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.
Highlights
In the industrial production process, with the increase of testing means, the production line can provide a large number of production data for the application of engineers
For complex industrial systems [1], traditional dynamics modeling is difficult to apply due to its high order or complex working mechanism, and even if it is applied, mathematical methods are needed for downscaling modeling: For the simplification of single-input single-output models of large power systems, a lot of people study model simplification method based on SVD-Krylov subspace projection methods, which aims to rely on singular value decomposition and Krylov subspace methods for order reduction system. e intelligent method is mainly applied in neural networks, fuzzy logic, genetic algorithm, etc. is method can solve the partial open-loop control problem without the need for an accurate dynamic model
Yichuan Fu and Zhiwei Gao et al proposed classifying the actuator and sensor faults of wind power equipment based on fast Fourier transform (FFT) and uncorrelated multilinear principal component analysis (UMPCA), which were tested and verified on wind turbines [2]
Summary
In the industrial production process, with the increase of testing means, the production line can provide a large number of production data for the application of engineers. E characteristic modeling method was proposed by Wu Hongxin Academician of the Chinese Academy of Sciences in the 1980s [3], which broke through the original framework of modeling controlled objects and provided a new idea for the modeling of high-order complex systems with unknown parameters and orders. (2) e measurement accuracy needs to be further improved and the measurement reproducibility is expected to be reduced to an ideal level [8,9,10,11,12] To address these two problems, a characteristic modelbased principal component analysis method is proposed in this paper, and the structural parameters of the flowmeter are optimized using this method. A three-dimensional simulation model was performed on the flowmeter, and the optimized structural parameters were tested in the simulation environment and verified in actual industrial production. e second part introduces the principle of the characteristic model, the principal component analysis method based on the characteristic model, and the flowmeter simulation design method. e third part is the result of the flowmeter simulation modeling and the results of the principal component analysis method based on the characteristic model on flowmeter parameter analysis. e fourth part is the conclusion
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