A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions
To timely detect bearing operating condition, and accurately identify bearing fault type and fault severity, a novel multi-stage hybrid fault diagnosis strategy for rolling bearing is proposed in this paper, which mainly consists of three stages (i.e. fault initial detection, fault type recognition...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8815765/ |
| _version_ | 1819174455176331264 |
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| author | Xiaoan Yan Ying Liu Minping Jia Yinlong Zhu |
| author_facet | Xiaoan Yan Ying Liu Minping Jia Yinlong Zhu |
| author_sort | Xiaoan Yan |
| collection | DOAJ |
| description | To timely detect bearing operating condition, and accurately identify bearing fault type and fault severity, a novel multi-stage hybrid fault diagnosis strategy for rolling bearing is proposed in this paper, which mainly consists of three stages (i.e. fault initial detection, fault type recognition and fault severity assessment). Firstly, the procedure of permutation entropy (PE)-based fault initial detection is performed to estimate bearing operating condition. If the bearing fault exists, the next two stages are conducted for fault type recognition and fault severity assessment. Specifically, in the second and third stages, for each dataset under different fault conditions, hybrid-domain features including time-domain, frequency-domain and time-frequency domain are firstly extracted to establish high-dimensional feature space based on statistical analysis and variational mode decomposition (VMD). Then, locality preserving projection (LPP) is introduced to compress high-dimensional dataset into low-dimensional feature space which can reflect preferably intrinsic information of the raw signal and remove information redundancy embedded in hybrid-domain features. Finally, the obtained low-dimensional dataset is imported into Fuzzy C-means (FCM) clustering for recognizing bearing fault type and fault severity. The efficacy of the proposed approach is verified by experimental bearing data under different working conditions. The results indicate that our proposed method can both assess effectively bearing health status and recognize accurately bearing fault type and fault severity. In addition, our proposed approach has higher diagnosis precision than traditional single-stage diagnosis method mentioned in this paper. |
| first_indexed | 2024-12-22T20:39:14Z |
| format | Article |
| id | doaj.art-b5681e4e5e1d4551bfa6fd26b7dff791 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:39:14Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b5681e4e5e1d4551bfa6fd26b7dff7912022-12-21T18:13:22ZengIEEEIEEE Access2169-35362019-01-01713842613844110.1109/ACCESS.2019.29378288815765A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working ConditionsXiaoan Yan0https://orcid.org/0000-0001-6986-6943Ying Liu1Minping Jia2Yinlong Zhu3School of Mechatronics Engineering, Nanjing Forestry University, Nanjing, ChinaSchool of Mechatronics Engineering, Nanjing Forestry University, Nanjing, ChinaSchool of Mechanical Engineering, Southeast University, Nanjing, ChinaSchool of Mechatronics Engineering, Nanjing Forestry University, Nanjing, ChinaTo timely detect bearing operating condition, and accurately identify bearing fault type and fault severity, a novel multi-stage hybrid fault diagnosis strategy for rolling bearing is proposed in this paper, which mainly consists of three stages (i.e. fault initial detection, fault type recognition and fault severity assessment). Firstly, the procedure of permutation entropy (PE)-based fault initial detection is performed to estimate bearing operating condition. If the bearing fault exists, the next two stages are conducted for fault type recognition and fault severity assessment. Specifically, in the second and third stages, for each dataset under different fault conditions, hybrid-domain features including time-domain, frequency-domain and time-frequency domain are firstly extracted to establish high-dimensional feature space based on statistical analysis and variational mode decomposition (VMD). Then, locality preserving projection (LPP) is introduced to compress high-dimensional dataset into low-dimensional feature space which can reflect preferably intrinsic information of the raw signal and remove information redundancy embedded in hybrid-domain features. Finally, the obtained low-dimensional dataset is imported into Fuzzy C-means (FCM) clustering for recognizing bearing fault type and fault severity. The efficacy of the proposed approach is verified by experimental bearing data under different working conditions. The results indicate that our proposed method can both assess effectively bearing health status and recognize accurately bearing fault type and fault severity. In addition, our proposed approach has higher diagnosis precision than traditional single-stage diagnosis method mentioned in this paper.https://ieeexplore.ieee.org/document/8815765/Permutation entropyvariational mode decompositionlocality preserving projectionrolling bearingfault diagnosis |
| spellingShingle | Xiaoan Yan Ying Liu Minping Jia Yinlong Zhu A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions IEEE Access Permutation entropy variational mode decomposition locality preserving projection rolling bearing fault diagnosis |
| title | A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions |
| title_full | A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions |
| title_fullStr | A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions |
| title_full_unstemmed | A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions |
| title_short | A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions |
| title_sort | multi stage hybrid fault diagnosis approach for rolling element bearing under various working conditions |
| topic | Permutation entropy variational mode decomposition locality preserving projection rolling bearing fault diagnosis |
| url | https://ieeexplore.ieee.org/document/8815765/ |
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