Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy
Most existing fault diagnosis methods for rolling bearings are single-stage; these methods can only judge the fault type but cannot detect the existence of a fault. Moreover, the uncertainty in pattern recognition may lead to misclassification of healthy bearings as faulty ones. This paper proposes...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9789190/ |
| _version_ | 1818536058986430464 |
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| author | Tao Han Jian-Cheng Gong Xiao-Qiang Yang Li-Zhou An |
| author_facet | Tao Han Jian-Cheng Gong Xiao-Qiang Yang Li-Zhou An |
| author_sort | Tao Han |
| collection | DOAJ |
| description | Most existing fault diagnosis methods for rolling bearings are single-stage; these methods can only judge the fault type but cannot detect the existence of a fault. Moreover, the uncertainty in pattern recognition may lead to misclassification of healthy bearings as faulty ones. This paper proposes a multistage fault detection scheme for rolling bearings. In the first stage, the sensitivity of the range entropy to bearing failure is used to define a threshold, based on which the health status of the bearing is judged. If the unknown bearing is judged to be faulty, the next stage is implemented. In the second stage, a fault feature extraction method based on dual-tree complex wavelet packet transform (DTCWPT), time-shifted multiscale range entropy (TSMRE), and t-distributed stochastic neighbor embedding (t-SNE) is proposed, and a random forest (RF) discriminator is used for fault classification. To achieve the desired performance of fault classification, a new coarsening approach for complexity measurement called TSMRE is developed on the basis of the range entropy (RE). First, the RE value of each time-shifted coarse-grained time series is calculated, and the TSMRE is obtained by averaging the entropy values. The TSMRE improves the coarse-graining processing of the MRE and enhances the stability and reliability of the algorithm. In addition, it can obtain more information from short time series using the time-shifted coarse-grained technology. Therefore, it is less dependent on the length of the original time series. Two sets of rolling bearing data are used for this experiment. The fault recognition rate of each category of samples is 100%. Therefore, the proposed multistage fault diagnosis method can pre-screen healthy bearings and accurately identify the failure types of faulty bearings. |
| first_indexed | 2024-12-11T18:33:05Z |
| format | Article |
| id | doaj.art-f98349d16f20426aa07c94f9aee03502 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T18:33:05Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f98349d16f20426aa07c94f9aee035022022-12-22T00:54:51ZengIEEEIEEE Access2169-35362022-01-0110593085932610.1109/ACCESS.2022.31803389789190Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range EntropyTao Han0https://orcid.org/0000-0002-3042-5195Jian-Cheng Gong1Xiao-Qiang Yang2https://orcid.org/0000-0003-1266-3509Li-Zhou An3College of Field Engineering, Army Engineering University of PLA, Nanjing, ChinaCollege of Field Engineering, Army Engineering University of PLA, Nanjing, ChinaCollege of Field Engineering, Army Engineering University of PLA, Nanjing, ChinaCollege of Field Engineering, Army Engineering University of PLA, Nanjing, ChinaMost existing fault diagnosis methods for rolling bearings are single-stage; these methods can only judge the fault type but cannot detect the existence of a fault. Moreover, the uncertainty in pattern recognition may lead to misclassification of healthy bearings as faulty ones. This paper proposes a multistage fault detection scheme for rolling bearings. In the first stage, the sensitivity of the range entropy to bearing failure is used to define a threshold, based on which the health status of the bearing is judged. If the unknown bearing is judged to be faulty, the next stage is implemented. In the second stage, a fault feature extraction method based on dual-tree complex wavelet packet transform (DTCWPT), time-shifted multiscale range entropy (TSMRE), and t-distributed stochastic neighbor embedding (t-SNE) is proposed, and a random forest (RF) discriminator is used for fault classification. To achieve the desired performance of fault classification, a new coarsening approach for complexity measurement called TSMRE is developed on the basis of the range entropy (RE). First, the RE value of each time-shifted coarse-grained time series is calculated, and the TSMRE is obtained by averaging the entropy values. The TSMRE improves the coarse-graining processing of the MRE and enhances the stability and reliability of the algorithm. In addition, it can obtain more information from short time series using the time-shifted coarse-grained technology. Therefore, it is less dependent on the length of the original time series. Two sets of rolling bearing data are used for this experiment. The fault recognition rate of each category of samples is 100%. Therefore, the proposed multistage fault diagnosis method can pre-screen healthy bearings and accurately identify the failure types of faulty bearings.https://ieeexplore.ieee.org/document/9789190/Dual-tree complex wavelet packet transformtime-shifted multiscale range entropyt-distributed stochastic neighbor embeddingrandom forestrolling bearingtwo-stage fault diagnosis |
| spellingShingle | Tao Han Jian-Cheng Gong Xiao-Qiang Yang Li-Zhou An Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy IEEE Access Dual-tree complex wavelet packet transform time-shifted multiscale range entropy t-distributed stochastic neighbor embedding random forest rolling bearing two-stage fault diagnosis |
| title | Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy |
| title_full | Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy |
| title_fullStr | Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy |
| title_full_unstemmed | Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy |
| title_short | Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy |
| title_sort | fault diagnosis of rolling bearings using dual tree complex wavelet packet transform and time shifted multiscale range entropy |
| topic | Dual-tree complex wavelet packet transform time-shifted multiscale range entropy t-distributed stochastic neighbor embedding random forest rolling bearing two-stage fault diagnosis |
| url | https://ieeexplore.ieee.org/document/9789190/ |
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