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

Cen Guo; Wenkai Hu; Fan Yang; Dexian Huang

doi:10.1016/j.cjche.2020.06.015

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Deep learning technique for process fault detection and diagnosis in the presence of incomplete data
- Deep learning technique for process fault detection and diagnosis in the presence of incomplete data
- 中国化学工程学报（英文版） 2020年28卷第9期页码：2358-2367
- Affiliations：
  
  1. Department of Automation, Tsinghua University,Beijing,China,10084
  2. Cornell University, NY 14850, United States of America
  3. University of Alberta,, AB,Edmonton,Canada,T6G 1H9
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2020.06.015
  中图分类号：
- 纸质出版：2020
- Accepted：
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Cen Guo, Wenkai Hu, Fan Yang, 等. Deep learning technique for process fault detection and diagnosis in the presence of incomplete data[J]. 中国化学工程学报（英文版）, 2020,28(9):2358-2367.

Cen Guo, Wenkai Hu, Fan Yang, Dexian Huang. Deep learning technique for process fault detection and diagnosis in the presence of incomplete data[J]. Chinese Journal of Chemical Engineering, 2020, 28(9): 2358-2367.
Cen Guo, Wenkai Hu, Fan Yang, 等. Deep learning technique for process fault detection and diagnosis in the presence of incomplete data[J]. 中国化学工程学报（英文版）, 2020,28(9):2358-2367. DOI： 10.1016/j.cjche.2020.06.015.

Cen Guo, Wenkai Hu, Fan Yang, Dexian Huang. Deep learning technique for process fault detection and diagnosis in the presence of incomplete data[J]. Chinese Journal of Chemical Engineering, 2020, 28(9): 2358-2367. DOI： 10.1016/j.cjche.2020.06.015.

摘要

Abstract

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.

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