Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis
In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous fea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/8/1194 |
| _version_ | 1828715388130557952 |
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| author | Yan Chu Syed Muhammad Ali Mingfeng Lu Yanan Zhang |
| author_facet | Yan Chu Syed Muhammad Ali Mingfeng Lu Yanan Zhang |
| author_sort | Yan Chu |
| collection | DOAJ |
| description | In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the interrelation and redundant problems to precisely discover the bearing faults. Thus, in the current study, a novel diagnostic method, namely the method of incorporating heterogeneous representative features into the random subspace, or IHF-RS, is proposed for accurate bearing fault diagnosis. Primarily, via signal processing methods, statistical features are extracted, and via the deep stack autoencoder (DSAE), deep representation features are acquired. Next, considering the different levels of predictive power of features, a modified lasso method incorporating the random subspace method is introduced to measure the features and produce better base classifiers. Finally, the majority voting strategy is applied to aggregate the outputs of these various base classifiers to enhance the diagnostic performance of the bearing fault. For the proposed method’s validity, two bearing datasets provided by the Case Western Reserve University Bearing Data Center and Paderborn University were utilized for the experiments. The results of the experiment revealed that in bearing fault diagnosis, the proposed method of IHF-RS can be successfully utilized. |
| first_indexed | 2024-03-10T23:57:07Z |
| format | Article |
| id | doaj.art-3acb3794c8c34b56ab148f36b36be24a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T23:57:07Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-3acb3794c8c34b56ab148f36b36be24a2023-11-19T00:59:53ZengMDPI AGEntropy1099-43002023-08-01258119410.3390/e25081194Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault DiagnosisYan Chu0Syed Muhammad Ali1Mingfeng Lu2Yanan Zhang3School of Finance, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, ChinaDepartment of Engineering Management, National University of Sciences and Technology, Islamabad 44000, PakistanSchool of Management, Hefei University of Technology, Hefei 230009, ChinaSchool of Management, Hefei University of Technology, Hefei 230009, ChinaIn bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the interrelation and redundant problems to precisely discover the bearing faults. Thus, in the current study, a novel diagnostic method, namely the method of incorporating heterogeneous representative features into the random subspace, or IHF-RS, is proposed for accurate bearing fault diagnosis. Primarily, via signal processing methods, statistical features are extracted, and via the deep stack autoencoder (DSAE), deep representation features are acquired. Next, considering the different levels of predictive power of features, a modified lasso method incorporating the random subspace method is introduced to measure the features and produce better base classifiers. Finally, the majority voting strategy is applied to aggregate the outputs of these various base classifiers to enhance the diagnostic performance of the bearing fault. For the proposed method’s validity, two bearing datasets provided by the Case Western Reserve University Bearing Data Center and Paderborn University were utilized for the experiments. The results of the experiment revealed that in bearing fault diagnosis, the proposed method of IHF-RS can be successfully utilized.https://www.mdpi.com/1099-4300/25/8/1194heterogeneous featuresrandom subspace methodbearing fault diagnosisdeep stack autoencoderlasso |
| spellingShingle | Yan Chu Syed Muhammad Ali Mingfeng Lu Yanan Zhang Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis Entropy heterogeneous features random subspace method bearing fault diagnosis deep stack autoencoder lasso |
| title | Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis |
| title_full | Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis |
| title_fullStr | Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis |
| title_full_unstemmed | Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis |
| title_short | Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis |
| title_sort | incorporating heterogeneous features into the random subspace method for bearing fault diagnosis |
| topic | heterogeneous features random subspace method bearing fault diagnosis deep stack autoencoder lasso |
| url | https://www.mdpi.com/1099-4300/25/8/1194 |
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