This paper presents a sound and complete fault detection approach for cyber-physical systems represented by hidden-mode switched affine models with time varying parametric uncertainty. The fault detection approach builds upon techniques from model invalidation. In particular, a set-membership approach is taken where the noisy input-output data is compared to the set of behaviors of a nominal model. As we show, this set-membership check can be reduced to the feasibility of a mixed-integer linear programming (MILP) problem, which can be solved efficiently by leveraging the state-of-the-art MILP solvers. In the second part of the paper, given a system model and a fault model, the concept of T -detectability is introduced. If a pair of system and fault models satisfies T -detectability property for a finite T , this allows the model invalidation algorithm to be implemented in a receding horizon manner, without compromising detection guarantees. In addition, the concept of weak-detectability is introduced which extends the proposed approach to a more expressive class of fault models that capture language constraints on the mode sequences. Finally, the efficiency of the approach is illustrated with numerical examples motivated by smart building radiant systems.

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A fully automated method for segmentation of neonatal skull in Magnetic Resonance (MR) images for source localization of electrical/magnetic encephalography (EEG/MEG) signals is proposed. Finding the source of these signals shows the origin of an abnormality. We propose a hybrid algorithm in which a Bayesian classifying framework is combined with a Hopfield Neural Network (HNN) for neonatal skull segmentation. Due to the non-homogeneity of skull intensities in MR images, local statistical parameters are used for adaptive training of Hopfield neural network based on Bayesian classifier error. The experimental results, which are obtained on high resolution T1-weighted MR images of nine neonates with gestational ages between 39 and 42 weeks, show 65% accuracy which consistently exhibits our scheme's superiority in comparison with previous neonatal skull segmentation methods.

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This paper considers the problem of fault detection and isolation (FDI) for switched affine models. We first study the model invalidation problem and its application to guaranteed fault detection. Novel and intuitive optimizationbased formulations are proposed for model invalidation and T -distinguishability problems, which we demonstrate to be computationally more efficient than an earlier formulation that required a complicated change of variables. Moreover, we introduce a distinguishability index as a measure of separation between the system and fault models, which offers a practical method for finding the smallest receding time horizon that is required for fault detection, and for finding potential design recommendations for ensuring T -distinguishability. Then, we extend our fault detection guarantees to the problem of fault isolation with multiple fault models, i.e., the identification of the type and location of faults, by introducing the concept of I-isolability. An efficient way to implement the FDI scheme is also proposed, whose run-time does not grow with the number of fault models that are considered. Moreover, we derive bounds on detection and isolation delays and present an adaptive scheme for reducing isolation delays. Finally, the effectiveness of the proposed method is illustrated using several examples, including an HVAC system model with multiple faults.