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2nd IEEE International Conference on Parallel, distributed and Grid Computing (PDGC)

作者： Panda, Meenakshi Khilar, Pabitra Mohan Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 India

ISBN: (纸本)9781467329255;9781467329224

Wireless sensor networks (WSN) continue to get tremendous popularity because of the increasing number of applications. Some times few sensor nodes are unable to send the correct data to the fusion center or to the neighbour node. Since the network is unaware of the faulty status of the node, so the performance degrades more. The data received from the faulty node can treat as outliers. So that a statistical test can be used to identify the outliers data send by the faulty sensor node. In this paper a neighbouring coordination based self detectable distributed fault detection algorithm and statistical Z-test is proposed. The proposed distributed algorithm is implemented in NS3 and the performance is evaluated in terms of false alarm rate (FAR) and detection accuracy (DA). The results are compared with existing algorithm and shows that the proposed approach gives better performance than the existing algorithms.

关键词： Wireless sensor networks distributed fault detection soft fault neighbor coordination

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Data-driven distributed collaborative fault detection and isolation for large-scale dynamic processes in simultaneous-fault cases

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CONTROL ENGINEERING PRACTICE 2023年 141卷

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

This paper proposes a data-driven distributed collaborative fault detection and isolation scheme for large-scale dynamic processes, interconnected by heterogeneous subsystems with both actuator and sensor faults. The residual generator in each subsystem is directly constructed with both local and neighboring process data based on subspace identification. Utilizing the rank deficiency property, the healthy mode residual covariance matrix is estimated with noisy data, and thus the test statistic and detection threshold are obtained. Then, a collaborative simultaneous fault isolation method is developed, where the local residual is decoupled with neighboring actuator fault's effects and a series of robust residual generators are constructed by solving corresponding optimization problems. In each subsystem, both local fault isolation and neighboring sensor fault isolation are realized. Thus, sensor fault detection and isolation for the whole process can be achieved with a reduced number of monitoring subsystems. Finally, case studies on the Tennessee Eastman (TE) process and a real hot strip mill process (HSMP) verify the feasibility and good performance of the proposed scheme.

关键词： distributed fault detection Collaborative fault isolation Data-driven method Subspace identification Large-scale process

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distributed data-driven optimal fault detection for large-scale systems

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JOURNAL OF PROCESS CONTROL 2020年 96卷 94-103页

作者： Li, Linlin Ding, Steven X. Peng, Xin Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst D-47057 Duisburg Germany

This paper is concentrated on two new distributed data-driven optimal fault detection approaches in large-scale systems using a group of sensor blocks, each of which accesses part of the process variables. Towards this end, an optimal fault detection problem is first formulated and solved, which lays a foundation for further distributed studies. Based on it, the first distributed data-driven optimal fault detection scheme, consisting of offline distributed learning and online distributed detection, is developed using the average consensus algorithm. To further reduce communication and computation efforts, the second average consensus based fault detection is investigated. Considering that the iteration computations for average consensus algorithm can lead to fault detection delay, a variation of the average consensus based fault detection scheme is proposed with iterative estimation of the covariance matrices of random variables and implementation of the distributed test statistic during the consensus iteration. A numerical example and a case study on the PRONTO heterogeneous benchmark dataset are used to demonstrate the proposed approaches. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： fault detection Large-scale systems Data-driven distributed fault detection

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A distributed High-Impedance fault detection and Protection Scheme in DC Microgrids

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IEEE TRANSACTIONS ON POWER DELIVERY 2024年第1期39卷 141-154页

作者： Yadegar, Meysam Zarei, Seyed Fariborz Meskin, Nader Blaabjerg, Frede Qom Univ Technol Dept Elect & Comp Engn Qom *** Iran Qatar Univ Dept Elect Engn Doha 2713 Qatar Aalborg Univ Dept Energy DK-9100 Aalborg Denmark

This paper proposes a high-impedance fault (HIF) detection and protection scheme for DC microgrids. HIFs occur when a (live) conductor makes contact with a surface which restricts the flow of fault current to a level that cannot be reliably sensed/detected by the conventional relays. HIF detection is an important concern for the electric power grid since it may cause public safety hazards. In this paper, a distributed HIF detection and localization scheme is proposed which considers the inherent HIF arc-characteristics of very low fault current amplitude, nonlinearity, buildup, shoulder, intermittence, and high-frequency content. The proposed scheme effectively identifies the faulty section, and provides forward and reverse fault discrimination capability. To verify the effectiveness of the proposed method, different sets of test cases and experiments are analyzed and presented. In the test cases, the forward/reverse faults and the noise-induced signals are considered and evaluated. Furthermore, detection and localization of both ideal and non-ideal HIFs are evaluated on the test system. The non-ideal HIFs are modeled based on the experimental test waveforms of HIFs. Also, the functionality of the proposed scheme is verified under normal condition with operational changes in a DC microgrid.

关键词： Circuit faults Microgrids Location awareness fault detection fault currents Integrated circuit interconnections Voltage control DC microgrid distributed fault detection fault localization high-impedance fault

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A distributed Kalman filter based fault detection scheme incorporating weighted average consensus algorithm for large-scale interconnected systems 12

A distributed Kalman filter based fault detection scheme inc...

