Imperceptible Attacks on Fault Detection and Diagnosis Systems in Smart Buildings

Automated Fault Detection and Diagnosis (AFDD) systems are critical to safe and efficient operation of smart buildings. A significant amount of building data can be collected and analyzed to detect building component failures. Attacks against such data that are contaminated with small additive disturbances (i.e., adversarial perturbation attacks) could dreadfully impact the performance of such systems while maintaining a high level of imperceptibility. The vulnerability studies of such data attacks is lacking. Specifically, most existing detection and classification models have flat structures, regarded as SingleStage Classifiers (SSCs), are prone to adversarial data perturbation attacks. In this paper, we present a coarse-to-fine Hierarchical Fault Detection and multi-level Diagnosis (HFDD) model, and formulate a mathematical program to derive targeted attacks on the model with respect to a pre-specified target diagnosis level. Two algorithms are developed based on convex relaxations of the formulated program for non-targeted attacks. An Alternating Direction Method of Multipliers (ADMM)-based solver is developed for the convex programs. Extensive experiments are conducted using two real-world datasets of measurements from air handling units and chillers, demonstrating the feasibility of the proposed attacks with regard to misclassification rate and imperceptibility of the attack. We also show that the HFDD is more robust to disturbances than SSCbased fault detection and multi-level diagnosis systems.