A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis
With the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/2593973 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849412285278191616 |
|---|---|
| author | Yan Huang Jianhui Lin Zechao Liu Chenguang Huang |
| author_facet | Yan Huang Jianhui Lin Zechao Liu Chenguang Huang |
| author_sort | Yan Huang |
| collection | DOAJ |
| description | With the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However, the choices of operators and structure elements (SE) are numerous and complicated to determine the best MF solution for different bearing faulty signals. In this paper, the particle swarm optimization (PSO) was introduced to optimize the effect of MF among several classical MF operators and different SE parameters. The proposed method applied PSO to select the best MF result with respect to the fitness function adopting kurtosis. A set of bearing signals with additional interference of wheel-track excitement are analyzed to verify the effectiveness of the proposed method. The results demonstrated that the proposed method is capable of obtaining the optimized solution and accurately extracting the fault information. Furthermore, the shaft rotation frequency and wheel-track interference were reduced by the proposed method. |
| format | Article |
| id | doaj-art-63b3c8db4d5047ea905fa68f4750b3f9 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-63b3c8db4d5047ea905fa68f4750b3f92025-08-20T03:34:29ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/25939732593973A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault DiagnosisYan Huang0Jianhui Lin1Zechao Liu2Chenguang Huang3State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaWith the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However, the choices of operators and structure elements (SE) are numerous and complicated to determine the best MF solution for different bearing faulty signals. In this paper, the particle swarm optimization (PSO) was introduced to optimize the effect of MF among several classical MF operators and different SE parameters. The proposed method applied PSO to select the best MF result with respect to the fitness function adopting kurtosis. A set of bearing signals with additional interference of wheel-track excitement are analyzed to verify the effectiveness of the proposed method. The results demonstrated that the proposed method is capable of obtaining the optimized solution and accurately extracting the fault information. Furthermore, the shaft rotation frequency and wheel-track interference were reduced by the proposed method.http://dx.doi.org/10.1155/2019/2593973 |
| spellingShingle | Yan Huang Jianhui Lin Zechao Liu Chenguang Huang A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis Shock and Vibration |
| title | A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis |
| title_full | A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis |
| title_fullStr | A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis |
| title_full_unstemmed | A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis |
| title_short | A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis |
| title_sort | morphological filtering method based on particle swarm optimization for railway vehicle bearing fault diagnosis |
| url | http://dx.doi.org/10.1155/2019/2593973 |
| work_keys_str_mv | AT yanhuang amorphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis AT jianhuilin amorphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis AT zechaoliu amorphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis AT chenguanghuang amorphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis AT yanhuang morphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis AT jianhuilin morphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis AT zechaoliu morphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis AT chenguanghuang morphologicalfilteringmethodbasedonparticleswarmoptimizationforrailwayvehiclebearingfaultdiagnosis |