Application of Characteristic Model-Based Principal Component Analysis in Optimization of Flowmeter Parameters

Author:

Jiang Wenping1ORCID,Jiang Zhencun1ORCID,Wang Lingyang1,Min Jun2,Zhu Yi3,Wang Zhonghui3,Tang Lizhuang3,Sun Limin3

Affiliation:

1. Shanghai Institute of Technology, School of Electrical and Electronic Engineering, No. 100 Haiquan Road, Fengxian District, Shanghai 201418, China

2. Tongji University, No. 1239 Siping Road, Yangpu District, Shanghai 200082, China

3. Bitobar Company, No. 265 Lingdong Street, Tieling, Liaoning 112000, China

Abstract

In complex industrial processes, it is necessary to perform modeling analysis on some industrial systems and find and optimize the factors that have the greatest impact on the results, in order to achieve the optimization of the industrial systems. However, due to the high-level nature or complex working mechanism of complex industrial systems, traditional principal component analysis methods are difficult to apply. Therefore, this paper proposes a characteristic model-based principal component analysis (CMPCA) to perform principal component analysis on complex industrial systems. The differential pressure flowmeter is taken as an example to verify the effectiveness of the method. Flowmeter is an indispensable instrument in measurement, and its accuracy depends on its own structural parameters. However, the measurement accuracy of some flow meters is not high, and the measurement error is large, which affects the normal industrial production process. This method is used to analyze the influence of the structural parameters of the flowmeter on its measurement accuracy, and the four most important structural parameters are found and optimized. The measurement error of the Bitoba flowmeter is reduced from 1% to 0.2%, and the measurement repeatability is reduced from 0.3 to 0.06, which proves the effectiveness of the method.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Reference25 articles.

1. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

2. Learning control systems and intelligent control systems: an intersection of artificial intelligent and automatic control;K. Fu;IEEE Transactions on Automatic Control,1971

3. Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

4. Study on internal flow field characteristics and measurement performance of momentum gas flowmeter;Z. Liu;Acta Metrologica Sinica,2019

5. The ap-Hilbert method for phase difference estimation of Coriolis flowmeter;H. Yang;Chinese Journal of Scientific Instrument,2019

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