文献类型：期刊文献

英文题名：A supervised multisegment probability density analysis method for incipient fault detection of quality indicator

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, Peoples R China

年份：2022

卷号：116

起止页码：53

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20222412231955);WOS:【SCI-EXPANDED(收录号:WOS:000822930300005)】；

基金：Acknowledgments This work was supported in part by the National Natural Sci-ence Foundation of China under Grant 62103149, Grant 62073140 and Grant 62073141, in part by National Natural Sci-ence Foundation of Shanghai under Grant 19ZR1473200.

语种：英文

外文关键词：Incipient fault detection; Quality indicator; Process monitoring; Supervised multisegment probability; density analysis

摘要：The quality indicator monitoring has received widely attention and research in recent years, however, the detection of indicator-related incipient fault is still a challenging topic. In this paper, a supervised probability density analysis algorithm is proposed to detect the incipient fault in quality indicator. Firstly, the core process variable filter is introduced, and the regression model is constructed to extract the indicator-related information from process variable. Secondly, the data distribution extension and subsegment division strategy are presented, and a probability density estimation method is put forward for the indicator-related latent variable. Through the proposed symmetric divergence index, the distribution discrepancy between the online sample and the reference sample set is evaluated, which can be used for the incipient fault detection. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the effectiveness of the proposed method. (c) 2022 Elsevier Ltd. All rights reserved.