Fault detection is important for effective and safe process operation. Partial least squares (PLS) has been used successfully in fault detection for multivariate processes with highly correlated variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T and the Q statistics are not well suited to detect small faults because they only use information about the process in the most recent observation. Exponentially weighed moving average (EWMA), however, has been shown to be more sensitive to small shifts in the mean of process variables. In this paper, a PLS-based EWMA fault detection method is proposed for monitoring processes represented by PLS models. The performance of the proposed method is compared with that of the traditional PLS-based fault detection method through a simulated example involving various fault scenarios that could be encountered in real processes. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS method.
|Original language||English (US)|
|Title of host publication||2016 International Conference on Control, Decision and Information Technologies (CoDIT)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||6|
|State||Published - Oct 20 2016|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors gratefully acknowledge financial support from Qatar National Research Fund, National Priorities Research Fund grant number NPRP-7-1172-2-439.