A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals
The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of th...
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| Vydáno v: | Wireless communications and mobile computing Ročník 2021; číslo 1 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford
Hindawi
2021
John Wiley & Sons, Inc |
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| ISSN: | 1530-8669, 1530-8677 |
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| Abstract | The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals. |
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| AbstractList | The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time‐domain signals. |
| Author | Song, Xin Cao, Jiawei Yao, Jian Gu, Yi |
| Author_xml | – sequence: 1 givenname: Yi orcidid: 0000-0001-7962-9466 surname: Gu fullname: Gu, Yi organization: School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxiJiangsu 214122Chinajiangnan.edu.cn – sequence: 2 givenname: Jiawei surname: Cao fullname: Cao, Jiawei organization: School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxiJiangsu 214122Chinajiangnan.edu.cn – sequence: 3 givenname: Xin surname: Song fullname: Song, Xin organization: School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxiJiangsu 214122Chinajiangnan.edu.cn – sequence: 4 givenname: Jian surname: Yao fullname: Yao, Jian organization: School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxiJiangsu 214122Chinajiangnan.edu.cn |
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| Copyright | Copyright © 2021 Yi Gu et al. Copyright © 2021 Yi Gu et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1155/2021/9790053 |
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| Snippet | The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on... |
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| SubjectTerms | Artificial intelligence Back propagation Background noise Bearings Breakdowns Classifiers Datasets Deep learning Fault detection Fault diagnosis Fault location Feature extraction Machinery Machinery condition monitoring Methods Neural networks Noise Noise reduction Roller bearings Rotating machinery Rotating machines Signal monitoring Time domain analysis Vibration monitoring |
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| Title | A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals |
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