基于主成分分析及多维高斯贝叶斯的超声流量计故障智能诊断方法

doi:10.3969/j.issn.1000-1158.2020.12.08

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Abstract The intelligent failure diagnosis method for equipment based on multivariate Gaussian Bayesian model was proposed. The method included data screening and structural analysis,data dimensionality reduction, model construction, verification and diagnostic results analysis. When using principal component analysis method for dimensionality reduction, it was shown that the selection of dimensionality reduction parameters has great influence on diagnosis result. The diagnostic effect varied with the property and quantity of samples. A publicly published ultrasonic flowmeter database was used to verified the method. By performing 280 and 550 failure diagnoses on two type of ultrasonic flowmeters (type B and type C) respectively, it was found that the correct failure recognition rate were up to 99.3% and 95.1%. Compared with the nearest neighbor KNN clustering analysis algorithm, this failure diagnosis method shows certain advantages.

Key words： metrology ultrasonic flowmeter Gaussian Bayesian smart failure diagnosis primary component analysis

Received: 17 October 2019 Published: 08 December 2020

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TB937

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	Articles by authors
	ZHU Jian-xin
	Lü Bao-lin
	QIAO Song
	WANG Yi-fang
	CHEN Jia-hong

Cite this article:

ZHU Jian-xin,Lü Bao-lin,QIAO Song, et al. Application of Primary Component Analysis and Multivariate Gaussian Bayesian Method on Intelligent Failure Diagnosis of Ultrasonic Flowmeter[J]. Acta Metrologica Sinica, 2020, 41(12): 1494-1499.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.12.08 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I12/1494

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