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Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used asdoi:10.1109/ssci.2016.7849880 dblp:conf/ssci/MadakyaruHS16 fatcat:rsnuh2dyjbeijob7q2jbp6ihqa