Robust fault detection and diagnosis for multiple-model systems with uncertainties
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.In this paper, a robust fault detection and diagnosis (FDD) method is proposed for multiple-model systems with modeling uncertainties. A compensation step is introduced to modify the mixed state...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/52706 |
| _version_ | 1848758991405973504 |
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| author | Zhao, S. Huang, B. Luan, X. Yin, YanYan Liu, F. |
| author_facet | Zhao, S. Huang, B. Luan, X. Yin, YanYan Liu, F. |
| author_sort | Zhao, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.In this paper, a robust fault detection and diagnosis (FDD) method is proposed for multiple-model systems with modeling uncertainties. A compensation step is introduced to modify the mixed states and their variances obtained through the interacting multiple model (IMM) approximation and to solve the uncertainty problem. The degree of compensation is governed by a modification parameter determined by the orthogonality principle, which means that the estimation error calculated in the sub-filter using the true system models should be orthogonal to the residual error vector. To avoid over compensation in the unmatched models, a minimization procedure is used to derive the overall modification parameter. When the modification parameter is equal to one, the proposed method reduces to the IMM algorithm. An experiment is conducted through the ball and tube system to demonstrate the effectiveness of the proposed method. |
| first_indexed | 2025-11-14T09:52:47Z |
| format | Conference Paper |
| id | curtin-20.500.11937-52706 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:52:47Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-527062017-09-13T15:39:23Z Robust fault detection and diagnosis for multiple-model systems with uncertainties Zhao, S. Huang, B. Luan, X. Yin, YanYan Liu, F. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.In this paper, a robust fault detection and diagnosis (FDD) method is proposed for multiple-model systems with modeling uncertainties. A compensation step is introduced to modify the mixed states and their variances obtained through the interacting multiple model (IMM) approximation and to solve the uncertainty problem. The degree of compensation is governed by a modification parameter determined by the orthogonality principle, which means that the estimation error calculated in the sub-filter using the true system models should be orthogonal to the residual error vector. To avoid over compensation in the unmatched models, a minimization procedure is used to derive the overall modification parameter. When the modification parameter is equal to one, the proposed method reduces to the IMM algorithm. An experiment is conducted through the ball and tube system to demonstrate the effectiveness of the proposed method. 2015 Conference Paper http://hdl.handle.net/20.500.11937/52706 10.1016/j.ifacol.2015.09.517 restricted |
| spellingShingle | Zhao, S. Huang, B. Luan, X. Yin, YanYan Liu, F. Robust fault detection and diagnosis for multiple-model systems with uncertainties |
| title | Robust fault detection and diagnosis for multiple-model systems with uncertainties |
| title_full | Robust fault detection and diagnosis for multiple-model systems with uncertainties |
| title_fullStr | Robust fault detection and diagnosis for multiple-model systems with uncertainties |
| title_full_unstemmed | Robust fault detection and diagnosis for multiple-model systems with uncertainties |
| title_short | Robust fault detection and diagnosis for multiple-model systems with uncertainties |
| title_sort | robust fault detection and diagnosis for multiple-model systems with uncertainties |
| url | http://hdl.handle.net/20.500.11937/52706 |