Feature selection for fault detection systems: Application to the Tennessee Eastman Process

A fault detection system based on data mining techniques is developed in this work. A novel concept of feature selection based on the k-way correlation is introduced and used to detect redundant measures relevant features (strong and weak relevant) and/or redundant ones is introduced. The authors propose to apply STRASS, a contextual filter algorithm to identify the relevant features on simulated data collected from the Tennessee Eastman chemical plant simulator. In effect the TEP process has been studied in many articles and three specific faults are not discriminated with a myopic filter algorithm. The results obtained by STRASS are compared to those obtained with reference feature selection algorithms. The features selected by STRASS reduced the data correlation and the overall misclassification for the testing set using K-nearest-neighbor decreased further to 0.8%.