A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven...

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Autores principales: Toma, Rafia Nishat, Piltan, Farzin, Kim, Jong-Myon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706012/
https://www.ncbi.nlm.nih.gov/pubmed/34960552
http://dx.doi.org/10.3390/s21248453
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author Toma, Rafia Nishat
Piltan, Farzin
Kim, Jong-Myon
author_facet Toma, Rafia Nishat
Piltan, Farzin
Kim, Jong-Myon
author_sort Toma, Rafia Nishat
collection PubMed
description Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal.
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spelling pubmed-87060122021-12-25 A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors Toma, Rafia Nishat Piltan, Farzin Kim, Jong-Myon Sensors (Basel) Article Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal. MDPI 2021-12-18 /pmc/articles/PMC8706012/ /pubmed/34960552 http://dx.doi.org/10.3390/s21248453 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
Toma, Rafia Nishat
Piltan, Farzin
Kim, Jong-Myon
A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
title A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
title_full A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
title_fullStr A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
title_full_unstemmed A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
title_short A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
title_sort deep autoencoder-based convolution neural network framework for bearing fault classification in induction motors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706012/
https://www.ncbi.nlm.nih.gov/pubmed/34960552
http://dx.doi.org/10.3390/s21248453
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