A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM
This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the orig...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2014/825825 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562741762588672 |
|---|---|
| author | HungLinh Ao Junsheng Cheng Kenli Li Tung Khac Truong |
| author_facet | HungLinh Ao Junsheng Cheng Kenli Li Tung Khac Truong |
| author_sort | HungLinh Ao |
| collection | DOAJ |
| description | This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time. |
| format | Article |
| id | doaj-art-703a4f4fad914bfe9b916acf7ca72076 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-703a4f4fad914bfe9b916acf7ca720762025-02-03T01:21:53ZengWileyShock and Vibration1070-96221875-92032014-01-01201410.1155/2014/825825825825A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVMHungLinh Ao0Junsheng Cheng1Kenli Li2Tung Khac Truong3State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, ChinaCollege of Information Science and Engineering, Hunan University, National Supercomputing Centre in Changsha, Changsha 410082, ChinaCollege of Information Science and Engineering, Hunan University, National Supercomputing Centre in Changsha, Changsha 410082, ChinaThis study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.http://dx.doi.org/10.1155/2014/825825 |
| spellingShingle | HungLinh Ao Junsheng Cheng Kenli Li Tung Khac Truong A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM Shock and Vibration |
| title | A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM |
| title_full | A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM |
| title_fullStr | A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM |
| title_full_unstemmed | A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM |
| title_short | A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM |
| title_sort | roller bearing fault diagnosis method based on lcd energy entropy and acroa svm |
| url | http://dx.doi.org/10.1155/2014/825825 |
| work_keys_str_mv | AT hunglinhao arollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm AT junshengcheng arollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm AT kenlili arollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm AT tungkhactruong arollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm AT hunglinhao rollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm AT junshengcheng rollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm AT kenlili rollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm AT tungkhactruong rollerbearingfaultdiagnosismethodbasedonlcdenergyentropyandacroasvm |