Art-2 and Multiscale Art-2 for On-Line Process Fault Diagnosis - Validation via Industrial Case Studies and Monte Carlo Simulation

Abstract

This paper provides experimental validation and insight into a new method of process fault detection based on the integration of multiscale signal representation and scale specific clustering-based diagnosis. The multisca1e ART-2 (MSART-2) algorithm models normal process operation as clusters of wavelet coefficients at different scales. It detects a process change when one or more wavelet coefficients of test data violate similarity thresholds with respect to clusters of normal data at that scale. In contrast to most other multiresolution schemes, this framework exploits clustering behavior of wavelet coefficients of multiple variables for the purpose of scale selection and feature extraction. Detailed performance comparisons, based on rigorous Monte-Carlo simulations as well as industrial data from a large scale petrochemical process, are provided. Our results show that MSART -2 significantly improves the detection performance of the ART -2 detection algorithm over a broad range of process anomalies.