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Mode Identification and Fault Detection of Hybrid Dynamical Systems Using Behavior Graphs

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The general principle of model-based Fault Detection and Isolation (FDI) algorithms is to compare the expected behavior of the system, given by a model, with its actual behavior, known through on-line observations. Hybrid Dynamical Systems (HDS) constitute a wide class of common industrial applications, where the behavior is determined by the interaction between continuous and discrete dynamics, i.e. behavioral modes succession. Faults in HDS may corrupt the two dynamics. As a consequence, FDI in HDS consists in three main activities : 1) to identify the current behavioral mode, i.e. to estimate the discrete state; 2) to check if the current mode is coherent with the expected one and 3) to check if the continuous behavior is normal or not. Classical approaches use exhaustive algorithms in order to solve the monitoring of the mode (activities 1-2). In fact, using the known inputs and outputs that may be either continuous or discrete information, each mode of the system is considered as an actual mode candidate. These approaches raise their limits when the number of modes is growing. In that paper, we propose to limit the set of possible mode candidates by using a priori information on the discrete evolution under normal and faulty hypothesis. Two kinds of graphs are derived from the initial hybrid model, namely Normal Behavior Graphs (NBG), Faulty Behavior Graphs (FBG). Using these graphs allows us not only to identify efficiently the actual mode but also to directly interpret (diagnose) the discrete faulty evolution in terms of faults.
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Hybrid Dynamical Systems; Fault Detection and Isolation; Behaviors Graphs; Similarity

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