Fault diagnosis of coal-mine-gas charging sensor networks using iterative learning-control algorithm
To detect and estimate the faults of discrete linear time-varying uncertain systems, a discrete learning strategy is applied to fault diagnosis, and a new fault-detection and estimation algorithm is proposed. The algorithm adopts the threshold-limit technology. In the selected optimal time domain, a...
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| Published in | Physical communication Vol. 43; p. 101175 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.12.2020
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| Online Access | Get full text |
| ISSN | 1874-4907 1876-3219 |
| DOI | 10.1016/j.phycom.2020.101175 |
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| Abstract | To detect and estimate the faults of discrete linear time-varying uncertain systems, a discrete learning strategy is applied to fault diagnosis, and a new fault-detection and estimation algorithm is proposed. The algorithm adopts the threshold-limit technology. In the selected optimal time domain, a residual signal is used to perform iterative learning correction for the introduced virtual faults so that the virtual faults in an actual system approach the actual faults. The same method is repeated in the remaining optimal time domain to achieve the objective of fault diagnosis. The algorithm not only completes the fault detection and estimation of a discrete linear time-varying uncertain system but also improves the reliability of fault detection and reduces the false alarm rate. Finally, the simulation results verify the effectiveness of the proposed algorithm. |
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| AbstractList | To detect and estimate the faults of discrete linear time-varying uncertain systems, a discrete learning strategy is applied to fault diagnosis, and a new fault-detection and estimation algorithm is proposed. The algorithm adopts the threshold-limit technology. In the selected optimal time domain, a residual signal is used to perform iterative learning correction for the introduced virtual faults so that the virtual faults in an actual system approach the actual faults. The same method is repeated in the remaining optimal time domain to achieve the objective of fault diagnosis. The algorithm not only completes the fault detection and estimation of a discrete linear time-varying uncertain system but also improves the reliability of fault detection and reduces the false alarm rate. Finally, the simulation results verify the effectiveness of the proposed algorithm. |
| ArticleNumber | 101175 |
| Author | Zhang, Jianyu Huang, Kai |
| Author_xml | – sequence: 1 givenname: Jianyu surname: Zhang fullname: Zhang, Jianyu email: zhangjianyuxs@163.com – sequence: 2 givenname: Kai surname: Huang fullname: Huang, Kai email: 1242365953@qq.com |
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| CitedBy_id | crossref_primary_10_1007_s40747_021_00519_2 crossref_primary_10_1155_2022_2191297 crossref_primary_10_32604_cmes_2022_020412 crossref_primary_10_1016_j_jprocont_2025_103402 crossref_primary_10_3390_math12070955 crossref_primary_10_1155_2022_3032445 |
| Cites_doi | 10.1109/JAS.2018.7511123 10.1109/JSEN.2014.2307878 10.1109/JSEE.2014.00057 10.1109/TIE.2017.2782201 10.1109/TIM.2009.2025068 10.1109/TCST.2013.2291069 10.1109/TSMC.2014.2358635 10.1515/ms-2017-0125 |
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| Title | Fault diagnosis of coal-mine-gas charging sensor networks using iterative learning-control algorithm |
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