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Application of Primary Component Analysis and Multivariate Gaussian Bayesian Method on Intelligent Failure Diagnosis of Ultrasonic Flowmeter |
ZHU Jian-xin1,2,LÜ Bao-lin1,2,QIAO Song1,2,WANG Yi-fang1,CHEN Jia-hong1,2 |
1. Hefei General Machinery Research Institute Co.Ltd, Hefei, Anhui 230031, China
2. National Technology Research Center for Safety Engineering of Pressure Vessels and Pipelines, Hefei, Anhui 230031, China |
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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.
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Received: 17 October 2019
Published: 08 December 2020
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