This paper investigates the problem of event-triggered fault detection for discrete-time networked systems subject to packet dropout. The main aim of the proposed approach is to efficiently use the communication network and reduce the signal transmission over the network. Toward this goal, a dynamic parity space event-triggered fault detection approach is developed to generate a robust residual signal. Moreover, an adaptive threshold is utilized to overcome the limitations of static thresholds. The efficiency of proposed approach is experimentally demonstrated and validated for a laboratory three-tank system.
Summary
The problem of modeling and fault detection in an electromechanical system having a graphical representation is considered. For this system's dynamics, motivated by a desire to provide a precise fault detection procedure using a unified energy‐based framework, 2 energy‐based graphical formalisms are presented and compared: the Hamiltonian Bond Graph and the Causal Ordering Graph. Firstly, easily calculated from the Hamiltonian Bond Graph representation covering the causal energetic paths, the energetic residual generators are systematically deduced by comparing the energy quantity in both normal and faulty situations. Secondly, the causal and structural properties of the Causal Ordering Graph tool can be used to design an observer devoted to fault detection so as to derive directly energy‐based residual generators in terms of faults. To highlight the effectiveness and applicability of 2 proposed approaches, simulation results on DC motor are provided and discussed. The performances of these fault detection schemes are compared and discussed in detail with respect to existing methods.
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