Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring

Fouzi Harrou, Ying Sun, Sofiane Khadraoui

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Accurate and effective anomaly detection and diagnosis of modern industrial systems are crucial for ensuring reliability and safety and for maintaining desired product quality. Anomaly detection based on principal component analysis (PCA) has been studied intensively and largely applied to multivariate processes with highly cross-correlated process variables; howver conventional PCA-based methods often fail to detect small or moderate anomalies. In this paper, the proposed approach integrates two popular process-monitoring detection tools, the conventional PCA-based monitoring indices Hotelling’s T2 and Q and the exponentially weighted moving average (EWMA). We develop two EWMA tools based on the Q and T2 statistics, T2-EWMA and Q-EWMA, to detect anomalies in the process mean. The performances of the proposed methods were compared with that of conventional PCA-based anomaly-detection methods by applying each method to two examples: a synthetic data set and experimental data collected from a flow heating system. The results clearly show the benefits and effectiveness of the proposed methods over conventional PCA-based methods.
Original languageEnglish (US)
Pages (from-to)365-377
Number of pages13
JournalJournal of Loss Prevention in the Process Industries
StatePublished - Jan 29 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Food Science
  • Chemical Engineering(all)
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality


Dive into the research topics of 'Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring'. Together they form a unique fingerprint.

Cite this