TY - GEN
T1 - Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
AU - Madakyaru, Muddu
AU - Harrou, Fouzi
AU - Sun, Ying
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/9
Y1 - 2017/2/9
N2 - 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 as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
AB - 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 as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85016086660&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016086660&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2016.7849880
DO - 10.1109/SSCI.2016.7849880
M3 - Conference contribution
AN - SCOPUS:85016086660
T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Y2 - 6 December 2016 through 9 December 2016
ER -