A novel anomaly detection scheme for high dimensional systems using Kantorovich distance statistic

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3 Citations (Scopus)


The partial least squares (PLS) is a commonly applied multi-variate method in anomaly detection problems. The PLS strategy has been amalgamated with T2 and squared prediction error (SPE) based statistical indicators to detect anomalies in process. These traditional indicators have few setbacks that has made them ineffective in monitoring applications. Hence, a statistical indicator based on Kantorovich distance (KD) is proposed for detecting sensor anomalies in this study. The proposed strategy integrates anomaly indicator based on KD metric with PLS based multi-variate method. The KD metric computes difference between the residuals of anomaly-free data and the data with anomaly and uses this distance as an indicator of anomaly. The proposed strategy’s critical feature is that a single anomaly indicator is sufficient to be integrated with PLS modeling framework. The Tennessee Eastman process benchmark and experimental distillation column processes data are used for assessing the performance of the proposed strategy. Further, comparisons have been provided between KD, T2, SPE and Generalized Likelihood Ratio indicators. The results demonstrate the superiority of the KD statistical indicator in detecting sensor anomalies in comparison to the traditional indicators of PLS based strategy. The KD indicator integrated with PLS framework also enhances the detection of small magnitude anomalies.

Original languageEnglish
Pages (from-to)3001-3010
Number of pages10
JournalInternational Journal of Information Technology (Singapore)
Issue number6
Publication statusPublished - 10-2022

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics
  • Electrical and Electronic Engineering


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