A measurement-based technique for incipient anomaly detection

Fouzi Harrou, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Fault detection is essential for safe operation of various engineering systems. Principal component analysis (PCA) has been widely used in monitoring highly correlated process variables. Conventional PCA-based methods, nevertheless, often fail to detect small or incipient faults. In this paper, we develop new PCA-based monitoring charts, combining PCA with multivariate memory control charts, such as the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes. The multivariate control charts with memory are sensitive to small and moderate faults in the process mean, which significantly improves the performance of PCA methods and widen their applicability in practice. Using simulated data, we demonstrate that the proposed PCA-based MEWMA and MCUSUM control charts are more effective in detecting small shifts in the mean of the multivariate process variables, and outperform the conventional PCA-based monitoring charts. © 2015 IEEE.
Original languageEnglish (US)
Title of host publication2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)9781467387095
StatePublished - Jun 13 2016


Dive into the research topics of 'A measurement-based technique for incipient anomaly detection'. Together they form a unique fingerprint.

Cite this