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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/161306 |
| _version_ | 1811681397082947584 |
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| author | Zhu, Jinlin Jiang, Muyun Liu, Zhong |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhu, Jinlin Jiang, Muyun Liu, Zhong |
| author_sort | Zhu, Jinlin |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T03:40:17Z |
| format | Journal Article |
| id | ntu-10356/161306 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:40:17Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1613062022-08-24T06:35:16Z Fault detection and diagnosis in industrial processes with variational autoencoder: a comprehensive study Zhu, Jinlin Jiang, Muyun Liu, Zhong School of Computer Science and Engineering Engineering::Computer science and engineering Process Monitoring Deep Model 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. Published version 2022-08-24T06:35:15Z 2022-08-24T06:35:15Z 2022 Journal Article Zhu, J., Jiang, M. & Liu, Z. (2022). Fault detection and diagnosis in industrial processes with variational autoencoder: a comprehensive study. Sensors, 22(1), 227-. https://dx.doi.org/10.3390/s22010227 1424-8220 https://hdl.handle.net/10356/161306 10.3390/s22010227 35009769 2-s2.0-85121791859 1 22 227 en Sensors © 2021 by the authors. 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/). application/pdf |
| spellingShingle | Engineering::Computer science and engineering Process Monitoring Deep Model 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 | Engineering::Computer science and engineering Process Monitoring Deep Model |
| url | https://hdl.handle.net/10356/161306 |
| work_keys_str_mv | AT zhujinlin faultdetectionanddiagnosisinindustrialprocesseswithvariationalautoencoderacomprehensivestudy AT jiangmuyun faultdetectionanddiagnosisinindustrialprocesseswithvariationalautoencoderacomprehensivestudy AT liuzhong faultdetectionanddiagnosisinindustrialprocesseswithvariationalautoencoderacomprehensivestudy |