Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm
The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenan...
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| Main Authors: | , , , , , |
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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/10.1155/2016/8631639 |
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| _version_ | 1832545797164498944 |
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| author | Wen-An Yang Maohua Xiao Wei Zhou Yu Guo Wenhe Liao Gang Shen |
| author_facet | Wen-An Yang Maohua Xiao Wei Zhou Yu Guo Wenhe Liao Gang Shen |
| author_sort | Wen-An Yang |
| collection | DOAJ |
| description | The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings. |
| format | Article |
| id | doaj-art-87a5b48b29fd4dcea109ce67be02df29 |
| 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-87a5b48b29fd4dcea109ce67be02df292025-02-03T07:24:48ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/86316398631639Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic AlgorithmWen-An Yang0Maohua Xiao1Wei Zhou2Yu Guo3Wenhe Liao4Gang Shen5College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaNanjing Surveying and Mapping Instrument Factory, Nanjing 210003, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaThe rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.http://dx.doi.org/10.1155/2016/8631639 |
| spellingShingle | Wen-An Yang Maohua Xiao Wei Zhou Yu Guo Wenhe Liao Gang Shen Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm Shock and Vibration |
| title | Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm |
| title_full | Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm |
| title_fullStr | Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm |
| title_full_unstemmed | Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm |
| title_short | Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm |
| title_sort | trace ratio criterion based kernel discriminant analysis for fault diagnosis of rolling element bearings using binary immune genetic algorithm |
| url | http://dx.doi.org/10.1155/2016/8631639 |
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