Abstract
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.
| Original language | English |
|---|---|
| Article number | 7 |
| Pages (from-to) | 4477-4487 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 18 |
| Issue number | 7 |
| Early online date | 12 Oct 2021 |
| DOIs | |
| Publication status | Published - Jul 2022 |
| Externally published | Yes |
Funding
The work of Jicong Fan was supported by the Shenzhen Research Institute of Big Data under Grant T00120210002. The work of Tommy W. S. Chow was supported by the National Natural Science Foundation of China under Grant 62073272. The work of S. Joe Qin was supported in part by the National Natural Science Foundation of China under Grant U20A201398 and in part by the City University of Hong Kong under Project 9380123.
Keywords
- Fault detection
- kernel principal component analysis (KPCA)
- matrix completion
- missing data
- statistical process monitoring (SPM)