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distributed fault detection and Dynamic Event-Triggered Consensus for Heterogeneous Multiagent Systems Under Deception Attacks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第8期70卷 3294-3304页

作者： Li, Shanglin Chen, Yangzhou Liu, Peter Xiaoping Beijing Univ Technol Coll Artificial Intelligence & Automation Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Minist Educ Beijing 100124 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

This paper focuses on the problem of distributed fault detection and leader-following output consensus for heterogeneous multiagent systems subject to deception attacks. During the information exchange and dissemination, a malicious attacker can make full use of specialized computer technology and launch stochastic deception attacks against some vulnerable agents over the network. The attack signals in actual operation tend to be energy-constrained, and Bernoulli distribution can be used to describe the random features. Taking the attack information into account, the distributed fault detection observer and the dynamic consensus compensator are designed in two separate steps. In order to reduce unnecessary information transmission, a dynamic event-triggered mechanism with output-dependent threshold is introduced to the adjustment of consensus protocol. According to Lyapunov stability theory and linear matrix inequality (LMI) techniques, sufficient conditions are derived for developing the model gains of the observer and the compensator. Finally, a simulation example of RLC circuit systems is provided to illustrate the effectiveness of the obtained theoretical results.

关键词： Observers fault detection Multi-agent systems Task analysis System performance Integrated circuit modeling Cyberattack distributed fault detection dynamic event-triggered consensus heterogeneous multiagent systems deception attacks

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Finite frequency distributed fault detection in sensor networks with memory event-triggered scheme and deception attacks

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ISA TRANSACTIONS 2025年 161卷 24-35页

作者： Cheng, Peng Cai, Chenxiao Park, Poogyeon Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China Univ Macau Fac Sci & Technol Dept Electromech Engn Macau Peoples R China Pohang Univ Sci & Technol Dept Elect Engn Pohang South Korea

This paper concentrates on the finite frequency distributed fault detection (FD) problem for discrete-time nonlinear switched systems (SSs) with time-varying delays through sensor networks. First, the switching mechanism is described by the so-called sojourn probability method. Next, a distributed memory event-triggered scheme (METS) is employed to alleviate the network bandwidth load, and the adverse effects of malicious deception attacks are considered and analyzed. Furthermore, a finite frequency distributed FD filter (FDF) subject to METS and deception attacks is developed corresponding to each sensor node, and the existence conditions of the FDF are offered in the form of linear matrix inequalities. Lyapunov stability analysis shows that the resulting augmented filter system is mean-square stable and meets the specified full-frequency HPo level bound and finite-frequency H_ level bound. Comparative simulations demonstrate that the proposed finite-frequency distributed FDF can detect unknown faults in nonlinear SSs on sensor networks more quickly and accurately than some existing full-frequency solutions.

关键词： distributed fault detection Finite frequency Nonlinear systems Switched systems

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Data-Driven Optimal distributed fault detection Based on Subspace Identification for Large-Scale Interconnected Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第2期20卷 2497-2507页

作者： Li, Biao Yang, Ying Peking Univ Coll Engn Dept Mech & Engn Sci State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

This article investigates the problem of data-driven distributed optimal fault detection for large-scale interconnected systems with the unmeasurable interaction term of neighboring system information. For large-scale systems, the computational and storage burdens hinder the application of centralized fault detection methods, while the existence of the unknown interaction term in residual generators brings challenges to distributed fault detection problems. To solve the above problems, the unknown interaction term is implicitly included in each subsystem through an algebraic equivalent transformation, so that the residual generator constructed by the distributed method will not lose the fault information propagated along the network topology. Furthermore, an optimization scheme is designed to measure the effect of the residual signal on noise and faults in all dimensions of the parity space, making the residual generator sufficiently sensitive to even weak faults. Numerical examples and a real hot strip rolling case verify the effectiveness and superiority of the proposed method.

关键词： Data-driven distributed fault detection large-scale interconnected systems optimization scheme subspace identification

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A data-driven distributed fault detection scheme based onsubspace identification technique for dynamic systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第7期33卷 3107-3128页

作者： Cheng, C. Wang, Q. Nikitin, Y. Liu, C. Zhou, Y. Chen, H. School of Computer Science and Engineering Changchun University of Technology Changchun China Department of Mechatronic Systems Kalashnikov Izhevsk State Technical University Izhevsk Russia School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Artificial Intelligence Shanghai University Shanghai China Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Department of Chemical and Materials Engineering University of Alberta Edmonton Alberta Canada

With 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.

关键词： average consensus data-driven designs distributed fault detection sensor networks subspaceidentification

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An Hoo/Hoo optimization technique to distributed fault detection for multi-agent systems based on event-triggered mechanism q

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NEUROCOMPUTING 2023年 535卷 134-143页

作者： Jia, Hao Ji, Wenchengyu Xie, Xiangpeng Wang, Shenquan Changchun Univ Technol Coll Elect & Elect Engn Changchun Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China

This paper proposes an H �/H � optimization technique to distributed fault detection (FD) for multi-agent systems (MASs). To simultaneously consider the effects of unknown disturbances and faults in the MASs and event-triggered transmission errors (ETTEs) on the residual signal, a residual generator related to the event-triggered threshold coefficient is designed. The generated residuals achieve the best compromise between robustness to unknown disturbances and sensitivity to faults. The obtained results have a better detection performance by using H �/H � optimization technique compared with other optimized schemes. In addition, the applicability of the proposed H �/H � optimized distributed FD scheme is veri-fied by the simulation of a vehicle lateral dynamic system.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Multi-agent systems Event-triggered mechanism distributed fault detection Residual evaluation

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Multi-node knowledge graph assisted distributed fault detection for large-scale industrial processes based on graph attention network and bidirectional LSTMs

