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A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spect...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566980/ https://www.ncbi.nlm.nih.gov/pubmed/31137616 http://dx.doi.org/10.3390/s19102381 |
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author | Ma, Shangjun Cai, Wei Liu, Wenkai Shang, Zhaowei Liu, Geng |
author_facet | Ma, Shangjun Cai, Wei Liu, Wenkai Shang, Zhaowei Liu, Geng |
author_sort | Ma, Shangjun |
collection | PubMed |
description | To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results. |
format | Online Article Text |
id | pubmed-6566980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65669802019-06-17 A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery Ma, Shangjun Cai, Wei Liu, Wenkai Shang, Zhaowei Liu, Geng Sensors (Basel) Article To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results. MDPI 2019-05-24 /pmc/articles/PMC6566980/ /pubmed/31137616 http://dx.doi.org/10.3390/s19102381 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 Ma, Shangjun Cai, Wei Liu, Wenkai Shang, Zhaowei Liu, Geng A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery |
title | A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery |
title_full | A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery |
title_fullStr | A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery |
title_full_unstemmed | A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery |
title_short | A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery |
title_sort | lighted deep convolutional neural network based fault diagnosis of rotating machinery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566980/ https://www.ncbi.nlm.nih.gov/pubmed/31137616 http://dx.doi.org/10.3390/s19102381 |
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