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Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time...

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Detalles Bibliográficos
Autores principales: Park, Pangun, Marco, Piergiuseppe Di, Shin, Hyejeon, Bang, Junseong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6866134/
https://www.ncbi.nlm.nih.gov/pubmed/31652821
http://dx.doi.org/10.3390/s19214612
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author Park, Pangun
Marco, Piergiuseppe Di
Shin, Hyejeon
Bang, Junseong
author_facet Park, Pangun
Marco, Piergiuseppe Di
Shin, Hyejeon
Bang, Junseong
author_sort Park, Pangun
collection PubMed
description Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.
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spelling pubmed-68661342019-12-09 Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network Park, Pangun Marco, Piergiuseppe Di Shin, Hyejeon Bang, Junseong Sensors (Basel) Article Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time. MDPI 2019-10-23 /pmc/articles/PMC6866134/ /pubmed/31652821 http://dx.doi.org/10.3390/s19214612 Text en © 2019 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Park, Pangun
Marco, Piergiuseppe Di
Shin, Hyejeon
Bang, Junseong
Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
title Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
title_full Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
title_fullStr Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
title_full_unstemmed Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
title_short Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
title_sort fault detection and diagnosis using combined autoencoder and long short-term memory network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6866134/
https://www.ncbi.nlm.nih.gov/pubmed/31652821
http://dx.doi.org/10.3390/s19214612
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