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NEURAL NETWORKS 2024年 173卷 106210页

作者： Li, Qing Wang, Yangfan Dong, Jie Zhang, Chi Peng, Kaixiang Univ Sci & Technol Beijing Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 PR Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Natl Engn Res Ctr Adv Rolling & Intelligent Mfg Beijing 100083 Peoples R China

Modern industrial processes are characterized by extensive, multiple operation units, and strong coupled correlation of subsystems. fault detection of large-scale processes is still a challenging problem, especially for tandem plant -wide processes in multiple fields such as water treatment process. In this paper, a novel distributed graph attention network -bidirectional long short-term memory (D-GATBLSTM) fault detection model is proposed for large-scale industrial processes. Firstly, a multi -node knowledge graph (MNKG) is constructed using a joint data and knowledge driven strategy. Secondly, for large-scale industrial process, a global feature extractor of graph attention networks (GATs) is constructed, on the basis of which, sub -blocks are decomposed based on MNKG. Then, local feature extractors of bidirectional long short-term memory (Bi-LSTM) for each sub -block are constructed, in which correlations among multiple sub -blocks are considered. Finally, a multi-subblock fusion collaborative prediction model is constructed and the comprehensive fault detection results are given by the grid search method. The effectiveness of our D-GATBLSTM is exemplified in a secure water treatment process case, where it outperforms baseline models compared, with 27% improvement in precision, 15% increase in recall, and overall F -score enhancement of 0.22.

关键词： distributed fault detection Graph attention networks Bidirectional long short-term memory Multi-node knowledge graph (MNKG)

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A distributed fault detection Filter based on Zonotopes 41

A Distributed Fault Detection Filter based on Zonotopes

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第41届中国控制会议

作者： Baiqi Yang Ye Wang College of Intelligent Systems Science and Technology Harbin Engineering University Engineering Research Center of Navigation Instruments Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a distributed fault detection filter based on zonotopes is proposed for discrete-time distributed systems subject to additive actuator *** considered discrete-time distributed systems are also perturbed by unknown-but-bounded uncertainties,including state disturbances and measurement *** using zonotopic approaches,the effects of system uncertainties and faults can be divided into two deterministic zonotopes for each *** on this decomposition,the distributed fault detection filter gains are designed to be robust against uncertainties and meanwhile sensitive to actuator faults in a finite-frequency *** a result,two linear matrix inequality conditions are developed for the design of the distributed fault detection filter ***,the effectiveness of the proposed approach is shown with some numerical simulations.

关键词： distributed fault detection Zonotopes Robustness fault Sensitivity Discrete-time Systems

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distributed fault detection of Nonlinear Process Systems with Senor Failures 21st

Distributed Fault Detection of Nonlinear Process Systems wit...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Zhang, Langwen Xie, Wei Lian, Yindong South China Univ Technol Guangzhou 510640 Peoples R China

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. Copyright (C) 2020 The Authors.

关键词： distributed fault detection distributed extended Kalman Filter Sensor faults Completely stirred tank reactor

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Adaptive Fuzzy distributed fault detection for Large-scale Systems with Overlapping Decompositions 33

Adaptive Fuzzy Distributed Fault Detection for Large-scale S...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Liang, Dingguo Yang, Ying Li, Rongchang Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst Dept Mech & Engn Sci Beijing 100871 Peoples R China

ISBN: (纸本)9781665440899

This paper focuses on the problem of distributed fault detection (FD) for large-scale dynamic systems which are modeled as the interconnection of several subsystems. A distributed estimator is constructed for each subsystem, in which an adaptive fuzzy approximator is embedded to learn the unknown nonlinear interconnection with the neighboring subsystems. Overlapping decompositions are considered in the FD scheme, thus allowing some state variables to be shared among different subsystems. A consensus filter is designed to help the shared state variable in each subsystem reach a common FD decision as well as improve the property of FD. The stability of the estimation error is analyzed and the sufficient conditions of the fault detectability are also addressed. Simulation results are provided to illustrate the effectiveness of the proposed method.

关键词： distributed fault detection large-scale systems (LSSs) overlapping decompositions adaptive fuzzy approximator nonlinear systems

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CONTROL ENGINEERING PRACTICE 2022年 127卷

作者： Zhang, Xueyi Ma, Liang Peng, Kaixiang Zhang, Chuanfang Univ Sci & Technol Beijing Sch Automation & Elect Engn Key Lab Knowledge Automation Ind Proc Minist Educ Beijing 100083 Peoples R China Univ Sci & Technol Beijing Natl Engn Res Ctr Adv Rolling Technol Beijing 100083 Peoples R China

Process industries are usually composed of several coupled sub-processes, which are distributed in different positions, connected and transmitted in the form of quality flow and information flow. The long process, large-scale, dynamic coupling variables, and quality inheritance among sub-processes for process industries have brought new challenges to traditional quality-related fault detection. In this paper, a novel distributed fault detection method based on quality-related modified regularized slow feature analysis (QMRSFA) is proposed to deal with dynamics, connection relation, and outliers in large-scale sequential processes. First, robust preprocessing methods are devised to eliminate outliers, and process knowledge is utilized as a constraint to decompose the whole production process into different sub-processes. Then, a new dynamic QMRSFA method is developed as the local monitoring model. After that, an expression of the connection relation between sub-processes is given, where the quality-related slow features extracted from the previous sub-process are used as part constraint conditions of the current sub-process. In addition, the local and global indexes are established based on Bayesian fusion for quality-related fault detection. Finally, a typical large-scale sequential process, the hot strip mill process is taken as an example for verification, and the results show the practicability and feasibility of the proposed method.

关键词： Quality-related distributed fault detection Sequential connection Slow feature analysis Hot strip mill process

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