Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis
The model-based fault detection technique, which needs to identify the system models, has been well established. The objective of this paper is to develop an alternative procedure instead of identifying the system models. In this paper, subspace method aided data-driven fault detection based on prin...
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| Main Authors: | , |
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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/10.1155/2017/1812989 |
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| _version_ | 1832566793458155520 |
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| author | Lingling Ma Xiangshun Li |
| author_facet | Lingling Ma Xiangshun Li |
| author_sort | Lingling Ma |
| collection | DOAJ |
| description | The model-based fault detection technique, which needs to identify the system models, has been well established. The objective of this paper is to develop an alternative procedure instead of identifying the system models. In this paper, subspace method aided data-driven fault detection based on principal component analysis (PCA) is proposed. The basic idea is to use PCA to identify the system observability matrices from input and output data and construct residual generators. The advantage of the proposed method is that we just need to identify the parameterized matrices related to residuals rather than the system models, which reduces the computational steps of the system. The proposed approach is illustrated by a simulation study on the Tennessee Eastman process. |
| format | Article |
| id | doaj-art-ef217a56fd9748a5a7a874f73d13d7e0 |
| 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-ef217a56fd9748a5a7a874f73d13d7e02025-02-03T01:03:15ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/18129891812989Subspace Method Aided Data-Driven Fault Detection Based on Principal Component AnalysisLingling Ma0Xiangshun Li1Wuhan University of Technology, Wuhan, ChinaWuhan University of Technology, Wuhan, ChinaThe model-based fault detection technique, which needs to identify the system models, has been well established. The objective of this paper is to develop an alternative procedure instead of identifying the system models. In this paper, subspace method aided data-driven fault detection based on principal component analysis (PCA) is proposed. The basic idea is to use PCA to identify the system observability matrices from input and output data and construct residual generators. The advantage of the proposed method is that we just need to identify the parameterized matrices related to residuals rather than the system models, which reduces the computational steps of the system. The proposed approach is illustrated by a simulation study on the Tennessee Eastman process.http://dx.doi.org/10.1155/2017/1812989 |
| spellingShingle | Lingling Ma Xiangshun Li Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis Journal of Control Science and Engineering |
| title | Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis |
| title_full | Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis |
| title_fullStr | Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis |
| title_full_unstemmed | Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis |
| title_short | Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis |
| title_sort | subspace method aided data driven fault detection based on principal component analysis |
| url | http://dx.doi.org/10.1155/2017/1812989 |
| work_keys_str_mv | AT linglingma subspacemethodaideddatadrivenfaultdetectionbasedonprincipalcomponentanalysis AT xiangshunli subspacemethodaideddatadrivenfaultdetectionbasedonprincipalcomponentanalysis |