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

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...

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Detalles Bibliográficos
Autores principales: Zhu, Jinlin, Jiang, Muyun, Liu, Zhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749793/
https://www.ncbi.nlm.nih.gov/pubmed/35009769
http://dx.doi.org/10.3390/s22010227
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author Zhu, Jinlin
Jiang, Muyun
Liu, Zhong
author_facet Zhu, Jinlin
Jiang, Muyun
Liu, Zhong
author_sort Zhu, Jinlin
collection PubMed
description 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.
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spelling pubmed-87497932022-01-12 Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study Zhu, Jinlin Jiang, Muyun Liu, Zhong Sensors (Basel) Article 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. MDPI 2021-12-29 /pmc/articles/PMC8749793/ /pubmed/35009769 http://dx.doi.org/10.3390/s22010227 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Jinlin
Jiang, Muyun
Liu, Zhong
Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
title Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
title_full Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
title_fullStr Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
title_full_unstemmed Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
title_short Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
title_sort fault detection and diagnosis in industrial processes with variational autoencoder: a comprehensive study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749793/
https://www.ncbi.nlm.nih.gov/pubmed/35009769
http://dx.doi.org/10.3390/s22010227
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AT jiangmuyun faultdetectionanddiagnosisinindustrialprocesseswithvariationalautoencoderacomprehensivestudy
AT liuzhong faultdetectionanddiagnosisinindustrialprocesseswithvariationalautoencoderacomprehensivestudy