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An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments

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  • Yifei Yang
  • Minjia Tan
  • Yuewei Dai

Abstract

A ship power equipments’ fault monitoring signal usually provides few samples and the data’s feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision which are induced by optimizing the kernel function parameter and penalty factor of LSSVM, an improved Cuckoo Search (CS) algorithm is proposed for the purpose of parameter optimization. Based on the dynamic adaptive strategy, the newly proposed algorithm improves the recognition probability and the searching step length, which can effectively solve the problems of slow searching speed and low calculation accuracy of the CS algorithm. A benchmark example demonstrates that the CS-LSSVM algorithm can accurately and effectively identify the fault pattern types of ship power equipments.

Suggested Citation

  • Yifei Yang & Minjia Tan & Yuewei Dai, 2017. "An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0171246
    DOI: 10.1371/journal.pone.0171246
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    Cited by:

    1. Li Guangfu & Wang Xu & Ren Jia, 2020. "Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-22, June.

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