文章基本信息

标题：Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows
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作者：Le Yao ; Zhiqiang Ge ; Xiaofeng Yuan 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：10778-10783
DOI：10.1016/j.ifacol.2017.08.2334
语种：English
出版社：Elsevier
摘要：AbstractAn adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Bayes’ rule for quality estimation. However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor.
关键词：KeywordsSupervised latent factor analysisWeighted modelExpectation Maximization algorithmAdaptive soft sensorMoving windowModel integrationQuality prediction