Mixture semisupervised probabilistic principal component regression model with missing inputs. (4th August 2017)

• Record Type: Journal Article
• Authors: Sedghi, Shabnam; Sadeghian, Anahita; Huang, Biao
• Is Part Of: Computers & chemical engineering. Volume 103(2017)
• Page Start: 176
• Page End: 187
• Publication Date: 2017-08-04
• Subjects: Probabilistic principal component regression -- Missing data -- Mixture semisupervised modeling -- Soft sensor design; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.03.015
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract : Highlights: Mixture semi-supervised probabilistic PCR model is extended to a general modeling solution. The problem of missing data in both input and output is addressed. The validity and performance for soft sensor design are demonstrated through examples including the benchmark TE process. Abstract: Principal component regression (PCR) has been widely used as a multivariate method for data-based soft sensor design. In order to take advantage of probabilistic features, it has been extended to probabilistic PCR (PPCR). Commonly, industrial processes operate in multiple operating modes. Moreover, in most cases, outputs are measured at a slower rate than inputs, and for each sample of input variable, its corresponding output may not always exist. These two issues have been solved by developing the mixture semi-supervised PPCR (MSPPCR) method. In this paper, we extend this developed model to the case of simultaneous missing data in both input and output. Missing data in multidimensional input space constitutes a significantly more challenging problem. Missing input data occurs frequently in industrial plants because of sensor failure and other problems. We develop and solve the MSPPCR model by using the expectation-maximization (EM) algorithm to deal with missing inputs, in addition to missing outputs and multi-mode conditions. Finally, we present two case studies to demonstrate its performance.