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Immune model-based fault diagnosis

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  • Luh, Guan-Chun
  • Cheng, Wei-Chong

Abstract

In this paper, a novel approach to immune model-based fault diagnosis methodology for nonlinear systems is presented. The diagnosis scheme consists of forward/inverse immune model identification, filtered residual generation, the fault alarm concentration (FAC), and the artificial immune regulation (AIR). A two-link manipulator simulation was employed to validate the effectiveness and robustness of the diagnosis approach. The simulation results show that it can detect and isolate actuator faults, sensor faults, and system component faults efficiently.

Suggested Citation

  • Luh, Guan-Chun & Cheng, Wei-Chong, 2005. "Immune model-based fault diagnosis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 67(6), pages 515-539.
    • Handle: RePEc:eee:matcom:v:67:y:2005:i:6:p:515-539
    DOI: 10.1016/j.matcom.2004.07.004
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    References listed on IDEAS

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    1. Chen, Y.M. & Lee, M.L., 2002. "Neural networks-based scheme for system failure detection and diagnosis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 58(2), pages 101-109.
    2. Thomas, Philippe & Lefebvre, Dimitri, 2002. "Fault detection and isolation in non-linear systems by using oversized neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 181-192.
    3. Puig, Vicenç & Quevedo, Joseba, 2002. "Passive robust fault detection using fuzzy parity equations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 193-207.
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    Cited by:

    1. Hou, Caihong & Ding, Yongsheng & Zeng, Xianyi, 2008. "Immune-based evolutionary algorithm for fabric evaluation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(5), pages 540-549.
    2. Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.

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