Distributed Fault Detection Scheme Research Articles

A data‐driven distributed fault diagnosis scheme for large‐scale systems based on correlation analysis

AbstractThis paper studies data‐driven distributed fault diagnosis for large‐scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection performance by minimizing the impact of noise‐induced uncertainty. The core of the method is to implement the correlation of the coupled nodes to reduce the covariance of the residual signal in a distributed manner. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality. A case study on Tennessee Eastman process is given in the end to demonstrate the proposed approach.

The following article for this Special Issue was published in different Issue

International Journal of Robust and Nonlinear ControlVolume 33, Issue 7 RESEARCH ARTICLE The following article for this Special Issue was published in different Issue First published: 10 April 2023 https://doi.org/10.1002/rnc.6708AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Cheng C, Wang Q, Nikitin Y, Liu C, Zhou Y, Chen H. A data-driven distributed fault detection scheme based on subspace identification technique for dynamic systems. Int J Robust Nonlinear Control. 2023;33(5):3107-3128. doi: 10.1002/rnc.6554 https://onlinelibrary.wiley.com/doi/10.1002/rnc.6554 Volume33, Issue7Special Issue: Emerging Robust and Data‐driven Control Methods for Uncertain Learning Systems10 May 2023 RelatedInformation

A data‐driven distributed fault detection scheme based on subspace identification technique for dynamic systems

AbstractWith the aid of the subspace technique and the average consensus algorithm, the main objective of this article is to develop a data‐driven design of distributed fault detection for dynamic systems using the measurement in a complex sensor network. Specifically, the design process consists of two stages: distributed off‐line learning and distributed online fault detection. Among them, the distributed off‐line learning stage involves the average consensus algorithm and parameter identification by subspace technique. It is worth mentioning that, the distributed fault detection approach has the same performance as the centralized fault detection approach and avoids complex information exchange. In the end, a numerical simulation example and a case study of the three‐phase flow facility are illustrated to show that the proposed distributed approach can accomplish the fault detection task successfully.

A distributed fault detection scheme in disturbed heterogeneous networked systems

This paper deals with the problem of distributed fault detection and isolation in multi-agent systems with disturbed high-order dynamics subject to communication uncertainties and faults. Distributed finite-frequency mixed {mathcal {H}}_-/{mathcal {H}}_infty unknown input observers are designed to detect and distinguish actuator, sensor and communication faults. Furthermore, an agent is capable of detecting not only its own faults but also faults in its neighbouring agents. Sufficient conditions are then derived in terms of a set of linear matrix inequalities while adding additional design variables to reduce the conservatism. A numerical simulation is carried out in order to demonstrate the effectiveness of the proposed approach.

Distributed fault detection and isolation for uncertain linear discrete time-varying heterogeneous multi-agent systems

The distributed fault detection and isolation of linear discrete time-varying multi-agent systems subject to heterogeneous dynamics and norm bounded model uncertainties is investigated. By combining the model uncertainties and external disturbances into a new generalized disturbance, a distributed closed-loop residual generator is constructed based on the estimate of the generalized disturbance. Then, the concerned fault detection is transformed into an indefinite quadratic minimum problem by using the finite-horizon robust H∞ filtering method, for which necessary and sufficient minimum conditions are provided by the Krein-space theory. Afterwards, a computationally efficient recursive algorithm is further developed to determine the residual for individual agent. Based on the resulting residuals, the fault occurrence can be alerted by devising a novel distributed fault detection scheme, which is then applied for the fault isolation by some appropriate transformation of the output measurements. Finally, both numerical and practical simulations are proposed to verify the effectiveness of the proposed algorithm.

Distributed Fault Detection of Nonlinear Process Systems with Senor Failures

A distributed fault detection scheme is presented in this work to deal with the senor failures in a nonlinear process system. Firstly, a residual generator is derived, in which the fault signal is generated by introducing a residual signal. Then, a distributed extended Kalman Filter (EKF) is designed to estimate the unmeasurable system states. Finally, the proposed distributed EKF is used for the fault detection and isolation in a distributed framework. By applying the distributed fault detection scheme to a completely stirred tank reactor process, it is shown that the proposed scheme has ability to monitor the sensor faults automatically.

Distributed H_/L∞ fault detection observer design for linear systems

This paper studies the distributed fault detection problem for linear time-invariant (LTI) systems with distributed measurement output. A distributed H_/L∞ fault detection observer (DFDO) design method is proposed to detect actuator faults of the monitored system in the presence of a bounded process disturbances. The DFDO consists of a network of local fault detection observers, which communicate with their neighbors as prescribed by a given network graph. By using finite-frequency H_ performance, the residual in fault detection is sensitive to fault in the interested frequency-domain. The residual is robust against effects of the external process disturbance by L∞ analysis. A systematic algorithm for DFDO design is addressed and the residual thresholds are calculated in our distributed fault detection scheme. Finally, we use a numerical simulation to demonstrate the effectiveness of the proposed distributed fault detection approach.

