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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations


Original languageEnglish
Pages (from-to)4477-4487
Number of pages10
Journal / PublicationIEEE Transactions on Industrial Informatics
Issue number7
Online published12 Oct 2021
Publication statusPublished - Jul 2022


Missing data widely exist in industrial processes and lead to difficulties in modelling, monitoring, fault diagnosis, and control. In this paper, we propose a nonlinear method to handle the missing data problem in the offline modelling 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 (KPCA) 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 method FINLMC can also be used to solve missing data in other problems such as classification and process control.

Research Area(s)

  • Data models, fault detection, Informatics, Kernel, kernel PCA, matrix completion, missing data, Neural networks, Principal component analysis, Process monitoring, Statistical process monitoring, Training data