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

• 作者: Cen Guo Dexian Huang Fan Yang Wenkai Hu
• 来源期刊: 中国化学工程学报（英文版）
• 年，卷（期）: 2020,（9）
• 所属期刊栏目: Process Systems Engineering and Process Safety
• 页码范围: 2358-2367
• 页数: 10页
• 语种: 英文

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 applicabili～ of the proposed method.