Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis
Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimod...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/6/1792 |
| _version_ | 1797239399213170688 |
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| author | Zhenzhong Xu Xu Chen Yilin Li Jiangtao Xu |
| author_facet | Zhenzhong Xu Xu Chen Yilin Li Jiangtao Xu |
| author_sort | Zhenzhong Xu |
| collection | DOAJ |
| description | Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate. |
| first_indexed | 2024-04-24T17:50:55Z |
| format | Article |
| id | doaj.art-b924e4a1c1b74a79b732680367b6d5ff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:50:55Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b924e4a1c1b74a79b732680367b6d5ff2024-03-27T14:03:47ZengMDPI AGSensors1424-82202024-03-01246179210.3390/s24061792Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault DiagnosisZhenzhong Xu0Xu Chen1Yilin Li2Jiangtao Xu3College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Foreign Languages, Beijing Institute of Technology, Beijing 102488, ChinaCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, ChinaAiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate.https://www.mdpi.com/1424-8220/24/6/1792multimodal feature fusionmulti-sensorPCAResNetSVMensemble learning |
| spellingShingle | Zhenzhong Xu Xu Chen Yilin Li Jiangtao Xu Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis Sensors multimodal feature fusion multi-sensor PCA ResNet SVM ensemble learning |
| title | Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis |
| title_full | Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis |
| title_fullStr | Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis |
| title_full_unstemmed | Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis |
| title_short | Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis |
| title_sort | hybrid multimodal feature fusion with multi sensor for bearing fault diagnosis |
| topic | multimodal feature fusion multi-sensor PCA ResNet SVM ensemble learning |
| url | https://www.mdpi.com/1424-8220/24/6/1792 |
| work_keys_str_mv | AT zhenzhongxu hybridmultimodalfeaturefusionwithmultisensorforbearingfaultdiagnosis AT xuchen hybridmultimodalfeaturefusionwithmultisensorforbearingfaultdiagnosis AT yilinli hybridmultimodalfeaturefusionwithmultisensorforbearingfaultdiagnosis AT jiangtaoxu hybridmultimodalfeaturefusionwithmultisensorforbearingfaultdiagnosis |