Kernel-Based Statistical Process Monitoring and Fault Detection in the Presence of Missing Data

Missing data widely exist in industrial processes and lead to difficulties in modeling, monitoring, fault diagnosis, and control. In this article, we propose a nonlinear method to handle the missing data problem in the offline modeling stage or/and the online monitoring stage of statistical process monitoring. We provide a fast incremental nonlinear matrix completion (FINLMC) method for missing da...