This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.
|Original language||English (US)|
|Title of host publication||2016 IEEE Symposium Series on Computational Intelligence (SSCI)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|State||Published - Feb 16 2017|