Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation
Compound faults easily happen in rolling bearing due to the complex working environment. Diagnosing compound faults accurately is a thorny problem, which can ensure the normal operation of mechanical structure. To tackle this problem, this paper proposes a novel method called double-domain reweighte...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10802894/ |
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| author | Jing Meng Jiawen Xu Chang Liu Chao Chen Lili Liu |
| author_facet | Jing Meng Jiawen Xu Chang Liu Chao Chen Lili Liu |
| author_sort | Jing Meng |
| collection | DOAJ |
| description | Compound faults easily happen in rolling bearing due to the complex working environment. Diagnosing compound faults accurately is a thorny problem, which can ensure the normal operation of mechanical structure. To tackle this problem, this paper proposes a novel method called double-domain reweighted adaptive sparse representation. The proposed method reweights the weight through the fault information from the wavelet domain and time domain. In the wavelet domain, periodic clustering similarity is proposed to measure fault features by similarity calculation. Then, wavelet domain information is transformed into time domain. In time domain, the Hadamard product of the sub-band signals is calculated to obtain fault features. The fault features in time domain are further transferred into wavelet domain, which is used for reweighting the weight. The proposed model’s optimal parameters are determined through the hippopotamus optimization algorithm, with the proposed frequency correlated kurtosis as the objective function. Frequency correlated kurtosis measures the signal’s frequency domain periodicity. The proposed method is verified by simulation, experimental signals, and comparison experiments, whose results show the effectiveness and superiority of the proposed method. |
| format | Article |
| id | doaj-art-e3af63e426ee4115a3e8ef7fb748d8fc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e3af63e426ee4115a3e8ef7fb748d8fc2024-12-25T00:01:32ZengIEEEIEEE Access2169-35362024-01-011219329919331210.1109/ACCESS.2024.351783310802894Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse RepresentationJing Meng0https://orcid.org/0000-0001-7973-1225Jiawen Xu1https://orcid.org/0000-0002-5398-0394Chang Liu2Chao Chen3https://orcid.org/0000-0002-1410-5466Lili Liu4School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang, ChinaSchool of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou, ChinaCompound faults easily happen in rolling bearing due to the complex working environment. Diagnosing compound faults accurately is a thorny problem, which can ensure the normal operation of mechanical structure. To tackle this problem, this paper proposes a novel method called double-domain reweighted adaptive sparse representation. The proposed method reweights the weight through the fault information from the wavelet domain and time domain. In the wavelet domain, periodic clustering similarity is proposed to measure fault features by similarity calculation. Then, wavelet domain information is transformed into time domain. In time domain, the Hadamard product of the sub-band signals is calculated to obtain fault features. The fault features in time domain are further transferred into wavelet domain, which is used for reweighting the weight. The proposed model’s optimal parameters are determined through the hippopotamus optimization algorithm, with the proposed frequency correlated kurtosis as the objective function. Frequency correlated kurtosis measures the signal’s frequency domain periodicity. The proposed method is verified by simulation, experimental signals, and comparison experiments, whose results show the effectiveness and superiority of the proposed method.https://ieeexplore.ieee.org/document/10802894/Compound fault diagnosisdouble-domainadaptive sparse representation |
| spellingShingle | Jing Meng Jiawen Xu Chang Liu Chao Chen Lili Liu Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation IEEE Access Compound fault diagnosis double-domain adaptive sparse representation |
| title | Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation |
| title_full | Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation |
| title_fullStr | Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation |
| title_full_unstemmed | Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation |
| title_short | Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation |
| title_sort | bearing compound fault diagnosis based on double domain reweighted adaptive sparse representation |
| topic | Compound fault diagnosis double-domain adaptive sparse representation |
| url | https://ieeexplore.ieee.org/document/10802894/ |
| work_keys_str_mv | AT jingmeng bearingcompoundfaultdiagnosisbasedondoubledomainreweightedadaptivesparserepresentation AT jiawenxu bearingcompoundfaultdiagnosisbasedondoubledomainreweightedadaptivesparserepresentation AT changliu bearingcompoundfaultdiagnosisbasedondoubledomainreweightedadaptivesparserepresentation AT chaochen bearingcompoundfaultdiagnosisbasedondoubledomainreweightedadaptivesparserepresentation AT lililiu bearingcompoundfaultdiagnosisbasedondoubledomainreweightedadaptivesparserepresentation |