Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

被引:18
|
作者
Zhu, Jinlin [1 ,2 ]
Jiang, Muyun [3 ]
Liu, Zhong [4 ]
机构
[1] Jiangnan Univ, State Key Lab Food Sci & Technol, Wuxi 214122, Jiangsu, Peoples R China
[2] Jiangnan Univ, Sch Food Sci & Technol, Wuxi 214122, Jiangsu, Peoples R China
[3] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
[4] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
process monitoring; deep model; variational autoencoder; deep reconstruction; dynamic process;
D O I
10.3390/s22010227
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.
引用
收藏
页数:26
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