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http://dx.doi.org/10.3837/tiis.2021.04.001

Quality Variable Prediction for Dynamic Process Based on Adaptive Principal Component Regression with Selective Integration of Multiple Local Models  

Tian, Ying (School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology)
Zhu, Yuting (School of Mechanical Engineering, University of Shanghai for Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.4, 2021 , pp. 1193-1215 More about this Journal
Abstract
The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ2 - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.
Keywords
Adaptive Variable Prediction; Moving Window; Bayesian; Hypothesis Testing; Selective Integration;
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