A new integrated on-line fuzzy clustering and segmentation methodology with adaptive PCA approach for process monitoring and fault detection and diagnosis

Abstract

A new on-line fuzzy clustering-based algorithm is developed using integration of an adaptive principal component analysis approach with a weighted fuzzy C-means (WFCM) methodology for process fault detection and diagnosis (FDD) applications. The proposed algorithm is based on the segmentation of measured multivariate time series process data through a sliding window scheme being realized in a bottom-up cluster merging approach to enable detection of probable changes embedded in their hidden structure. The method recursively maintain updated PCA models and their corresponding fuzzy membership functions based on the most recent arrival of each independent chunk of process data. The extracted chunk features are then retained in the memory to be merged using a new on-line fuzzy C-means methodology before incoming of the following chunks of data. A new formula is then presented for cluster merging improvement by incorporating an on-line weight to address the issue of cluster's weight updating in the on-line WFCM methodology. The cluster merging mechanism is coordinated by a compatibility criterion, utilizing both similarities of the adapted clusters-based PCA models and their center closeness. The proposed algorithm has been evaluated on an artificial case study and Tennessee Eastman benchmark process plant. The observed performances demonstrate promising capabilities of the proposed algorithm to successfully detect and diagnose the introduced fault scenarios.