An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further

A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes

Soft sensors are inferential estimators when the employment of hardware sensors is inapplicable, expensive, or difficult in industrial plant processes. Currently, a simple soft sensor, namely locally weighted partial least squares (LW-PLS), which can cope with the nonlinearity of the process, has been developed. However, LW-PLS exhibits the disadvantages of handling strong nonlinear process data. To address this problem, Kernel functions are integrated into LW-PLS to form locally weighted Kernel partial least squares (LW-KPLS). Notice that a minimal study was carried out on the impact of different kernel functions that have not been integrated with the LW-KPLS, in which this model has the potential to be applied to different chemical-related nonlinear processes. Thus, this study investigates the predictive performance of LW-KPLS with several different Kernel functions using three nonlinear case studies. As the results, the predictive performances of LW-KPLS with Polynomial Kernel are better than other Kernel functions. The values of root-mean-square errors (RMSE) and error of approximation (Ea) for the training and testing dataset by utilizing this Kernel function are the lowest in their respective case studies, which are 34.60% to 95.39% lower for RMSEs values and 68.20% to 95.49% smaller for Ea values