Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory
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Conference PaperAuthors
Harrou, Fouzi
Sun, Ying

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2016-01-11Online Publication Date
2016-01-11Print Publication Date
2015-12Permanent link to this record
http://hdl.handle.net/10754/595955
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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.Citation
Fouzi, H., & Sun, Y. (2015). Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory. 2015 IEEE Symposium Series on Computational Intelligence. doi:10.1109/ssci.2015.64Conference/Event name
2015 IEEE Symposium Series on Computational Intelligenceae974a485f413a2113503eed53cd6c53
10.1109/SSCI.2015.64