Deep Learning Method for Bearing Fault Diagnosis
In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and th...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2022-08-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2124 |
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| author | LIU Xiu MA Shan-tao XIE Yi-ning HE Yong-jun |
| author_facet | LIU Xiu MA Shan-tao XIE Yi-ning HE Yong-jun |
| author_sort | LIU Xiu |
| collection | DOAJ |
| description | In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. After testing on multiple data sets, and comparing with existing methods, experiments show that the method in this paper has significant performance in accuracy, noise robustness, and fault recognition rate under variable load conditions. |
| format | Article |
| id | doaj-art-ccc44ee1d96944c8acec94b8a5e4aac4 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-08-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-ccc44ee1d96944c8acec94b8a5e4aac42025-08-20T03:13:59ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-08-01270411812410.15938/j.jhust.2022.04.015Deep Learning Method for Bearing Fault DiagnosisLIU Xiu0MA Shan-tao1XIE Yi-ning2HE Yong-jun3School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaCollege of Mechanical and Electrical Engineering, The Northeast Forestry University, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaIn recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. After testing on multiple data sets, and comparing with existing methods, experiments show that the method in this paper has significant performance in accuracy, noise robustness, and fault recognition rate under variable load conditions.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2124fault diagnosisconvolutional neural networklong and short-term memory networkmulti-scale feature extractionattention mechanism |
| spellingShingle | LIU Xiu MA Shan-tao XIE Yi-ning HE Yong-jun Deep Learning Method for Bearing Fault Diagnosis Journal of Harbin University of Science and Technology fault diagnosis convolutional neural network long and short-term memory network multi-scale feature extraction attention mechanism |
| title | Deep Learning Method for Bearing Fault Diagnosis |
| title_full | Deep Learning Method for Bearing Fault Diagnosis |
| title_fullStr | Deep Learning Method for Bearing Fault Diagnosis |
| title_full_unstemmed | Deep Learning Method for Bearing Fault Diagnosis |
| title_short | Deep Learning Method for Bearing Fault Diagnosis |
| title_sort | deep learning method for bearing fault diagnosis |
| topic | fault diagnosis convolutional neural network long and short-term memory network multi-scale feature extraction attention mechanism |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2124 |
| work_keys_str_mv | AT liuxiu deeplearningmethodforbearingfaultdiagnosis AT mashantao deeplearningmethodforbearingfaultdiagnosis AT xieyining deeplearningmethodforbearingfaultdiagnosis AT heyongjun deeplearningmethodforbearingfaultdiagnosis |