Multivariate Statistical Process Monitoring (MSPM) is used normally in industrial processes where large number of process variables are involved and complex correlations exists between the process variables. In this paper an efficient fault isolation based process monitoring method is discussed using variable selection. Sparse Partial Least Square (SPLS) is a special type of discriminant analysis for doing variable selection based on the classification between normal process data and actual process measurement data. The SPLS based variable selection for fault isolation is explained here by implementing with real time industrial process data.
Volume 12 | 08-Special Issue
Pages: 1236-1240
DOI: 10.5373/JARDCS/V12SP8/20202644