Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis
This paper proposes a new approach combining autoregressive (AR) model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signa...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.3233/SAV-2010-0572 |
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| _version_ | 1832560293492817920 |
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| author | Lingli Jiang Yilun Liu Xuejun Li Anhua Chen |
| author_facet | Lingli Jiang Yilun Liu Xuejun Li Anhua Chen |
| author_sort | Lingli Jiang |
| collection | DOAJ |
| description | This paper proposes a new approach combining autoregressive (AR) model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment. |
| format | Article |
| id | doaj-art-025c5ef8a39a44be922c7191be0ebd81 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-025c5ef8a39a44be922c7191be0ebd812025-02-03T01:27:49ZengWileyShock and Vibration1070-96221875-92032011-01-01181-212713710.3233/SAV-2010-0572Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster AnalysisLingli Jiang0Yilun Liu1Xuejun Li2Anhua Chen3College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaCollege of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaThis paper proposes a new approach combining autoregressive (AR) model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.http://dx.doi.org/10.3233/SAV-2010-0572 |
| spellingShingle | Lingli Jiang Yilun Liu Xuejun Li Anhua Chen Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis Shock and Vibration |
| title | Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis |
| title_full | Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis |
| title_fullStr | Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis |
| title_full_unstemmed | Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis |
| title_short | Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis |
| title_sort | degradation assessment and fault diagnosis for roller bearing based on ar model and fuzzy cluster analysis |
| url | http://dx.doi.org/10.3233/SAV-2010-0572 |
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