KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2016-06-13
Print Publication Date2015-12
Permanent link to this recordhttp://hdl.handle.net/10754/621301
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AbstractFault 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.
CitationHarrou F, Sun Y (2015) A measurement-based technique for incipient anomaly detection. 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). Available: http://dx.doi.org/10.1109/ISDA.2015.7489200.
Conference/Event name15th International Conference on Intelligent Systems Design and Applications, ISDA 2015