Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis
Rolling bearings are omnipresent parts in industrial fields. To comprehensively reflect the status of rolling bearing and improve the classification accuracy, fusion information is widely used in various studies, which may result in high dimensionality, redundancy information of dataset, and time co...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/8946094 |
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| author | Hongdi Zhou Lin Zhu Xixing Li |
| author_facet | Hongdi Zhou Lin Zhu Xixing Li |
| author_sort | Hongdi Zhou |
| collection | DOAJ |
| description | Rolling bearings are omnipresent parts in industrial fields. To comprehensively reflect the status of rolling bearing and improve the classification accuracy, fusion information is widely used in various studies, which may result in high dimensionality, redundancy information of dataset, and time consumption. Thus, it is of crucial significance in extracting optimal features from high-dimensional and redundant feature space for classification. In this study, a fault diagnosis of rolling bearings model based on sparse principal subspace discriminant analysis is proposed. It extracts sparse discrimination information, meanwhile preserving the main energy of original dataset, and the sparse regularization term and sparse error term constrained by l2,1-norm are introduced to improve the performance of feature extraction and the robustness to noise and outliers. The multi-domain feature space involved a time domain, frequency domain, and time-frequency domain is first derived from the original vibration signals. Then, the intrinsic geometric features extracted by sparse principal subspace discriminant analysis are fed into a support vector machine classifier to recognize different operating conditions of bearings. The experimental results demonstrated that the feasibility and effectiveness of the proposed fault diagnosis model based on a sparse principal subspace discriminant analysis algorithm can achieve higher recognition accuracy than fisher discriminant analysis and its extensions, and it is relatively insensitive to the impact of noise and outliers owing to the sparse property. |
| format | Article |
| id | doaj-art-6fb56668bc2141ad97e5a28c8f302e46 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6fb56668bc2141ad97e5a28c8f302e462025-02-03T01:22:46ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/8946094Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant AnalysisHongdi Zhou0Lin Zhu1Xixing Li2School of Mechanical EngineeringSchool of Mechanical EngineeringSchool of Mechanical EngineeringRolling bearings are omnipresent parts in industrial fields. To comprehensively reflect the status of rolling bearing and improve the classification accuracy, fusion information is widely used in various studies, which may result in high dimensionality, redundancy information of dataset, and time consumption. Thus, it is of crucial significance in extracting optimal features from high-dimensional and redundant feature space for classification. In this study, a fault diagnosis of rolling bearings model based on sparse principal subspace discriminant analysis is proposed. It extracts sparse discrimination information, meanwhile preserving the main energy of original dataset, and the sparse regularization term and sparse error term constrained by l2,1-norm are introduced to improve the performance of feature extraction and the robustness to noise and outliers. The multi-domain feature space involved a time domain, frequency domain, and time-frequency domain is first derived from the original vibration signals. Then, the intrinsic geometric features extracted by sparse principal subspace discriminant analysis are fed into a support vector machine classifier to recognize different operating conditions of bearings. The experimental results demonstrated that the feasibility and effectiveness of the proposed fault diagnosis model based on a sparse principal subspace discriminant analysis algorithm can achieve higher recognition accuracy than fisher discriminant analysis and its extensions, and it is relatively insensitive to the impact of noise and outliers owing to the sparse property.http://dx.doi.org/10.1155/2022/8946094 |
| spellingShingle | Hongdi Zhou Lin Zhu Xixing Li Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis Shock and Vibration |
| title | Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis |
| title_full | Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis |
| title_fullStr | Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis |
| title_full_unstemmed | Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis |
| title_short | Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis |
| title_sort | fault diagnosis method for rolling bearing based on sparse principal subspace discriminant analysis |
| url | http://dx.doi.org/10.1155/2022/8946094 |
| work_keys_str_mv | AT hongdizhou faultdiagnosismethodforrollingbearingbasedonsparseprincipalsubspacediscriminantanalysis AT linzhu faultdiagnosismethodforrollingbearingbasedonsparseprincipalsubspacediscriminantanalysis AT xixingli faultdiagnosismethodforrollingbearingbasedonsparseprincipalsubspacediscriminantanalysis |