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- Publisher Website: 10.1109/TCYB.2018.2789360
- Scopus: eid_2-s2.0-85042853239
- PMID: 29994593
- WOS: WOS:000458655900015
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Article: Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning
Title | Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning |
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Authors | |
Keywords | System dynamics Artificial neural networks Trajectory Fault detection Closed loop systems |
Issue Date | 2019 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036 |
Citation | IEEE Transactions on Cybernetics, 2019, v. 49 n. 3, p. 897-906 How to Cite? |
Abstract | In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First, the DL-based neural control approach and identification approach are employed to extract the knowledge of the control effort that compensates the fault dynamics (change of the control effort) and the fault dynamics (the change of system dynamics due to fault). Second, two types of residuals are constructed. One is to measure the change of system dynamics, another one is to measure change of the control effort. By combining these residuals, an enhanced residual is generated, in which the fault dynamics and the control effort are combined to diagnose the fault. It is shown that the major fault information is compensated by the control, and the major fault information is double in the enhanced residual. Therefore, the fault information in the diagnosis residual is enhanced. Finally, an analysis of the fault detectability condition of the diagnosis scheme is given. Simulation studies are included to demonstrate the effectiveness of the approach. |
Persistent Identifier | http://hdl.handle.net/10722/279151 |
ISSN | 2023 Impact Factor: 9.4 2023 SCImago Journal Rankings: 5.641 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, T | - |
dc.contributor.author | Wang, C | - |
dc.contributor.author | Chen, G | - |
dc.contributor.author | Dong, Z | - |
dc.contributor.author | Hill, DJ | - |
dc.date.accessioned | 2019-10-21T02:20:29Z | - |
dc.date.available | 2019-10-21T02:20:29Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Cybernetics, 2019, v. 49 n. 3, p. 897-906 | - |
dc.identifier.issn | 2168-2267 | - |
dc.identifier.uri | http://hdl.handle.net/10722/279151 | - |
dc.description.abstract | In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First, the DL-based neural control approach and identification approach are employed to extract the knowledge of the control effort that compensates the fault dynamics (change of the control effort) and the fault dynamics (the change of system dynamics due to fault). Second, two types of residuals are constructed. One is to measure the change of system dynamics, another one is to measure change of the control effort. By combining these residuals, an enhanced residual is generated, in which the fault dynamics and the control effort are combined to diagnose the fault. It is shown that the major fault information is compensated by the control, and the major fault information is double in the enhanced residual. Therefore, the fault information in the diagnosis residual is enhanced. Finally, an analysis of the fault detectability condition of the diagnosis scheme is given. Simulation studies are included to demonstrate the effectiveness of the approach. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036 | - |
dc.relation.ispartof | IEEE Transactions on Cybernetics | - |
dc.rights | IEEE Transactions on Cybernetics. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | System dynamics | - |
dc.subject | Artificial neural networks | - |
dc.subject | Trajectory | - |
dc.subject | Fault detection | - |
dc.subject | Closed loop systems | - |
dc.title | Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning | - |
dc.type | Article | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TCYB.2018.2789360 | - |
dc.identifier.pmid | 29994593 | - |
dc.identifier.scopus | eid_2-s2.0-85042853239 | - |
dc.identifier.hkuros | 307223 | - |
dc.identifier.volume | 49 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 897 | - |
dc.identifier.epage | 906 | - |
dc.identifier.isi | WOS:000458655900015 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2168-2267 | - |