An adaptive CRPF fault diagnosis method under strong noise condition

doi:10.13700/j.bh.1001-5965.2017.0353

	An adaptive CRPF fault diagnosis method under strong noise condition
	Wang, Jinhua 1,2,3; Cao, Jie 1,2,3; Li, Wei 2; Huang, Ling 1,2,3
刊名	Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics
	2018-05-01
卷号	44 期号:5 页码:923-930
关键词	Failure analysis Monte Carlo methods Adaptive thresholds Confidence interval Cost reference particle filters (CRPF) Discriminant functions Fault diagnosis method Misdiagnosis rate Non-linear non-Gaussian Strong noise
ISSN号	10015965
DOI	10.13700/j.bh.1001-5965.2017.0353
英文摘要	Aimed at the problem of low precision in fault diagnosis of nonlinear non-Gaussian system due to serious noise interference under the actual working condition, this paper puts forward a new fault diagnosis method, which can adaptively update the state transition density variance of a cost reference particle filter (CRPF). By designing the correlation discriminant function between the measurement value and the prior state, the variance of the state transition density was adjusted adaptively according to the magnitudes of noise and error, and the adaptability of the algorithm to strong noise interference is dramatically enhanced. Furthermore, the method for designing adaptive threshold of residual was studied, and the sliding window was also introduced to calculate the mean of interval instead of the mean and variance of the adaptive threshold based on parameter confidence interval, which was expected to reduce the calculation time under the premise of ensuring the accuracy of fault diagnosis. Taking 160 MW fuel unit as an example, drum level sensor fault diagnoses under different strong noise conditions were analyzed. From the results, it is found that the accuracy of fault diagnosis in the complex noise environment is obviously improved and the computation time is greatly reduced. © 2018, Editorial Board of JBUAA. All right reserved.
语种	中文
出版者	Beijing University of Aeronautics and Astronautics (BUAA)
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/114442]
专题	电气工程与信息工程学院
作者单位	1.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou; 730050, China; 2.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 3.National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Wang, Jinhua,Cao, Jie,Li, Wei,et al. An adaptive CRPF fault diagnosis method under strong noise condition[J]. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics,2018,44(5):923-930.
APA	Wang, Jinhua,Cao, Jie,Li, Wei,&Huang, Ling.(2018).An adaptive CRPF fault diagnosis method under strong noise condition.Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics,44(5),923-930.
MLA	Wang, Jinhua,et al."An adaptive CRPF fault diagnosis method under strong noise condition".Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 44.5(2018):923-930.