Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn
Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/6321785 |
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| _version_ | 1832554568131543040 |
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| author | Fengtao Wang Chenxi Liu Wensheng Su Zhigang Xue Qingkai Han Hongkun Li |
| author_facet | Fengtao Wang Chenxi Liu Wensheng Su Zhigang Xue Qingkai Han Hongkun Li |
| author_sort | Fengtao Wang |
| collection | DOAJ |
| description | Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-5e49580e0f9a494698ca2f1ca83b4b79 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-5e49580e0f9a494698ca2f1ca83b4b792025-02-03T05:51:09ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/63217856321785Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEnFengtao Wang0Chenxi Liu1Wensheng Su2Zhigang Xue3Qingkai Han4Hongkun Li5Institute of Vibration Engineering, Dalian University of Technology, Dalian 116024, ChinaInstitute of Vibration Engineering, Dalian University of Technology, Dalian 116024, ChinaJiangsu Province Special Equipment Safety Supervision Inspection Institute, Branch of Wuxi, Wuxi 214071, ChinaJiangsu Province Special Equipment Safety Supervision Inspection Institute, Branch of Wuxi, Wuxi 214071, ChinaInstitute of Vibration Engineering, Dalian University of Technology, Dalian 116024, ChinaInstitute of Vibration Engineering, Dalian University of Technology, Dalian 116024, ChinaLarge-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method.http://dx.doi.org/10.1155/2018/6321785 |
| spellingShingle | Fengtao Wang Chenxi Liu Wensheng Su Zhigang Xue Qingkai Han Hongkun Li Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn Shock and Vibration |
| title | Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn |
| title_full | Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn |
| title_fullStr | Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn |
| title_full_unstemmed | Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn |
| title_short | Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn |
| title_sort | combined failure diagnosis of slewing bearings based on mckd ceemd apen |
| url | http://dx.doi.org/10.1155/2018/6321785 |
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