Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory

Fouzi Harrou, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Accurate and effective fault detection and diagnosis of modern engineering systems is crucial for ensuring reliability, safety and maintaining the desired product quality. In this work, we propose an innovative method for detecting small faults in the highly correlated multivariate data. The developed method utilizes partial least square (PLS) method as a modelling framework, and the symmetrized Kullback-Leibler divergence (KLD) as a monitoring index, where it is used to quantify the dissimilarity between probability distributions of current PLS-based residual and reference one obtained using fault-free data. The performance of the PLS-based KLD fault detection algorithm is illustrated and compared to the conventional PLS-based fault detection methods. Using synthetic data, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional methods, especially when data are highly correlated and small faults are of interest.
Original languageEnglish (US)
Title of host publication2015 IEEE Symposium Series on Computational Intelligence
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages383-388
Number of pages6
ISBN (Print)9781479975600
DOIs
StatePublished - Jan 11 2016

Fingerprint

Dive into the research topics of 'Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory'. Together they form a unique fingerprint.

Cite this