PLS-based EWMA fault detection strategy for process monitoring. (July 2015) - Harrou, Fouzi; Nounou, Mohamed N.; Nounou, Hazem N.; Madakyaru, Muddu
Abstract: Fault detection (FD) and diagnosis in industrial processes is essential to ensure process safety and maintain product quality. Partial least squares (PLS) has been used successfully in process monitoring because it can effectively deal with highly correlated process variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T 2 and the Q statistics are ill suited to detect small faults because they only use information from the most recent observations. Other univariate statistical monitoring methods, such as the exponentially weighted moving average (EWMA) control scheme, has shown better abilities to detect small faults. However, EWMA can only be used to monitor single variables. Therefore, the main objective of this paper is to combine the advantages of the univariate EWMA and PLS methods to enhance their performances and widen their applicability in practice. The performance of the proposed PLS-based EWMA FD method was compared with that of the conventional PLS FD method through two simulated examples, one using synthetic data and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS, especially in the presence of faults with small magnitudes. Highlights: PLS-based EWMA fault detection strategy is developed. EWMA scheme is introduced to overcome the shortcomings of the conventional PLS. The developed scheme extends the advantage of EWMA when small faults are of interest. Examples show effectiveness of developed PLS-based EWMA fault detection method.