文献类型：期刊文献

英文题名：Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes

作者：Jiang, Qingchao[1];Jiang, Jiashi[1];Wang, Wenjing[1];Pan, Chunjian[2];Zhong, Weimin[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Univ Elect Power, Coll Automat Engn, Shanghai 200090, Peoples R China

年份：2024

卷号：35

期号：5

起止页码：6218

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20231013683819);WOS:【SCI-EXPANDED(收录号:WOS:000936289700001)】；

基金：This work was supported in part by the National NaturalScience Fund for Distinguished Young Scholars under Grant 61925305, in part by the National Natural Science Foundation of China under Grant 61973119,and in part by the Shanghai Rising-Star Program under Grant 20QA1402600.

语种：英文

外文关键词：Phase change materials; Fault diagnosis; Fault detection; Time series analysis; Process monitoring; Input variables; Industries; Causality inference; fault diagnosis; partial cross mapping (PCM); process monitoring; root cause diagnosis

摘要：Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T-2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.