Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM
Abstract Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contam...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | IET Science, Measurement & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/smt2.12149 |
| _version_ | 1827878158581366784 |
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| author | Li Liu Zijin Liu Xuefei Qian |
| author_facet | Li Liu Zijin Liu Xuefei Qian |
| author_sort | Li Liu |
| collection | DOAJ |
| description | Abstract Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non‐linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO‐LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO‐LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis. |
| first_indexed | 2024-03-12T17:47:21Z |
| format | Article |
| id | doaj.art-332187070301423f9ef1911bb7ec8ce6 |
| institution | Directory Open Access Journal |
| issn | 1751-8822 1751-8830 |
| language | English |
| last_indexed | 2024-03-12T17:47:21Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Science, Measurement & Technology |
| spelling | doaj.art-332187070301423f9ef1911bb7ec8ce62023-08-03T12:39:24ZengWileyIET Science, Measurement & Technology1751-88221751-88302023-08-0117624325610.1049/smt2.12149Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVMLi Liu0Zijin Liu1Xuefei Qian2School of Mechanical Engineering Shenyang Jianzhu University Shenyang ChinaSchool of Mechanical Engineering Shenyang Jianzhu University Shenyang ChinaEngineering DepartmentChina Petroleum Pipeline Bureau Engineering Co., LtdLangfangChinaAbstract Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non‐linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO‐LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO‐LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.https://doi.org/10.1049/smt2.12149fault diagnosisfeature extractionleast squares support vector machinemultiscale permutation entropyrolling bearing |
| spellingShingle | Li Liu Zijin Liu Xuefei Qian Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM IET Science, Measurement & Technology fault diagnosis feature extraction least squares support vector machine multiscale permutation entropy rolling bearing |
| title | Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM |
| title_full | Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM |
| title_fullStr | Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM |
| title_full_unstemmed | Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM |
| title_short | Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM |
| title_sort | rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and gwo lssvm |
| topic | fault diagnosis feature extraction least squares support vector machine multiscale permutation entropy rolling bearing |
| url | https://doi.org/10.1049/smt2.12149 |
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