Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

作     者：Cen Guo Wenkai Hu Fan Yang Dexian Huang Cen Guo;Wenkai Hu;Fan Yang;Dexian Huang

作者机构：Department of AutomationTsinghua UniversityBeijing 10084China Cornell UniversityNY 14850United States of America University of AlbertaEdmontonAB T6G 1H9Canada 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2020年第28卷第9期

页      面：2358-2367页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0838[工学-公安技术] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61433001) Tsinghua University Initiative Scientific Research Program 

主　　题：Alarm configuration Deep learning Fault detection and diagnosis Incomplete data Stacked autoencoder 

摘      要：In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.