Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis
Aiming at the problem that the time-frequency image of bearing fault characteristics is relatively weak and difficult to identify. This paper presents a time-frequency analysis method of local maximum synchrosqueezing transform based on image enhancement. Firstly, the instantaneous frequency of the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9770845/ |
| _version_ | 1811256429886046208 |
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| author | Zhiqiang Zeng Rui Zhang Wenan Cai Yanfeng Li |
| author_facet | Zhiqiang Zeng Rui Zhang Wenan Cai Yanfeng Li |
| author_sort | Zhiqiang Zeng |
| collection | DOAJ |
| description | Aiming at the problem that the time-frequency image of bearing fault characteristics is relatively weak and difficult to identify. This paper presents a time-frequency analysis method of local maximum synchrosqueezing transform based on image enhancement. Firstly, the instantaneous frequency of the collected vibration signal is obtained through local maximum synchrosqueezing transformation. Secondly, a local histogram cropping equalization image enhancement algorithm is proposed, which is used to obtain time-frequency images with clearer textures. Then, in order to extract fault features from the enhanced instantaneous frequency (IF) image, A new neural network is proposed. The network consists of Multi-size convolution kernel module, Dual-channel pooling layer and Cross Stage Partial Network (MDCNet). Finally, the fault signal was collected on the bearing fault test bench for prediction, and the accuracy rate reached 99.7%. And compared with AlexNet, VGG-16, Resnet and other methods. The results show that the method can meet the needs of actual engineering. |
| first_indexed | 2024-04-12T17:40:05Z |
| format | Article |
| id | doaj.art-fc0fb51e29584cad8136ea38298b7386 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T17:40:05Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fc0fb51e29584cad8136ea38298b73862022-12-22T03:22:50ZengIEEEIEEE Access2169-35362022-01-0110492514926410.1109/ACCESS.2022.31733269770845Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault DiagnosisZhiqiang Zeng0Rui Zhang1https://orcid.org/0000-0003-1162-7688Wenan Cai2https://orcid.org/0000-0003-4956-3552Yanfeng Li3https://orcid.org/0000-0002-5884-5060School of Mechanical Engineering, North University of China, Taiyuan, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan, ChinaSchool of Mechanical Engineering, Jinzhong University, Taiyuan, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan, ChinaAiming at the problem that the time-frequency image of bearing fault characteristics is relatively weak and difficult to identify. This paper presents a time-frequency analysis method of local maximum synchrosqueezing transform based on image enhancement. Firstly, the instantaneous frequency of the collected vibration signal is obtained through local maximum synchrosqueezing transformation. Secondly, a local histogram cropping equalization image enhancement algorithm is proposed, which is used to obtain time-frequency images with clearer textures. Then, in order to extract fault features from the enhanced instantaneous frequency (IF) image, A new neural network is proposed. The network consists of Multi-size convolution kernel module, Dual-channel pooling layer and Cross Stage Partial Network (MDCNet). Finally, the fault signal was collected on the bearing fault test bench for prediction, and the accuracy rate reached 99.7%. And compared with AlexNet, VGG-16, Resnet and other methods. The results show that the method can meet the needs of actual engineering.https://ieeexplore.ieee.org/document/9770845/Fault diagnosisimage enhancementinstantaneous frequency |
| spellingShingle | Zhiqiang Zeng Rui Zhang Wenan Cai Yanfeng Li Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis IEEE Access Fault diagnosis image enhancement instantaneous frequency |
| title | Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis |
| title_full | Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis |
| title_fullStr | Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis |
| title_full_unstemmed | Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis |
| title_short | Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis |
| title_sort | application of local histogram clipping equalization image enhancement in bearing fault diagnosis |
| topic | Fault diagnosis image enhancement instantaneous frequency |
| url | https://ieeexplore.ieee.org/document/9770845/ |
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