Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis
The high-dimensional features of defective bearings usually include redundant and irrelevant information, which will degrade the diagnosis performance. Thus, it is critical to extract the sensitive low-dimensional characteristics for improving diagnosis performance. This paper proposes modified kern...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/1205868 |
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| _version_ | 1832551895928930304 |
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| author | Li Jiang Shunsheng Guo |
| author_facet | Li Jiang Shunsheng Guo |
| author_sort | Li Jiang |
| collection | DOAJ |
| description | The high-dimensional features of defective bearings usually include redundant and irrelevant information, which will degrade the diagnosis performance. Thus, it is critical to extract the sensitive low-dimensional characteristics for improving diagnosis performance. This paper proposes modified kernel marginal Fisher analysis (MKMFA) for feature extraction with dimensionality reduction. Due to its outstanding performance in enhancing the intraclass compactness and interclass dispersibility, MKMFA is capable of effectively extracting the sensitive low-dimensional manifold characteristics beneficial to subsequent pattern classification even for few training samples. A MKMFA- based fault diagnosis model is presented and applied to identify different bearing faults. It firstly utilizes MKMFA to directly extract the low-dimensional manifold characteristics from the raw time-series signal samples in high-dimensional ambient space. Subsequently, the sensitive low-dimensional characteristics in feature space are inputted into K-nearest neighbor classifier so as to distinguish various fault patterns. The four-fault-type and ten-fault-severity bearing fault diagnosis experiment results show the feasibility and superiority of the proposed scheme in comparison with the other five methods. |
| format | Article |
| id | doaj-art-a92757d54b30413b82a9843ad2c4e687 |
| 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-a92757d54b30413b82a9843ad2c4e6872025-02-03T06:00:15ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/12058681205868Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault DiagnosisLi Jiang0Shunsheng Guo1School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, ChinaThe high-dimensional features of defective bearings usually include redundant and irrelevant information, which will degrade the diagnosis performance. Thus, it is critical to extract the sensitive low-dimensional characteristics for improving diagnosis performance. This paper proposes modified kernel marginal Fisher analysis (MKMFA) for feature extraction with dimensionality reduction. Due to its outstanding performance in enhancing the intraclass compactness and interclass dispersibility, MKMFA is capable of effectively extracting the sensitive low-dimensional manifold characteristics beneficial to subsequent pattern classification even for few training samples. A MKMFA- based fault diagnosis model is presented and applied to identify different bearing faults. It firstly utilizes MKMFA to directly extract the low-dimensional manifold characteristics from the raw time-series signal samples in high-dimensional ambient space. Subsequently, the sensitive low-dimensional characteristics in feature space are inputted into K-nearest neighbor classifier so as to distinguish various fault patterns. The four-fault-type and ten-fault-severity bearing fault diagnosis experiment results show the feasibility and superiority of the proposed scheme in comparison with the other five methods.http://dx.doi.org/10.1155/2016/1205868 |
| spellingShingle | Li Jiang Shunsheng Guo Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis Shock and Vibration |
| title | Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis |
| title_full | Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis |
| title_fullStr | Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis |
| title_full_unstemmed | Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis |
| title_short | Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis |
| title_sort | modified kernel marginal fisher analysis for feature extraction and its application to bearing fault diagnosis |
| url | http://dx.doi.org/10.1155/2016/1205868 |
| work_keys_str_mv | AT lijiang modifiedkernelmarginalfisheranalysisforfeatureextractionanditsapplicationtobearingfaultdiagnosis AT shunshengguo modifiedkernelmarginalfisheranalysisforfeatureextractionanditsapplicationtobearingfaultdiagnosis |