Journal of Information Processing Systems

  • 제16권3호
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  • Pages.530-540
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  • 2020
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Fault Diagnosis Method Based on High Precision CRPF under Complex Noise Environment

  • Wang, Jinhua (College of Electrical and Information Engineering, Lanzhou University of Technology, Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology) ;
  • Cao, Jie (College of Electrical and Information Engineering, Lanzhou University of Technology, Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology)
  • 투고 : 2017.09.26
  • 심사 : 2018.03.30
  • 발행 : 2020.06.30
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초록

In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.

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과제정보

This paper is funded by the National Natural Science Foundation of China (No. 61763028) and the Natural Science Foundation of Gansu (No. 1506RJZA105) and the open project of Key Laboratory of Gansu Advanced Control for Industrial Processes (No. XJK201805).

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