Fault Diagnosis Method Based on High Precision CRPF under Complex Noise Environment
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초록
In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis ofcomplex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) isproposed. By optimizing the low confidence particles to replace the resampling process, this paper improvedthe problem of sample impoverishment caused by the sample updating based on risk and cost of CRPFalgorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtainingsamples. Then, we study the correlation between the current observation value and the prior state. By adjustingthe density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noisescan be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulationanalysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improvedobviously under the background of complex noise.
Abstract
In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis ofcomplex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) isproposed. By optimizing the low confidence particles to replace the resampling process, this paper improvedthe problem of sample impoverishment caused by the sample updating based on risk and cost of CRPFalgorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtainingsamples. Then, we study the correlation between the current observation value and the prior state. By adjustingthe density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noisescan be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulationanalysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improvedobviously under the background of complex noise.
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