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 data imputation, which enables us to use kernel methods such as kernel principal component analysis to monitor nonlinear multivariate processes even when there are missing data. We also provide theoretical analysis for the effectiveness of the proposed method. Experiments show that the proposed method can reduce the false alarm rate and improve the fault detection rate in nonlinear processing monitoring with missing data. The proposed FINLMC method can also be used to solve missing data in other problems such as classification and process control.