Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis
In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is no...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/2697297 |
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| _version_ | 1832553465453215744 |
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| author | Xiaoming Xu Chenglin Wen |
| author_facet | Xiaoming Xu Chenglin Wen |
| author_sort | Xiaoming Xu |
| collection | DOAJ |
| description | In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method. |
| format | Article |
| id | doaj-art-589418d048b7483bb871c7151328c0a5 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-589418d048b7483bb871c7151328c0a52025-02-03T05:53:56ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/26972972697297Fault Diagnosis Method Based on Information Entropy and Relative Principal Component AnalysisXiaoming Xu0Chenglin Wen1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaIn traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.http://dx.doi.org/10.1155/2017/2697297 |
| spellingShingle | Xiaoming Xu Chenglin Wen Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis Journal of Control Science and Engineering |
| title | Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis |
| title_full | Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis |
| title_fullStr | Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis |
| title_full_unstemmed | Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis |
| title_short | Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis |
| title_sort | fault diagnosis method based on information entropy and relative principal component analysis |
| url | http://dx.doi.org/10.1155/2017/2697297 |
| work_keys_str_mv | AT xiaomingxu faultdiagnosismethodbasedoninformationentropyandrelativeprincipalcomponentanalysis AT chenglinwen faultdiagnosismethodbasedoninformationentropyandrelativeprincipalcomponentanalysis |