Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis
Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. H...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2017/2384184 |
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| author | Jun Shuai Changqing Shen Zhongkui Zhu |
| author_facet | Jun Shuai Changqing Shen Zhongkui Zhu |
| author_sort | Jun Shuai |
| collection | DOAJ |
| description | Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise. |
| format | Article |
| id | doaj-art-d4d7378a019a4fc7be8d6eb78558d027 |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-d4d7378a019a4fc7be8d6eb78558d0272025-02-03T01:27:34ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342017-01-01201710.1155/2017/23841842384184Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault DiagnosisJun Shuai0Changqing Shen1Zhongkui Zhu2School of Urban Rail Transportation, Soochow University, Suzhou 215131, ChinaSchool of Urban Rail Transportation, Soochow University, Suzhou 215131, ChinaSchool of Urban Rail Transportation, Soochow University, Suzhou 215131, ChinaNumerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise.http://dx.doi.org/10.1155/2017/2384184 |
| spellingShingle | Jun Shuai Changqing Shen Zhongkui Zhu Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis International Journal of Rotating Machinery |
| title | Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis |
| title_full | Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis |
| title_fullStr | Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis |
| title_full_unstemmed | Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis |
| title_short | Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis |
| title_sort | adaptive morphological feature extraction and support vector regressive classification for bearing fault diagnosis |
| url | http://dx.doi.org/10.1155/2017/2384184 |
| work_keys_str_mv | AT junshuai adaptivemorphologicalfeatureextractionandsupportvectorregressiveclassificationforbearingfaultdiagnosis AT changqingshen adaptivemorphologicalfeatureextractionandsupportvectorregressiveclassificationforbearingfaultdiagnosis AT zhongkuizhu adaptivemorphologicalfeatureextractionandsupportvectorregressiveclassificationforbearingfaultdiagnosis |