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Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neu...

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Autores principales: Pham, Minh Tuan, Kim, Jong-Myon, Kim, Cheol Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730594/
https://www.ncbi.nlm.nih.gov/pubmed/33276483
http://dx.doi.org/10.3390/s20236886
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author Pham, Minh Tuan
Kim, Jong-Myon
Kim, Cheol Hong
author_facet Pham, Minh Tuan
Kim, Jong-Myon
Kim, Cheol Hong
author_sort Pham, Minh Tuan
collection PubMed
description Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.
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spelling pubmed-77305942020-12-12 Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems Pham, Minh Tuan Kim, Jong-Myon Kim, Cheol Hong Sensors (Basel) Article Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods. MDPI 2020-12-02 /pmc/articles/PMC7730594/ /pubmed/33276483 http://dx.doi.org/10.3390/s20236886 Text en © 2020 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
Pham, Minh Tuan
Kim, Jong-Myon
Kim, Cheol Hong
Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems
title Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems
title_full Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems
title_fullStr Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems
title_full_unstemmed Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems
title_short Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems
title_sort deep learning-based bearing fault diagnosis method for embedded systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730594/
https://www.ncbi.nlm.nih.gov/pubmed/33276483
http://dx.doi.org/10.3390/s20236886
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