LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information
Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wave...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/13/7/8679 |
| _version_ | 1798024545859272704 |
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| author | Zhongjie Shen Zhengjia He Xuefeng Chen Zhiwen Liu |
| author_facet | Zhongjie Shen Zhengjia He Xuefeng Chen Zhiwen Liu |
| author_sort | Zhongjie Shen |
| collection | DOAJ |
| description | Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM. |
| first_indexed | 2024-04-11T18:04:24Z |
| format | Article |
| id | doaj.art-95ccd8e2a7e0445888ae4c1022228fb3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:04:24Z |
| publishDate | 2013-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-95ccd8e2a7e0445888ae4c1022228fb32022-12-22T04:10:22ZengMDPI AGSensors1424-82202013-07-011378679869410.3390/s130708679LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring InformationZhongjie ShenZhengjia HeXuefeng ChenZhiwen LiuTimely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.http://www.mdpi.com/1424-8220/13/7/8679local mean decompositionreproducing wavelet kernel support vector machinesfault diagnosissensor-based vibration signalsrotating machinery |
| spellingShingle | Zhongjie Shen Zhengjia He Xuefeng Chen Zhiwen Liu LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information Sensors local mean decomposition reproducing wavelet kernel support vector machines fault diagnosis sensor-based vibration signals rotating machinery |
| title | LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information |
| title_full | LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information |
| title_fullStr | LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information |
| title_full_unstemmed | LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information |
| title_short | LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information |
| title_sort | lmd method and multi class rwsvm of fault diagnosis for rotating machinery using condition monitoring information |
| topic | local mean decomposition reproducing wavelet kernel support vector machines fault diagnosis sensor-based vibration signals rotating machinery |
| url | http://www.mdpi.com/1424-8220/13/7/8679 |
| work_keys_str_mv | AT zhongjieshen lmdmethodandmulticlassrwsvmoffaultdiagnosisforrotatingmachineryusingconditionmonitoringinformation AT zhengjiahe lmdmethodandmulticlassrwsvmoffaultdiagnosisforrotatingmachineryusingconditionmonitoringinformation AT xuefengchen lmdmethodandmulticlassrwsvmoffaultdiagnosisforrotatingmachineryusingconditionmonitoringinformation AT zhiwenliu lmdmethodandmulticlassrwsvmoffaultdiagnosisforrotatingmachineryusingconditionmonitoringinformation |