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12th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Yuan, Haozhou Gao, Jingjing Huang, Jian Yang, Xu Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

Stimulated by the increasing demands for system safety and process reliability in complex industrial processes, this paper investigates a weighted average consensus algorithm based distributed fault detection scheme for large-scale interconnected systems, using a sensor network where each node is equipped with a Kalman filter (KF). To reduce the communication and computation efforts, the proposed distributed fault detection scheme is splitted into two phases: distributed offline training and online fault detection. To this end, the Expectation-Maximization (EM) algorithm is firstly addressed to identify the unknown measurement matrices and covariance matrices of noise vectors. It is followed by an average consensus algorithm so that the identical Kalman filters can be designed in parallel at all sensor nodes. On this basis, distributed residual generators and test statistics are constructed for fault detection purpose using the average consensus algorithm. Considering that there exist some special conditions, such as the occurrence of node failures, a variation of the distributed Kalman filter based fault detection scheme is proposed by dynamically adjusting the consensus weight. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated through a case study on the waste water treatment plants (WWTPs).

关键词： distributed fault detection Large-scale systems distributed Kalman filter Weighted average consensus Expectation-Maximization algorithm

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distributed H_/L∞ fault detection observer design for linear systems 21st

Distributed H_/L∞ fault detection observer design for linea...

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

作者： Han, Weixin Trentelman, Harry L. Xu, Bin Northwestern Polytech Univ Xian Peoples R China Univ Groningen Groningen Netherlands

This paper studies the distributed fault detection problem for linear time-invariant (LTI) systems with distributed measurement output. A distributed H_/L-infinity 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(infinity )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. Copyright (C) 2020 The Authors.

关键词： distributed fault detection linear system observers finite-frequency L-infinity analysis

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Fully distributed Unknown Input Observer based fault detection for Interconnected Systems

Fully Distributed Unknown Input Observer based Fault Detecti...

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American Control Conference (ACC)

作者： Liang, Dingguo He, Zhichen Zhao, Zhengen Li, Wenlong Yang, Ying Peking Univ Dept Mech & Engn Sci Coll Engn Beijing 100871 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

ISBN: (纸本)9798350382662;9798350382655

In this article, a set of fully distributed unknown input observers (UIOs) are designed for continuous-time linear interconnected systems. As a fully distributed scheme, each subsystem uses only local and interconnected information with neighbors to design the UIO, and then their data to monitor the local state. By unknown input decomposition, the UIOs can asymptotically estimate the state of interconnected systems in the presence of actuator faults. The presented scheme is scalable, allowing plug and play (PnP) operations. Then, a Luenberger observer is constructed for each subsystem, which interconnected with UIOs for the other subsystems is utilized to construct the fault detector. The residual generated by the fault detector is sensitive to the local fault while decoupled with faults in the other subsystems such that distributed fault detection for interconnected systems with more than one faulty subsystems is feasible. Finally, simulations are conducted for fault detection on power networks to show the effectiveness of the proposed method.

关键词： Unknown input observer distributed fault detection interconnected systems plug-and-play operations

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distributed H_/L ∞ fault detection observer design for linear systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 688-693页

作者： Weixin Han Harry L. Trentelman Bin Xu Northwestern Polytechnical University Xi’an P.R. China University of Groningen Groningen The Netherlands

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.

关键词： distributed fault detection linear system observers finite-frequency L ∞ analysis

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A distributed Kalman filter based fault detection scheme incorporating weighted average consensus algorithm for large-scale interconnected systems ⁎

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IFAC-PapersOnLine 2024年第14期58卷 586-591页

作者： Haozhou Yuan Jingjing Gao Jian Huang Xu Yang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China

关键词： distributed fault detection Large-scale systems distributed Kalman filter Weighted average consensus Expectation-Maximization algorithm

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A novel distributed detection framework for quality-related faults in industrial plant-wide processes

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NEUROCOMPUTING 2022年 492卷 126-136页

作者： Ma, Liang Wang, Mengwei Dong, Jie Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

Quality-related fault detection is crucial for improving system reliability, reducing production costs and ensuring product quality, which has been an emerging area of practical interest. An industrial plant-wide process contains several interactive subprocesses and large number of variables, which makes traditional centralized monitoring methods face severe challenges. In this context, a new distributed detection framework for quality-related faults is designed, which will provide a reasonable solution to increase the monitoring reliability and economic efficiency for industrial plant-wide processes. To be specific, the main innovations are: (1) a performance-based process decomposition method is proposed combined mechanical knowledge with affinity propagation (AP) clustering algorithm, which will be helpful to find the common variables between subprocesses;(2) a new dynamic mixed kernel partial least squares (DMKPLS) model with information interaction is built for local monitoring;(3) Bayesian inference is implemented to establish statistical indicators for monitoring the plant-wide process. Furthermore, Tennessee Eastman (TE) process is adopted to verify the fault detection performance of the proposed framework. (c) 2022 Elsevier B.V. All rights reserved.

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