A missing variable approach for decentralized statistical process monitoring. (October 2018) - Tong, Chudong; Lan, Ting; Zhu, Ying; Shi, Xuhua; Chen, Yuwei
Abstract: The main focus of the current work is on the investigation and application of a missing variable approach in principal component analysis (PCA) model for decentralized process monitoring purpose. Given that the widely studied PCA algorithm can recover the correlations between measured variables, a missing variable approach is employed for computing score estimation error and residual estimation error from the developed PCA model. Through assuming but only one variable is missing in sequence, the residual between the actual and estimated components is generated and then monitored instead of the original data. The presented method implements a missing variable based offline modeling and online monitoring in a decentralized manner. Generally, the generated residual is expected to follow or at least become much closer to a Gaussian distribution, the resulted model has no restriction on Gaussian distributed dataset and can achieve salient monitoring performance in contrast to its counterparts. Finally, its superiority and effectiveness have been demonstrated by conducting comparisons on two industrial examples. Highlights: Decentralized process monitoring based on a missing variable approach is proposed. Residual generation for statistical process monitoring is first considered. Experimental results demonstrated the superiority and effectiveness of the presented method.