Dynamic Partial-Least-Squares-Based Fault Detection for Nonlinear Distributed Parameter Systems

Distributed parameter systems (DPSs) are commonly used to characterize various industrial processes, but the coupling of spatiotemporal data and time delay effects poses challenges for their fault detection. This paper proposes a fault detection method for a class of nonlinear parabolic DPSs with limited sensors. A time/space separation method is first applied to decouple the spatiotemporal data to obtain time coefficients that are available for data-driven modeling. Then, the obtained dominant time coefficients are modeled by a dynamic partial least squares (D-PLS) method. Finally, the residual space is utilized to establish two monitoring statistics and a reference boundary is established with the aid of the mirrored data kernel density estimation. This method exploits the separable characteristics of parabolic DPSs and is a data-driven method that is independent of an explicit mathematical model of the system processes. The proposed method is validated on a curing oven experimental platform, and comparative results with other methods shows that it achieves satisfactory performance in fault detection accuracy and first-time detection timeliness.