Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair
In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolli...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/3871089 |
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| _version_ | 1832564737082130432 |
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| author | Dan Ma Yixiang Lu Yushun Zhang Hua Bao Xueming Peng |
| author_facet | Dan Ma Yixiang Lu Yushun Zhang Hua Bao Xueming Peng |
| author_sort | Dan Ma |
| collection | DOAJ |
| description | In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs. |
| format | Article |
| id | doaj-art-29f755bec0234cfc9ee0dc1ad17a582e |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-29f755bec0234cfc9ee0dc1ad17a582e2025-02-03T01:10:12ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/38710893871089Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary PairDan Ma0Yixiang Lu1Yushun Zhang2Hua Bao3Xueming Peng4Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, ChinaKey Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, ChinaKey Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, ChinaKey Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, ChinaKey Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, ChinaIn state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.http://dx.doi.org/10.1155/2019/3871089 |
| spellingShingle | Dan Ma Yixiang Lu Yushun Zhang Hua Bao Xueming Peng Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair Shock and Vibration |
| title | Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair |
| title_full | Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair |
| title_fullStr | Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair |
| title_full_unstemmed | Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair |
| title_short | Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair |
| title_sort | bearing fault diagnosis based on collaborative representation using projection dictionary pair |
| url | http://dx.doi.org/10.1155/2019/3871089 |
| work_keys_str_mv | AT danma bearingfaultdiagnosisbasedoncollaborativerepresentationusingprojectiondictionarypair AT yixianglu bearingfaultdiagnosisbasedoncollaborativerepresentationusingprojectiondictionarypair AT yushunzhang bearingfaultdiagnosisbasedoncollaborativerepresentationusingprojectiondictionarypair AT huabao bearingfaultdiagnosisbasedoncollaborativerepresentationusingprojectiondictionarypair AT xuemingpeng bearingfaultdiagnosisbasedoncollaborativerepresentationusingprojectiondictionarypair |