Abstract
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 language | English (US) |
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Pages (from-to) | 365-377 |
Number of pages | 13 |
Journal | Journal of Loss Prevention in the Process Industries |
Volume | 40 |
DOIs | |
State | Published - Jan 29 2016 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Food Science
- General Chemical Engineering
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality