Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy
The fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosis based on frequency-domain energy feature reconst...
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| Format: | Article |
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MDPI AG
2019-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/9/865 |
| _version_ | 1811299478050701312 |
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| author | Xiaochao Wang Zhenggang Lu Juyao Wei Yuan Zhang |
| author_facet | Xiaochao Wang Zhenggang Lu Juyao Wei Yuan Zhang |
| author_sort | Xiaochao Wang |
| collection | DOAJ |
| description | The fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosis based on frequency-domain energy feature reconstruction (EFR) and composite multiscale permutation entropy (CMPE). First, a wavelet packet transform (WPT) is applied to decompose the vibration signals into multiple frequency bands. Then, considering that the bearing-localized defects cause the axle-box bearing system to resonate at a high frequency, which will lead to uneven energy distribution of the signal in the frequency domain, the energy factors of each frequency band are calculated by an energy feature extraction algorithm, from which the frequency band with maximum energy factor (which contains abundant fault information) is reconstructed to the time-domain signal. Next, the complexity of the reconstructed signals is calculated by CMPE as fault feature vectors. Finally, the feature vectors are input into a medium Gaussian support vector machine (MG-SVM) for bearing condition classification. The proposed method is validated by a public bearing data set and a wheelset-bearing system test bench data set. The experimental results indicate that the proposed method can effectively extract bearing fault features and provides a new solution for condition monitoring and fault diagnosis of rail vehicle axle-box bearings. |
| first_indexed | 2024-04-13T06:36:34Z |
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| id | doaj.art-76c80767b6f64383b86bfb53853652a2 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:36:34Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-76c80767b6f64383b86bfb53853652a22022-12-22T02:57:54ZengMDPI AGEntropy1099-43002019-09-0121986510.3390/e21090865e21090865Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation EntropyXiaochao Wang0Zhenggang Lu1Juyao Wei2Yuan Zhang3Institute of Rail Transit, Tongji University, Shanghai 201804, ChinaInstitute of Rail Transit, Tongji University, Shanghai 201804, ChinaInstitute of Rail Transit, Tongji University, Shanghai 201804, ChinaInstitute of Rail Transit, Tongji University, Shanghai 201804, ChinaThe fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosis based on frequency-domain energy feature reconstruction (EFR) and composite multiscale permutation entropy (CMPE). First, a wavelet packet transform (WPT) is applied to decompose the vibration signals into multiple frequency bands. Then, considering that the bearing-localized defects cause the axle-box bearing system to resonate at a high frequency, which will lead to uneven energy distribution of the signal in the frequency domain, the energy factors of each frequency band are calculated by an energy feature extraction algorithm, from which the frequency band with maximum energy factor (which contains abundant fault information) is reconstructed to the time-domain signal. Next, the complexity of the reconstructed signals is calculated by CMPE as fault feature vectors. Finally, the feature vectors are input into a medium Gaussian support vector machine (MG-SVM) for bearing condition classification. The proposed method is validated by a public bearing data set and a wheelset-bearing system test bench data set. The experimental results indicate that the proposed method can effectively extract bearing fault features and provides a new solution for condition monitoring and fault diagnosis of rail vehicle axle-box bearings.https://www.mdpi.com/1099-4300/21/9/865axle-box bearing of rail vehiclewavelet packet transformenergy feature reconstructioncomposite multiscale permutation entropyMG-SVMfault diagnosis |
| spellingShingle | Xiaochao Wang Zhenggang Lu Juyao Wei Yuan Zhang Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy Entropy axle-box bearing of rail vehicle wavelet packet transform energy feature reconstruction composite multiscale permutation entropy MG-SVM fault diagnosis |
| title | Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy |
| title_full | Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy |
| title_fullStr | Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy |
| title_full_unstemmed | Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy |
| title_short | Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy |
| title_sort | fault diagnosis for rail vehicle axle box bearings based on energy feature reconstruction and composite multiscale permutation entropy |
| topic | axle-box bearing of rail vehicle wavelet packet transform energy feature reconstruction composite multiscale permutation entropy MG-SVM fault diagnosis |
| url | https://www.mdpi.com/1099-4300/21/9/865 |
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