Fault detection and isolation in uncertain dynamic systems using composite optimization and inferential sensing

Abstract

Inferential sensing is a cost-effective and reliable approach to replace expensive and impractical hardware sensing, while yielding robust fault detection and isolation (FDI) during system maintenance or operation. Yet, their high computational footprint necessitates the employment of advanced and efficient estimation techniques, along with symbolic mathematics and automatic differentiation. Herein, we attempt to combine model-driven sensor selection methods with inferential sensing to accurately identify faults in the presence of system noise and uncertainty. During sensor selection, we employ criteria from information theory to maximize the estimability of system faults from available system measurements. The most informative sensor set stems from a comparison of all possible sensor suites via information theoretic criteria expressed as functions of the Fisher Information Matrix. Optimal inferential sensors are calculated through a novel fusion of symbolic regression and information theory. For built-in test deployment, we utilize k-Nearest Neighbors classification to assess the capability of each sensor suite for all plausible instantiations of uncertainty and system noise, and evaluate FDI performance benefits from the inclusion of inferential sensor(s). The proposed computational framework is deployed with steady-state and dynamic models of a cross-flow plate-fin heat exchanger, at varying levels of measurement noise and uncertainty. As shown, the augmented system of composite sensors (i.e., inferential and hardware) provides accurate information on system fault(s) and reduces the evidence of uncertainty. Compared to traditional sensor approaches, the proposed techniques are shown to be more precise while simultaneously granting increased insight into details otherwise hidden by noise. Thus, they form a robust and reliable tool when conducting fault diagnosis and possibly toward system safety.