Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns
This article presents a solution to the problem of multiple fault detection, isolation and identification for hybrid systems without information on mode change and fault patterns. Multiple faults of different patterns are considered in a complex hybrid system and these faults can happen either in a...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98511 http://hdl.handle.net/10220/11115 |
| _version_ | 1826117348803739648 |
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| author | Yu, Ming Wang, Danwei Luo, Ming Zhang, Danhong Chen, Qijun |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yu, Ming Wang, Danwei Luo, Ming Zhang, Danhong Chen, Qijun |
| author_sort | Yu, Ming |
| collection | NTU |
| description | This article presents a solution to the problem of multiple fault detection, isolation and identification for hybrid systems without information on mode change and fault patterns. Multiple faults of different patterns are considered in a complex hybrid system and these faults can happen either in a detectable mode or in a non-detectable mode. A method for multiple fault isolation is introduced for situation of lacking information on fault pattern and mode change. The nature of faults in a monitored system can be classified as abrupt faults and incipient faults. Under abrupt fault assumption, i.e. constant values for fault parameters, fault identification is inappropriate to handle cases related to incipient fault. Without information on fault nature, it is difficult to achieve fault estimation. Situation is further complicated when mode change is unknown after fault occurrence. In this work, fault pattern is represented by a binary vector to reduce computational complexity of fault identification. Mode change is parameterized as a discontinuous function. Based on these new representations, a multiple hybrid differential evolution algorithm is developed to identify fault pattern vector, abrupt fault parameter/incipient fault dynamic coefficient, and mode change indexes. Simulation and experiment results are reported to validate the proposed method. |
| first_indexed | 2024-10-01T04:25:49Z |
| format | Journal Article |
| id | ntu-10356/98511 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:25:49Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/985112020-03-07T14:00:30Z Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns Yu, Ming Wang, Danwei Luo, Ming Zhang, Danhong Chen, Qijun School of Electrical and Electronic Engineering A*STAR SIMTech DRNTU::Engineering::Electrical and electronic engineering This article presents a solution to the problem of multiple fault detection, isolation and identification for hybrid systems without information on mode change and fault patterns. Multiple faults of different patterns are considered in a complex hybrid system and these faults can happen either in a detectable mode or in a non-detectable mode. A method for multiple fault isolation is introduced for situation of lacking information on fault pattern and mode change. The nature of faults in a monitored system can be classified as abrupt faults and incipient faults. Under abrupt fault assumption, i.e. constant values for fault parameters, fault identification is inappropriate to handle cases related to incipient fault. Without information on fault nature, it is difficult to achieve fault estimation. Situation is further complicated when mode change is unknown after fault occurrence. In this work, fault pattern is represented by a binary vector to reduce computational complexity of fault identification. Mode change is parameterized as a discontinuous function. Based on these new representations, a multiple hybrid differential evolution algorithm is developed to identify fault pattern vector, abrupt fault parameter/incipient fault dynamic coefficient, and mode change indexes. Simulation and experiment results are reported to validate the proposed method. 2013-07-10T07:03:30Z 2019-12-06T19:56:22Z 2013-07-10T07:03:30Z 2019-12-06T19:56:22Z 2012 2012 Journal Article https://hdl.handle.net/10356/98511 http://hdl.handle.net/10220/11115 10.1016/j.eswa.2012.01.103 en Expert systems with applications © 2012 Elsevier Ltd. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Yu, Ming Wang, Danwei Luo, Ming Zhang, Danhong Chen, Qijun Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns |
| title | Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns |
| title_full | Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns |
| title_fullStr | Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns |
| title_full_unstemmed | Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns |
| title_short | Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns |
| title_sort | fault detection isolation and identification for hybrid systems with unknown mode changes and fault patterns |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/98511 http://hdl.handle.net/10220/11115 |
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