An Optimal Distributed Fault Detection Scheme for Large-Scale Systems with Deterministic Disturbances

The main objective of this paper is to develop an optimal distributed fault detection (FD) approach for large-scale systems in the presence of unknown deterministic disturbances using the measurement of sensor networks. To be specific, the design approach consists of two phases: the distributed offline training phase and the online implementation phase. The offline training phase includes distributed iterative learning and average consensus algorithm. It is worth mentioning that, the distributed approach avoids enormous computational costs and complex information exchanges. Finally, a numerical example is illustrated to show that the distributed approach can successfully and efficiently accomplish the FD task.

Distributed Soft Fault Detection for Interval Type-2 Fuzzy-Model-Based Stochastic Systems With Wireless Sensor Networks

In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T–S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T–S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.

Distributed resilient consensus: a non-parametric approach

In this paper, two parameter-independent fault-tolerant consensus algorithms are proposed to address the consensus problem in the presence of misbehaving agents. The first algorithm relies on adaptively estimating the number of faulty agents in the network by using a distributed fault-detection scheme. It is shown that this algorithm converges if the network of non-faulty agents is ( f+1)-robust, where f is the number of faulty agents in the network. The second algorithm is a non-parametric Mean-Subsequence-Reduced algorithm whose convergence is guaranteed if the network of non-faulty nodes is ( f+1)-robust and all non-faulty nodes have the same number of in-neighbours. Neither algorithm requires initial knowledge on the number of faulty agents in the network. The efficacy of the algorithms are illustrated with simulation results.

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

This paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for a class of interconnected input–output nonlinear uncertain discrete-time systems. A robust distributed fault detection scheme is designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to the decisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process fault occurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystem can be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by the subsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolation scheme. Moreover, process and sensor fault detectability conditions characterising quantitatively the class of detectable faults are derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detection scheme.

A robust nonlinear observer-based approach for distributed fault detection of input–output interconnected systems

This paper develops a nonlinear observer-based approach for distributed fault detection of a class of interconnected input–output nonlinear systems, which is robust to modeling uncertainty and measurement noise. First, a nonlinear observer design is used to generate the residual signals required for fault detection. Then, a distributed fault detection scheme and the corresponding adaptive thresholds are designed based on the observer characteristics and, at the same time, filtering is used in order to attenuate the effect of measurement noise, which facilitates less conservative thresholds and enhanced robustness. Finally, a fault detectability condition characterizing quantitatively the class of detectable faults is derived.

A Distributed Fault Detection Filtering Approach for a Class of Interconnected Continuous-Time Nonlinear Systems

This paper develops a filtering approach for distributed fault detection of a class of interconnected continuous-time nonlinear systems with modeling uncertainties, disturbances and measurement noise. A distributed fault detection scheme and the corresponding adaptive thresholds are designed based on filtering certain signals so that the effect of the measurement noise and of the disturbances is attenuated, allowing for the design of less conservative thresholds. A key novelty of the proposed work is that a general class of filters can be embedded into the design of the residual and threshold signals in a way that takes advantage of the filtering noise suppression properties. The analysis of the proposed distributed fault detection scheme shows that the derived thresholds guarantee that there are no false alarms and characterizes quantitatively the class of detectable faults. Further rigorous detectability analysis provides results regarding the magnitude of the detectable faults, an upper bound on the detection time and the relation of the detection time with respect to the order and pole locations of the filters used. Simulation results illustrate the proposed distributed fault filtering approach.

Distributed fault detection for modular and reconfigurable robots with joint torque sensing: A prediction error based approach

A distributed fault detection scheme for modular and reconfigurable robots (MRRs) with joint torque sensing is proposed in this paper. With the proposed scheme, the joint torque command is filtered and compared with a filtered torque estimate derived from the nonlinear dynamic model of MRR with joint torque sensing. Common joint actuator faults are considered with fault detection being performed independently for each joint module. The proposed fault detection scheme for each module does not require motion states of any other module making it an ideal modular approach for fault detection of modular robots. Experimental results have confirmed the effectiveness of the proposed fault detection scheme.

Distributed Fault Accommodation for a Class of Interconnected Nonlinear Systems With Partial Communication

Motivated by applications related to large-scale and complex systems, there has recently been an increased interest in the design and analysis of distributed control and fault accommodation schemes. This note presents a distributed fault detection and accommodation design method for a class of nonlinear interconnected subsystems. A fault may occur in the subsystems local dynamics as well as in the interconnections between the subsystems. The subsystems exchange state information according to a tracking error based communication protocol, where each subsystem transmits its state information only when its tracking error exceeds a certain threshold. The robustness of the distributed fault detection scheme and the closed-loop stability properties of the fault accommodation design are established through a rigorous analysis.

Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach

This technical note deals with the problem of designing a distributed fault detection methodology for distributed (and possibly large-scale) nonlinear dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a local fault detector is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability of faults affecting variables shared among different subsystems. Simulation results provide an evidence of the effectiveness of the proposed distributed fault detection scheme.

A New Method for Node Fault Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD) scheme checks out the failed nodes by exchanging data and mutually testing among neighbor nodes in this network., but the fault detection accuracy of a DFD scheme would decrease rapidly when the number of neighbor nodes to be diagnosed is small and the node's failure ratio is high. In this paper, an improved DFD scheme is proposed by defining new detection criteria. Simulation results demonstrate that the improved DFD scheme performs well in the above situation and can increase the fault detection accuracy greatly.