文献类型：期刊文献

英文题名：Hierarchical Latent Variable Extraction and Multisegment Probability Density Analysis Method for Incipient Fault Detection

作者：Tao, Yang[1];Shi, Hongbo[1];Song, Bing[1];Tan, Shuai[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：18

期号：4

起止页码：2244

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20213310777816);WOS:【SCI-EXPANDED(收录号:WOS:000739636900012)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62073140 and Grant 62073141 and in part by the Fundamental Research Funds for the Central Universities under Grant 222201717006. Paper no. TII-21-0003.

语种：英文

外文关键词：Monitoring; Data mining; Fault detection; Indexes; Heuristic algorithms; Informatics; Feature extraction; Fault detection; hierarchical latent variable extraction; incipient fault; minor fault; multisegment probability density analysis

摘要：The incipient fault is difficult to detect because of its small amplitude and insignificant impact, however, ignoring such fault may cause irreversible damage to the system. In this article, a hierarchical latent variable extraction and multisegment probability density analysis method is proposed to detect the incipient fault. First, three data subspaces are constructed, which are named dominant, intermediate, and residual spaces, and key latent variables which contain more offline variance or online variation information will be retained. Afterward, the expanded data distribution interval and multiple data segmentsare constructed for the probability density estimation. Based on the improved symmetric divergence index, the distribution distance between the online data and offline modeling data can be evaluated, which has achieved 95.3% and 86.8% average detection rates for the faults in numerical case and Tennessee Eastman process. Finally, a real multiphase flow facility is used to demonstrate the effectiveness of the proposed method.