Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis
Principal component analysis (PCA) is widely used in fault diagnosis. Because the traditional data preprocessing method ignores the correlation between different variables in the system, the feature extraction is not accurate. In order to solve it, this paper proposes a kind of data preprocessing me...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/1025353 |
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| _version_ | 1832556572151119872 |
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| author | Zihan Wang Chenglin Wen Xiaoming Xu Siyu Ji |
| author_facet | Zihan Wang Chenglin Wen Xiaoming Xu Siyu Ji |
| author_sort | Zihan Wang |
| collection | DOAJ |
| description | Principal component analysis (PCA) is widely used in fault diagnosis. Because the traditional data preprocessing method ignores the correlation between different variables in the system, the feature extraction is not accurate. In order to solve it, this paper proposes a kind of data preprocessing method based on the Gap metric to improve the performance of PCA in fault diagnosis. For different types of faults, the original dataset transformation through Gap metric can reflect the correlation of different variables of the system in high-dimensional space, so as to model more accurately. Finally, the feasibility and effectiveness of the proposed method are verified through simulation. |
| format | Article |
| id | doaj-art-c4ab273f401247e4ad5fae10c6d3b673 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-c4ab273f401247e4ad5fae10c6d3b6732025-02-03T05:45:00ZengWileyJournal of Control Science and Engineering1687-52491687-52572018-01-01201810.1155/2018/10253531025353Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component AnalysisZihan Wang0Chenglin Wen1Xiaoming Xu2Siyu Ji3School of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaPrincipal component analysis (PCA) is widely used in fault diagnosis. Because the traditional data preprocessing method ignores the correlation between different variables in the system, the feature extraction is not accurate. In order to solve it, this paper proposes a kind of data preprocessing method based on the Gap metric to improve the performance of PCA in fault diagnosis. For different types of faults, the original dataset transformation through Gap metric can reflect the correlation of different variables of the system in high-dimensional space, so as to model more accurately. Finally, the feasibility and effectiveness of the proposed method are verified through simulation.http://dx.doi.org/10.1155/2018/1025353 |
| spellingShingle | Zihan Wang Chenglin Wen Xiaoming Xu Siyu Ji Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis Journal of Control Science and Engineering |
| title | Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis |
| title_full | Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis |
| title_fullStr | Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis |
| title_full_unstemmed | Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis |
| title_short | Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis |
| title_sort | fault diagnosis method based on gap metric data preprocessing and principal component analysis |
| url | http://dx.doi.org/10.1155/2018/1025353 |
| work_keys_str_mv | AT zihanwang faultdiagnosismethodbasedongapmetricdatapreprocessingandprincipalcomponentanalysis AT chenglinwen faultdiagnosismethodbasedongapmetricdatapreprocessingandprincipalcomponentanalysis AT xiaomingxu faultdiagnosismethodbasedongapmetricdatapreprocessingandprincipalcomponentanalysis AT siyuji faultdiagnosismethodbasedongapmetricdatapreprocessingandprincipalcomponentanalysis |