Fault Detection and Diagnosis in Process Data Using Support Vector Machines
For the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/732104 |
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| author | Fang Wu Shen Yin Hamid Reza Karimi |
| author_facet | Fang Wu Shen Yin Hamid Reza Karimi |
| author_sort | Fang Wu |
| collection | DOAJ |
| description | For the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination) method. RFE is used for feature extraction, and PCA is utilized to project the original data onto a lower dimensional space. PCA T2, SPE statistics, and original SVM are proposed to detect the faults. Some common faults of the Tennessee Eastman Process (TEP) are analyzed in terms of the practical system and reflections of the dataset. PCA-SVM and SVM-RFE can effectively detect and diagnose these common faults. In RFE algorithm, all variables are decreasingly ordered according to their contributions. The classification accuracy rate is improved by choosing a reasonable number of features. |
| format | Article |
| id | doaj-art-18ec231dc7494961af872df5ee8828fc |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-18ec231dc7494961af872df5ee8828fc2025-02-03T05:58:52ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/732104732104Fault Detection and Diagnosis in Process Data Using Support Vector MachinesFang Wu0Shen Yin1Hamid Reza Karimi2Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Heilongjiang 150001, ChinaResearch Institute of Intelligent Control and Systems, Harbin Institute of Technology, Heilongjiang 150001, ChinaDepartment of Engineering, Faculty of Engineering and Science, The University of Agder, 4898 Grimstad, NorwayFor the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination) method. RFE is used for feature extraction, and PCA is utilized to project the original data onto a lower dimensional space. PCA T2, SPE statistics, and original SVM are proposed to detect the faults. Some common faults of the Tennessee Eastman Process (TEP) are analyzed in terms of the practical system and reflections of the dataset. PCA-SVM and SVM-RFE can effectively detect and diagnose these common faults. In RFE algorithm, all variables are decreasingly ordered according to their contributions. The classification accuracy rate is improved by choosing a reasonable number of features.http://dx.doi.org/10.1155/2014/732104 |
| spellingShingle | Fang Wu Shen Yin Hamid Reza Karimi Fault Detection and Diagnosis in Process Data Using Support Vector Machines Journal of Applied Mathematics |
| title | Fault Detection and Diagnosis in Process Data Using Support Vector Machines |
| title_full | Fault Detection and Diagnosis in Process Data Using Support Vector Machines |
| title_fullStr | Fault Detection and Diagnosis in Process Data Using Support Vector Machines |
| title_full_unstemmed | Fault Detection and Diagnosis in Process Data Using Support Vector Machines |
| title_short | Fault Detection and Diagnosis in Process Data Using Support Vector Machines |
| title_sort | fault detection and diagnosis in process data using support vector machines |
| url | http://dx.doi.org/10.1155/2014/732104 |
| work_keys_str_mv | AT fangwu faultdetectionanddiagnosisinprocessdatausingsupportvectormachines AT shenyin faultdetectionanddiagnosisinprocessdatausingsupportvectormachines AT hamidrezakarimi faultdetectionanddiagnosisinprocessdatausingsupportvectormachines |