Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Yao, Le; Ge, Zhiqiang; Yuan, Xiaofeng; Wang, Peiliang
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 10778
• Page End: 10783
• Publication Date: 2017-07
• Subjects: Supervised latent factor analysis -- Weighted model -- Expectation Maximization algorithm -- Adaptive soft sensor -- Moving window -- Model integration -- Quality prediction; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.2334
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: An 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.