Robust probabilistic principal component regression with switching mixture Gaussian noise for soft sensing

• Published in: Chemometrics and intelligent laboratory systems Vol. 222; p. 104491
• Main Authors: Sadeghian, Anahita, Magbool Jan, Nabil, Wu, Ouyang, Huang, Biao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.03.2022
• Subjects: Expectation maximization (EM) algorithm; Outliers; Probabilistic principal component regression (PPCR); Robust predictive models; Robustness; Soft sensors; Switching Gaussian mixture noise; Outliers; Robust predictive models; Switching Gaussian mixture noise; Soft sensors; Probabilistic principal component regression (PPCR); Expectation maximization (EM) algorithm; Robustness
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2022.104491

In the era, that data collection is not as challenging as before, data-driven process modeling for prediction of unmeasurable or expensive-to-measure variables is gaining popularity. Probabilistic principal component analysis has powerful features for modeling such as considering uncertainty and dealing with high-dimensional process data. Although data collection is more attainable these days, low quality of data still diminishes model performance. High-fidelity modeling requires high-quality data. The focus of this work is to deal with outlying observations by developing a Robust Probabilistic Principal Component Regression (RPPCR). Here, we have investigated a scenario of mixture Gaussian switching measurement noise to mimic certain type of outliers in a forward-looking approach that extends our previous work. A rigorous modeling approach that can handle switching noise and the solution methodology are discussed in detail. Two case studies, a numerical illustrative example and a real industrial counterpart, are considered to verify the robustness of proposed model. •The measurement noise model possesses switching characteristics which describes switching of the noise model between the regular Gaussian and scaled Gaussian for different sample points.•The formulated problem is solved using the Expectation Maximization algorithm.•The efficacy of the proposed methods are demonstrated through simulated, and industrial datasets.