A model-based probabilistic approach for fault detection and identification with application to the diagnosis of automotive engines

A model based parameter and state estimation technique is presented toward fault diagnosis in dynamic systems. The methodology is based on the representation of the system dynamics in terms of transition probabilities between user-specified sets of magnitude intervals of system parameters and state variables during user-specified time intervals. These intervals may reflect noise in the monitored data, random changes in the parameters, or modeling uncertainties in general. The transition probabilities are obtained from a given system model that yields the current values of the state variables in discrete time from their values at the previous time step and the values of the system parameters at the previous time step. Implementation of the methodology on a simplified model of the air, inertial, fuel, and exhaust dynamics of the powertrain of a vehicle shows that the methodology is capable of estimating the system parameters and tracking the unmonitored dynamic variables within the user-specified magnitude intervals.

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