文献类型：期刊文献

英文题名：Weighted Conditional Discriminant Analysis for Unseen Operating Modes Fault Diagnosis in Chemical Processes

作者：Xiao, Yutang[1];Shi, Hongbo[1];Wang, Boyu[2,3];Tao, Yang[1];Tan, Shuai[1];Song, Bing[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Western Ontario, Dept Comp Sci, London, ON N6A 5B7, Canada;[3]Univ Western Ontario, Brain Mind Inst, London, ON N6A 5B7, Canada

年份：2022

卷号：71

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20220911725567);WOS:【SCI-EXPANDED(收录号:WOS:000766618900021)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFC1522502 and Grant 2020YFC1522505; in part by the National Natural Science Foundation of China under Grant 62073140, Grant 62073141, and Grant 62103149; in part by the Shanghai Rising-Star Program under Grant 21QA1401800; in part by the National Natural Science Foundation of Shanghai under Grant 19ZR1473200 and Grant 22ZR1417000. The work of Boyu Wang was supported by the NSERC Discovery Grants Program.

语种：英文

外文关键词：Fault diagnosis; Chemical processes; Chemicals; Data models; Computational modeling; Task analysis; Feature extraction; Chemical processes fault diagnosis; conditional discriminant analysis; domain adaptation (DA); domain generalization (DG); model updating; variable weighting

摘要：One challenge faced by data-driven fault diagnosis methods is that they may perform well over the operating modes where the historical data are collected, but fail to generalize to unseen modes that have never appeared before. That is one of the root causes that have prevented many advanced fault diagnosis methods from being widely accepted by the chemical industry. Consequently, it is significant to develop a novel fault diagnosis method, which can build a model to determine the type of faults occurred on unseen modes. On the other hand, one chemical process generally experiences multiple operating modes, from which common knowledge of these modes can be extracted and be applied to an unseen mode. To this end, a novel weighted conditional discriminant analysis (WCDA) algorithm is proposed by adopting the context of domain generalization (DG) approaches to leverage and distill the knowledge from historical modes for unseen modes of fault diagnosis. Specifically, a novel variable weighting scheme is developed based on the Kullback-Leibler divergence between features of different modes. Then, a fault diagnosis model is constructed by learning a classifier and invariant feature representation simultaneously. Moreover, WCDA is extended to the context of domain adaptation (DA), where the performance of the fault diagnosis model is further improved by leveraging the unlabeled data collected from a new mode. Empirical results on a numerical example, the Tennessee Eastman process, and continuous stirred tank chemical reactor demonstrate the effectiveness of our method.