Research on bearing fault diagnosis based on a multimodal method
As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper intro...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024338 |
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| _version_ | 1832591038503452672 |
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| author | Hao Chen Shengjie Li Xi Lu Qiong Zhang Jixining Zhu Jiaxin Lu |
| author_facet | Hao Chen Shengjie Li Xi Lu Qiong Zhang Jixining Zhu Jiaxin Lu |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM). Specifically, the method first employs continuous wavelet transform to generate time-frequency images, capturing local and global features of the signal at different scales. Contrast enhancement techniques are then used to improve the visual quality of these images. Next, features are extracted from the time-frequency images using a visual geometry group network to obtain deep features of image modalities. In parallel, 13 key features are extracted from the original vibration data in the time-frequency domain. Convolutional neural networks are then employed for deep feature extraction. Experimental results demonstrate that MTSF-FM achieves accuracies of 98.5% and 95.1% on two public datasets. These findings highlight the effectiveness of MTSF-FM in analyzing complex vibration data and propose a novel method for bearing fault diagnosis. |
| format | Article |
| id | doaj-art-7dc4183696a746a2bdba4bf533798ce1 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-7dc4183696a746a2bdba4bf533798ce12025-01-23T05:05:30ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-12-0121127688770610.3934/mbe.2024338Research on bearing fault diagnosis based on a multimodal methodHao Chen0Shengjie Li1Xi Lu2Qiong Zhang3Jixining Zhu4Jiaxin Lu5School of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, ChinaSchool of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, ChinaSchool of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, ChinaSchool of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, ChinaSchool of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, ChinaSchool of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, ChinaAs an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM). Specifically, the method first employs continuous wavelet transform to generate time-frequency images, capturing local and global features of the signal at different scales. Contrast enhancement techniques are then used to improve the visual quality of these images. Next, features are extracted from the time-frequency images using a visual geometry group network to obtain deep features of image modalities. In parallel, 13 key features are extracted from the original vibration data in the time-frequency domain. Convolutional neural networks are then employed for deep feature extraction. Experimental results demonstrate that MTSF-FM achieves accuracies of 98.5% and 95.1% on two public datasets. These findings highlight the effectiveness of MTSF-FM in analyzing complex vibration data and propose a novel method for bearing fault diagnosis.https://www.aimspress.com/article/doi/10.3934/mbe.2024338bearing fault diagnosisnon-stationarynonlineartime-frequencydeep feature |
| spellingShingle | Hao Chen Shengjie Li Xi Lu Qiong Zhang Jixining Zhu Jiaxin Lu Research on bearing fault diagnosis based on a multimodal method Mathematical Biosciences and Engineering bearing fault diagnosis non-stationary nonlinear time-frequency deep feature |
| title | Research on bearing fault diagnosis based on a multimodal method |
| title_full | Research on bearing fault diagnosis based on a multimodal method |
| title_fullStr | Research on bearing fault diagnosis based on a multimodal method |
| title_full_unstemmed | Research on bearing fault diagnosis based on a multimodal method |
| title_short | Research on bearing fault diagnosis based on a multimodal method |
| title_sort | research on bearing fault diagnosis based on a multimodal method |
| topic | bearing fault diagnosis non-stationary nonlinear time-frequency deep feature |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024338 |
| work_keys_str_mv | AT haochen researchonbearingfaultdiagnosisbasedonamultimodalmethod AT shengjieli researchonbearingfaultdiagnosisbasedonamultimodalmethod AT xilu researchonbearingfaultdiagnosisbasedonamultimodalmethod AT qiongzhang researchonbearingfaultdiagnosisbasedonamultimodalmethod AT jixiningzhu researchonbearingfaultdiagnosisbasedonamultimodalmethod AT jiaxinlu researchonbearingfaultdiagnosisbasedonamultimodalmethod |