ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVM
To monitor the health of rolling bearing, the vibration signals are always used for fault diagnosis. However, the non-linear and non-stationary characteristics of vibration signals have not been solved in current methods. In this work, an intelligent fault diagnosis method is proposed, which is a se...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2019-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.04.007 |
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| _version_ | 1841535704576819200 |
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| author | ZHANG LuYang QIN Bo ZHAO WenJun LI Hong ZHANG JianQiang WANG JianGuao |
| author_facet | ZHANG LuYang QIN Bo ZHAO WenJun LI Hong ZHANG JianQiang WANG JianGuao |
| author_sort | ZHANG LuYang |
| collection | DOAJ |
| description | To monitor the health of rolling bearing, the vibration signals are always used for fault diagnosis. However, the non-linear and non-stationary characteristics of vibration signals have not been solved in current methods. In this work, an intelligent fault diagnosis method is proposed, which is a sequential combinations of variational mode decomposition（VMD）, Kurtogram, and artificial fish algorithm（AFSA）. To begin, original vibration signals are decomposed into intrinsic mode functions（IMFs） using VMD, among which the most effective fault information is selected based on the Kurtogram algorithm and the rules of maximum correlation coefficients. Then the feature vectors are identified using the morphological entropy and energy entropy of the above IMFs. Next, two crucial tunable parameters, penalty coefficient C and Gaussian kernel width coefficient σ are optimized through AFSA algorithm. At last, the fault diagnosis model is developed based on AFSA-SVM algorithm, in which the extracted fault features are employed as inputs. The experimental results show that the proposed method accurately identifies fault features of the original signal. It has also improved model learning efficiency and classification accuracy. |
| format | Article |
| id | doaj-art-8197be22813640bbbd5d3540eead931d |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2019-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-8197be22813640bbbd5d3540eead931d2025-01-15T02:29:30ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692019-01-014180781330605258ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVMZHANG LuYangQIN BoZHAO WenJunLI HongZHANG JianQiangWANG JianGuaoTo monitor the health of rolling bearing, the vibration signals are always used for fault diagnosis. However, the non-linear and non-stationary characteristics of vibration signals have not been solved in current methods. In this work, an intelligent fault diagnosis method is proposed, which is a sequential combinations of variational mode decomposition（VMD）, Kurtogram, and artificial fish algorithm（AFSA）. To begin, original vibration signals are decomposed into intrinsic mode functions（IMFs） using VMD, among which the most effective fault information is selected based on the Kurtogram algorithm and the rules of maximum correlation coefficients. Then the feature vectors are identified using the morphological entropy and energy entropy of the above IMFs. Next, two crucial tunable parameters, penalty coefficient C and Gaussian kernel width coefficient σ are optimized through AFSA algorithm. At last, the fault diagnosis model is developed based on AFSA-SVM algorithm, in which the extracted fault features are employed as inputs. The experimental results show that the proposed method accurately identifies fault features of the original signal. It has also improved model learning efficiency and classification accuracy.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.04.007Variational mode decompositionKurtogramArtificial fish swarm algorithmOptimal combination of kernel function parameters |
| spellingShingle | ZHANG LuYang QIN Bo ZHAO WenJun LI Hong ZHANG JianQiang WANG JianGuao ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVM Jixie qiangdu Variational mode decomposition Kurtogram Artificial fish swarm algorithm Optimal combination of kernel function parameters |
| title | ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVM |
| title_full | ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVM |
| title_fullStr | ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVM |
| title_full_unstemmed | ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVM |
| title_short | ROLLING BEARING FAULT DIAGNOSIS BASED TWO TYPES OF FEATURES AND AFSA IMPROVED SVM |
| title_sort | rolling bearing fault diagnosis based two types of features and afsa improved svm |
| topic | Variational mode decomposition Kurtogram Artificial fish swarm algorithm Optimal combination of kernel function parameters |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.04.007 |
| work_keys_str_mv | AT zhangluyang rollingbearingfaultdiagnosisbasedtwotypesoffeaturesandafsaimprovedsvm AT qinbo rollingbearingfaultdiagnosisbasedtwotypesoffeaturesandafsaimprovedsvm AT zhaowenjun rollingbearingfaultdiagnosisbasedtwotypesoffeaturesandafsaimprovedsvm AT lihong rollingbearingfaultdiagnosisbasedtwotypesoffeaturesandafsaimprovedsvm AT zhangjianqiang rollingbearingfaultdiagnosisbasedtwotypesoffeaturesandafsaimprovedsvm AT wangjianguao rollingbearingfaultdiagnosisbasedtwotypesoffeaturesandafsaimprovedsvm |