This paper addresses the problem of fault tolerant estimation and the design of fault tolerant sensor networks. Fault tolerance is defined with respect to a given estimation objective, namely a given functional of the system state should remain observable when sensor failures occur. Redundant and minimal sensor sets are defined and organized into an automaton which contains all the subsets of sensors such that the estimation objective can be achieved. Three criteria, which evaluate the system fault tolerance with respect to sensor failures when a reconfiguration strategy is used, are introduced: (strong and weak) redundancy degrees (RD), sensor network reliability (R), and mean time to non-observability (MTTNO). Sensor networks are designed by finding redundant sensor sets whose RD and/or R and/or MTTNO are larger than some specified values. A ship boiler example is developed for illustration.

This paper presents a procedure dealing with the issue of fault detection and isolation (FDI) using nonlinear analytical redundancy (NLAR) technique applied in a proton exchange membrane (PEM) fuel cell system based on its mathematic model. The model is proposed and simplified into a five orders state space representation. The transient phenomena captured in the model include the compressor dynamics, the flow characteristics, mass and energy conservation and manifold fluidic mechanics. Nonlinear analytical residuals are generated based on the elimination of the unknown variables of the system by an extended parity space approach to detect and isolate actuator and sensor faults. Finally, numerical simulation results are given corresponding to a faults signature matrix.

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International audienceThe main interest of this paper is the generation of residuals using nonlinear analytical redundancy relations (NARR) for sensor and actuator faults detection and isolation of an autonomous electric vehicle called RobuCar. The relations are used to detect and to isolate sensor or actuator faults based on the elimination of the unknown variables of the system. The nonlinear system of a four wheel steering vehicle RobuCar modelled in state space is used in our work. The method is well explained in detail and applied on the nonlinear vehicle model