文献类型：期刊文献

英文题名：Industrial process fault detection and diagnosis framework based on enhanced supervised kernel entropy component analysis

作者：Xu, Peng[1,2];Liu, Jianchang[1,2];Shang, Liangliang[3];Zhang, Wenle[4,5]

机构：[1]Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China;[2]Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang, Peoples R China;[3]Nantong Univ, Sch Elect Engn, Nantong, Peoples R China;[4]Ocean Univ China, Coll Engn, Qingdao, Peoples R China;[5]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China

年份：2022

卷号：196

外文期刊名：MEASUREMENT

收录：;EI(收录号：20221812061290);WOS:【SCI-EXPANDED(收录号:WOS:000794987600001)】；

基金：This research was supported by the National Natural Science Foun-dation of China (No. 61773106, No. 61806079) .

语种：英文

外文关键词：Process monitoring; Kernel entropy component analysis (KECA); Data-dependent kernel; Multiscale principal component analysis  (MSPCA); Unknown fault diagnosis

摘要：Most existing industrial process fault detection and diagnosis (FDD) techniques operate on data collected at a single scale and focus only on known faults. However, actual process data are inherently multiscale and unknown faults are always inevitable during system running. Therefore, they may perform unsatisfactorily. To tackle this problem, this paper develops a decentralized industrial process FDD framework using multiple enhanced supervised kernel entropy component analysis (enhanced SKECA) models, where each model acts as a fault indicator for one specific fault. Faults can be easily diagnosed by monitoring the outputs of all models within the framework. In particular, when new faults are identified, the framework can update itself only by adding the corresponding enhanced SKECA models without a complete rebuilding process. The monitoring results for the continuous stirred tank reactor (CSTR) process show that the proposed framework is effective in diagnosing both known and unknown faults.