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XUAN Hengnong, ZHANG Runchi, SHI Shengsheng. An Efficient Cuckoo Search Algorithm for System-Level Fault Diagnosis[J]. Chinese Journal of Electronics, 2016, 25(6): 999-1004. DOI: 10.1049/cje.2016.06.035
Citation: XUAN Hengnong, ZHANG Runchi, SHI Shengsheng. An Efficient Cuckoo Search Algorithm for System-Level Fault Diagnosis[J]. Chinese Journal of Electronics, 2016, 25(6): 999-1004. DOI: 10.1049/cje.2016.06.035
shu

An Efficient Cuckoo Search Algorithm for System-Level Fault Diagnosis

  • 1.

    School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210046, China;

  • 2.

    Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China

Funds: This work is supported by the National Natural Science Foundation of China (No.90718008, No.61133015).
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  • Received Date: October 09, 2014
  • Revised Date: August 13, 2015
  • Published Date: November 09, 2016
    Graphical Abstract
    • Abstract
  • We propose a new efficient algorithm named Cuckoo search fault diagnosis (CSFD) to solve system-level fault diagnosis problem. KMP algorithm is proposed for initialization based on the K-means partition algorithm; a fitness function is designed according to the equation constraints satisfied by the test model; the binary mapping method is advanced by optimizing existing binary mapping algorithm. Experiments show that KMP algorithm significantly reduces the disparity between the initial solution and the actual solution, and CSFD algorithm improves the efficiency and correctness significantly compared with existing typical swarm intelligence diagnosis algorithm.
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    • References (15)
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