backstepping-based FTC techniques have been developed for strict-feedback systems, which may cause algebraic loop problems when applied directly to NSF systems. In this work, unknown nonlinear functions are approximated by neural networks (NNs). Based on the properties of NN basis functions and a barrier Lyapunov function, a novel adaptive FTC strategy for an NSF system is proposed to achieve tracking control without violating the output constraint. Adaptive fault accommodation terms are constructed to compensate for the faults without a priori information to achieve online fault tolerance. The designed controller guarantees the fault-tolerant tracking performance while ensuring that all signals are bounded in probability and that the output is within the specified constraint. The performance of the proposed FTC strategy is illustrated by simulation studies.

This article addresses the adaptive fault-tolerant tracking control (FTTC) problem for a family of strict-feedback uncertain nonlinear systems subject to limited sensor resolution and unknown control directions. Both partial loss-of-effectiveness (LOE) and lock-in-place (LIP) faults of actuators are investigated. An adaptive control strategy based on a Nussbaum-type function and neural networks is presented by introducing a backstepping approach to make the system output track a desired reference output signal with bounded tracking error in the case of faulty actuators. The effect of the limited resolution is decoupled from the nonlinear system and is approximated using a neural network. The impact of disturbances is effectively compensated by utilizing adaptive parameter estimation terms in the backstepping procedure. It is proven that the proposed FTTC strategy can ensure the boundedness of all signals and guarantee that the output tracking error can converge into a small neighborhood of the origin. Two simulation examples are given to illustrate the effectiveness of the proposed FTTC strategy.

This article investigates the command filter-based decentralized prescribed performance adaptive fault-tolerant compensation control strategy for uncertain nonlinear large-scale systems subject to asymmetric time-varying full-state constraints. Via integrating the performance function with command filter-based backstepping technique, the prescribed performance control problem is addressed, under which the complexity of controller design is reduced. Under the prescribed performance control framework, the nonlinear transformed function is constructed so as to ensure that the asymmetric time-varying full-state constraints free from feasibility conditions imposed on virtual control signals are not violated. Besides, the effect of infinite number of time-varying actuator faults is compensated with the aid of projection adaption design. Furthermore, based on the piecewise Lyapunov function analysis, it is rigorously testified that entire involved signals are bounded, desired constraints are not breached and tracking errors within the predefined domains. Finally, the effective performances of the developed control algorithm are confirmed by some simulation results.

In this article, a frequency-domain approach is developed to deal with the global consensus problem for a class of general second-order multiagent systems (MASs) subject to actuator saturations. By employing the describing function and the generalized Nyquist criterion, the global consensus problem is thoroughly investigated for both undirected and directed topologies. First, the describing function is introduced to characterize the actuator saturations in the s -plane, and the inherent representation error is quantitatively analyzed from a frequency-domain perspective. Then, by means of the Kronecker product, the addressed consensus problem of the MAS is transformed into a corresponding stability analysis problem for a certain multi-input-multi-output (MIMO) system and, consequently, the generalized Nyquist criterion for MIMO systems is exploited to derive the condition for the global consensus of the MAS where the impact from the actuator saturation is explicitly reflected. Finally, numerical simulations are provided to illustrate the validity of the proposed theoretical result.

Wheelset intermittent over-creeps (WIOs), i.e., slips or slides, can decrease the overall traction and braking performance of Electric Multiple Units (EMUs). However, they are difficult to detect and isolate due to their small magnitude and short duration. This paper presents a new index called variable-to-minimum difference (VMD) and a novel technique called weighted moving average (WMA). Their combination, i.e., the WMA-VMD index, which uses correlation information to find an optimal weight vector (OWV) for the VMD indices within a time window, is employed to detect and isolate WIOs in real time. The uniqueness of the OWV is proven, and its properties such as the symmetrical structure are revealed. WIO detectability and isolability conditions of the WMA-VMD index are provided, leading to the property analyses of two nonlinear, discontinuous operators,$\min $and VMDi. Experimental studies are conducted based on practical running data and a hardware-in-the-loop platform of an EMU, which show the effectiveness of the developed method.

This article is concerned with the consensus control problem for a general class of linear multiagent systems (MASs) subject to actuator imperfection consisting of both actuator saturations and actuator faults. A novel two-step saturation-resistant approach is proposed to attenuate the side effects resulting from the actuator imperfection. In the first step of controller design, the state information received from the neighboring agents is used to design a consensus controller capable of tolerating the actuator fault. Then, in the second step of controller optimization, the domain of attraction (DOA) is introduced for MASs to evaluate the performance of the controller and, subsequently, optimize the controller parameters to enlarge the DOA in terms of solutions to a certain set of matrix inequalities. Finally, simulation examples are provided to demonstrate the effectiveness of the developed saturation-resistant approach.

Due to the inner deteriorating mechanism or the mutant environmental stress, the degradation systems with multi-phase features have frequently been encountered in engineering practice. The key issue for prognostics of such systems is to account for the impact of the changing-point variability and the associated degradation state at this point on the progression of the degradation process. However, current studies usually treat the degradation state at the change point as a fixed value rather a random variable. Thus, it is still challenging to predict the lifetime of such multi-phase degrading systems. To this end, we first formulate a general degradation modeling framework based on a two-phase Wiener process. In prognostics, we take into full account the uncertainty of the degradation state at the changing point and then derive the analytical expressions of the lifetime and remaining useful life under the concept of the first passage time. The derived results are distinguished from existing results limited to the fixed state at the changing point. Furthermore, we extend our approach and results to cases with unit-to-unit variability and multiple phases. To facilitate the model implementation, we propose both offline and online methods for parameter identification, which make full use of the historical data and the in-service data. Finally, a numerical simulation and a practical case study are provided for illustration.

