Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM
To solve the problems of low accuracy, difficult diagnosis and long time consuming of the current fan bearing fault diagnosis, an improved grey wolf optimization (IGWO) algorithm and a support vector machine (SVM) fault diagnosis method are proposed. In order to accurately extract the fault features...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2023-09-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.09.022 |
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| author | Liu Jinyan Abulizi Maimaitireyimu Xiang Zhicheng Xie Lirong |
| author_facet | Liu Jinyan Abulizi Maimaitireyimu Xiang Zhicheng Xie Lirong |
| author_sort | Liu Jinyan |
| collection | DOAJ |
| description | To solve the problems of low accuracy, difficult diagnosis and long time consuming of the current fan bearing fault diagnosis, an improved grey wolf optimization (IGWO) algorithm and a support vector machine (SVM) fault diagnosis method are proposed. In order to accurately extract the fault features, the wavelet packet decomposition method in the time-frequency domain analysis is used to extract the fault vibration signal. Take the wavelet packet decomposition energy of the eight frequency bands as the fault feature， the eigenvectors are constructed. Then, the fault model of SVM is established and the parameters of the SVM model are optimized by the IGWO algorithm, which avoids the defects of local optimum and slow convergence. According to the experimental analysis result, the average fault recognition rate of the IGWO algorithm is up to 99.3%, and it can identify fault types more quickly, more efficiently and more accurately, which provides a good support for the development of fault diagnosis. |
| format | Article |
| id | doaj-art-9aa670ceb3874f0f911ec7c9e483d38a |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-9aa670ceb3874f0f911ec7c9e483d38a2025-01-10T14:58:54ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392023-09-014716016941952016Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVMLiu JinyanAbulizi MaimaitireyimuXiang ZhichengXie LirongTo solve the problems of low accuracy, difficult diagnosis and long time consuming of the current fan bearing fault diagnosis, an improved grey wolf optimization (IGWO) algorithm and a support vector machine (SVM) fault diagnosis method are proposed. In order to accurately extract the fault features, the wavelet packet decomposition method in the time-frequency domain analysis is used to extract the fault vibration signal. Take the wavelet packet decomposition energy of the eight frequency bands as the fault feature， the eigenvectors are constructed. Then, the fault model of SVM is established and the parameters of the SVM model are optimized by the IGWO algorithm, which avoids the defects of local optimum and slow convergence. According to the experimental analysis result, the average fault recognition rate of the IGWO algorithm is up to 99.3%, and it can identify fault types more quickly, more efficiently and more accurately, which provides a good support for the development of fault diagnosis.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.09.022Support vector machineImproved grey wolf optimization algorithmWavelet packet decompositionFeature extractionFault classification |
| spellingShingle | Liu Jinyan Abulizi Maimaitireyimu Xiang Zhicheng Xie Lirong Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM Jixie chuandong Support vector machine Improved grey wolf optimization algorithm Wavelet packet decomposition Feature extraction Fault classification |
| title | Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM |
| title_full | Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM |
| title_fullStr | Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM |
| title_full_unstemmed | Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM |
| title_short | Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM |
| title_sort | fault diagnosis of fan bearings based on an improved grey wolf optimization algorithm and svm |
| topic | Support vector machine Improved grey wolf optimization algorithm Wavelet packet decomposition Feature extraction Fault classification |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.09.022 |
| work_keys_str_mv | AT liujinyan faultdiagnosisoffanbearingsbasedonanimprovedgreywolfoptimizationalgorithmandsvm AT abulizimaimaitireyimu faultdiagnosisoffanbearingsbasedonanimprovedgreywolfoptimizationalgorithmandsvm AT xiangzhicheng faultdiagnosisoffanbearingsbasedonanimprovedgreywolfoptimizationalgorithmandsvm AT xielirong faultdiagnosisoffanbearingsbasedonanimprovedgreywolfoptimizationalgorithmandsvm |