Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features
Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable. For bearing faults’ diagnosis, bearing faults signals collected from transducers are of...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/1835127 |
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| _version_ | 1832567714473836544 |
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| author | Wei Peng Dong Wang Changqing Shen Dongni Liu |
| author_facet | Wei Peng Dong Wang Changqing Shen Dongni Liu |
| author_sort | Wei Peng |
| collection | DOAJ |
| description | Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable. For bearing faults’ diagnosis, bearing faults signals collected from transducers are often overwhelmed by strong low-frequency periodic signals and heavy noises. In this paper, a joint signal processing method is proposed to extract sparse envelope coefficients, which are the sparse signal representations of bearing fault signals. Firstly, to enhance bearing fault signals, particle swarm optimization is introduced to tune the parameters of wavelet transform and the optimal wavelet transform is used for retaining one of the resonant frequency bands. Thus, sparse wavelet coefficients are obtained. Secondly, to reduce the in-band noises existing in the sparse wavelet coefficients, an adaptive morphological analysis with an iterative local maximum detection method is developed to extract sparse envelope coefficients. Simulated and real bearing fault signals are investigated to illustrate how the sparse envelope coefficients are extracted. The results show that the sparse envelope coefficients can be used to represent bearing fault features and identify different localized bearing faults. |
| format | Article |
| id | doaj-art-30c07e5240874ce9845a2d5bcc96c58c |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-30c07e5240874ce9845a2d5bcc96c58c2025-02-03T01:00:37ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/18351271835127Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault FeaturesWei Peng0Dong Wang1Changqing Shen2Dongni Liu3Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, ChinaDepartment of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong KongSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, ChinaDepartment of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong KongSparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable. For bearing faults’ diagnosis, bearing faults signals collected from transducers are often overwhelmed by strong low-frequency periodic signals and heavy noises. In this paper, a joint signal processing method is proposed to extract sparse envelope coefficients, which are the sparse signal representations of bearing fault signals. Firstly, to enhance bearing fault signals, particle swarm optimization is introduced to tune the parameters of wavelet transform and the optimal wavelet transform is used for retaining one of the resonant frequency bands. Thus, sparse wavelet coefficients are obtained. Secondly, to reduce the in-band noises existing in the sparse wavelet coefficients, an adaptive morphological analysis with an iterative local maximum detection method is developed to extract sparse envelope coefficients. Simulated and real bearing fault signals are investigated to illustrate how the sparse envelope coefficients are extracted. The results show that the sparse envelope coefficients can be used to represent bearing fault features and identify different localized bearing faults.http://dx.doi.org/10.1155/2016/1835127 |
| spellingShingle | Wei Peng Dong Wang Changqing Shen Dongni Liu Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features Shock and Vibration |
| title | Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features |
| title_full | Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features |
| title_fullStr | Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features |
| title_full_unstemmed | Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features |
| title_short | Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features |
| title_sort | sparse signal representations of bearing fault signals for exhibiting bearing fault features |
| url | http://dx.doi.org/10.1155/2016/1835127 |
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