文献类型：期刊文献

英文题名：Distributed data-driven optimal fault detection for large-scale systems

作者：Li, Linlin[1,2];Ding, Steven X.[3];Peng, Xin[2]

机构：[1]Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Key Lab Knowledge Automat Ind Proc, Minist Educ, Beijing 100083, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[3]Univ Duisburg Essen, Inst Automat Control & Complex Syst, D-47057 Duisburg, Germany

年份：2020

卷号：96

起止页码：94

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20204809533639);WOS:【SCI-EXPANDED(收录号:WOS:000598615400009)】；

基金：This work has been supported by the National Natural Science Foundation of China under Grants 62073029 and 61803157, Beijing Natural Science Foundation under Grant 4202045, and Shanghai Sailing Program under Grant 18YF1405200, and the Fundamental Research Funds for the Central Universities under Grant 222202017006.

语种：英文

外文关键词：Fault detection; Large-scale systems; Data-driven; Distributed fault detection

摘要：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.