In this paper, we present an extended Tobit Kalman filter that deals with fault detection problem in nonlinear systems with missing measurements and censored data. The missing measurements randomly occurring are regulated by individual random variables whose probability distributions are on the interval [0,1]. The censored data are characterized by the Tobit measurement model. The Tobit Kalman filter designed for linear systems with missing measurements is extended to nonlinear systems. The residual signals are generated by the extended Tobit Kalman filter and then evaluated to detect the occurrence of a fault. Finally, the feasibility of the proposed fault detection filter is illustrated by an example of leakage fault of a three-tank system.

The fault detection and estimation problems are investigated for a class of Markov jump linear systems (MJLS) with state delays based on the particle filter. For MJLS subjected to the actuator fault, we use particle filter to estimate the Markov parameter, then the states are estimated depended on the Kalman filter. According to the estimation result, a residual is designed by using the sliding-time window technique for the purpose of fault detection. Then the fault is estimated in the way of state augment method. A simulation experiment is designed to illustrate the effectiveness of the proposed method.

This paper investigates distributed fault detection of wireless sensor networks with a class of faulty sensor node, fault source isolation and networked communication topology accommodation design issue considering filtering performance. The wireless sensor network in this paper is composed of spatially placed sensors which has a changeable networked communication topology, different state-space representations and different sensor gain degradations. By augmenting the state components, a set of recursive matrix equation in Riccati form is derived to calculate the distributed filter parameters and generate the residual signals for fault detection. In order to eliminate the influence of possible faulty node on distributed filtering performance, the fault source isolation is carried on by removing the communication channel between every one sensor and all other sensors according to beat isolation. The communication network topology accommodation is put forward with a framework preliminarily in view of the networked fault, distributed filtering performance and communication energy cost.

For modern industrial processes, timely detection of incipient faults is of vital importance so as to ensure safe and optimal process operation. Though recently statistical process monitoring (SPM) has been extensively studied and widely applied in practice, conventional multivariate statistics are usually not sensitive to incipient faults. In this paper, a new multivariate statistical index called augmented Mahalanobis distance (AMD) is proposed for incipient fault detection. It can be concluded from fault detectability analysis that the AMD index is more sensitive to incipient faults than the conventional Mahalanobis distance (MD) index. Besides, the idea of augmentation utilized in the AMD index can also be applied to some other SPM models. Finally, case studies on a numerical example and the continuous stirred tank heater (CSTH) process are conducted to demonstrate the effectiveness of the proposed AMD index, in comparison with the MD index, as well as the squared prediction error (SPE) and T-square indices.

In this paper, we investigate the distributed filtering problem over wireless sensor networks (WSNs) with bandwidth and energy constraints. To utilize the limited resources efficiently, a novel event-based mechanism is proposed for the sensor node, such that only selected valuable data are broadcasted to its neighboring sensors via the wireless channel according to whether specific events happen. By resorting to graph theory and utilizing stochastic analysis methods, the filter parameters and the event triggering rules are designed, such that the filtering error converges at an exponential rate in the mean square sense. An adaptive algorithm for determining the triggering threshold is developed, which allows the intelligent sensors to tune the boundary of a local event domain in an online manner, so as to keep the average transmission rate level off a desired value. An illustrative example is given to demonstrate the effectiveness of the proposed strategy.

In this paper, the problems of fault detection, isolation, and estimation are considered for a class of discrete time-varying networked sensing systems with incomplete measurements. A unified measurement model is utilized to simultaneously characterize both the phenomena of multiple communication delays and data missing. A least-squares filter that minimizes the estimation variance is first designed for the addressed time-varying networked sensing systems, and then a novel residual matching (RM) approach is developed to isolate and estimate the fault once it is detected. The RM strategy is implemented via a series of Kalman filters, where each filter is designed to estimate the augmented signal composed of the system state and a specific fault signal. The design scheme for each filter is proposed in a recursive way. The main idea for the fault detection and estimation is that the Kalman filter with least residual value is regarded as corresponding to the right fault signal, and its estimation is utilized to represent the actual occurred fault. The effectiveness of our proposed method is demonstrated via simulation experiments on a real Internet-based three-tank system.

In this paper, we are concerned with the H∞model reduction problem for a class of nonlinear time-varying systems with parameter uncertainties and time delays in a discrete time framework. By introducing a proper Lyapunov function, we establish a sufficient condition ensuring the asymptotically mean square stability of the associate error system with a prescribed H∞error performance in the form of quasi-linear matrix inequality, based on which the parameters of the reduced-order model can also be determined. Finally, a numerical example is included to show the effectiveness of the developed technique.

In this paper, a new robustHinfinfiltering problem is investigated for a class of time-varying nonlinear system with norm-bounded parameter uncertainties, bounded state delay, sector-bounded nonlinearity and probabilistic sensor gain faults. The probabilistic sensor reductions are modeled by using a random variable that obeys a specific distribution in a known interval [alpha,beta], which accounts for the following two phenomenon: 1) signal stochastic attenuation in unreliable analog channel and 2) random sensor gain reduction in severe environment. The main task is to design a robustHinfinfilter such that, for all possible uncertain measurements, system parameter uncertainties, nonlinearity as well as time-varying delays, the filtering error dynamics is asymptotically mean-square stable with a prescribedHinfinperformance level. A sufficient condition for the existence of such a filter is presented in terms of the feasibility of a certain linear matrix inequality (LMI). A numerical example is introduced to illustrate the effectiveness and applicability of the proposed methodology.