Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit
This paper explores sparse time-frequency distribution (TFD) using overcomplete discrete wavelet transform (DWT) and sparse representation techniques. This distribution is discovered for characterizing the periodic transient information embedded in rolling element bearings and extracting effective f...
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| Published in | Journal of intelligent manufacturing Vol. 28; no. 6; pp. 1377 - 1391 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.08.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper explores sparse time-frequency distribution (TFD) using overcomplete discrete wavelet transform (DWT) and sparse representation techniques. This distribution is discovered for characterizing the periodic transient information embedded in rolling element bearings and extracting effective features that can discriminate different fault conditions. Based on the sparse TFD, a new sparse wavelet energy (SWE) feature is obtained by three main steps: first, an overcomplete discrete DWT is employed to decompose the fault signal and construct a redundant dictionary; second, the redundant dictionary is optimized by basis pursuit to obtain the sparsest TFD; finally, SWE is calculated from the new TFD to produce a feature vector for each signal. SWE features that combine the merits of overcomplete DWT and sparse representation techniques can precisely reveal fault-induced information, thereby exhibiting valuable properties for automatic fault identification by intelligent classifiers. The effectiveness and advantages of the proposed features are confirmed by simulation and the practical fault pattern recognition of rolling bearings. |
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| AbstractList | This paper explores sparse time-frequency distribution (TFD) using overcomplete discrete wavelet transform (DWT) and sparse representation techniques. This distribution is discovered for characterizing the periodic transient information embedded in rolling element bearings and extracting effective features that can discriminate different fault conditions. Based on the sparse TFD, a new sparse wavelet energy (SWE) feature is obtained by three main steps: first, an overcomplete discrete DWT is employed to decompose the fault signal and construct a redundant dictionary; second, the redundant dictionary is optimized by basis pursuit to obtain the sparsest TFD; finally, SWE is calculated from the new TFD to produce a feature vector for each signal. SWE features that combine the merits of overcomplete DWT and sparse representation techniques can precisely reveal fault-induced information, thereby exhibiting valuable properties for automatic fault identification by intelligent classifiers. The effectiveness and advantages of the proposed features are confirmed by simulation and the practical fault pattern recognition of rolling bearings. |
| Author | Zhu, Chang’an Wang, Cong Gan, Meng |
| Author_xml | – sequence: 1 givenname: Cong surname: Wang fullname: Wang, Cong email: wcc12345@mail.ustc.edu.cn organization: Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC) – sequence: 2 givenname: Meng surname: Gan fullname: Gan, Meng organization: Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC) – sequence: 3 givenname: Chang’an surname: Zhu fullname: Zhu, Chang’an organization: Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC) |
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| Keywords | Basis pursuit Intelligent fault diagnosis Sparse wavelet energy (SWE) Overcomplete discrete wavelet transform (DWT) |
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| SubjectTerms | Advanced manufacturing technologies Bearings Business and Management Classifiers Control Dictionaries Discrete Wavelet Transform Energy consumption Fault diagnosis Feature extraction Feature recognition Frequency distribution Intelligent systems Machines Manufacturing Mechatronics Pattern recognition Processes Production Redundancy Robotics Roller bearings Studies Wavelet transforms |
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| Title | Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit |
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