Fault Detection and Isolation Applied to a Ship Propulsion Benchmark

Abstract: This paper describes a fault detection and isolation (FDI) scheme performed on the benchmark problem of a ship propulsion system. The model used for the ship propulsion system is nonlinear, for which two types of additive sensor faults, an additive incipient fault, and a multiplicative parametric fault are simulated. The estimation of the fault severity is accomplished by using an adaptive two-stage extended Kalman filter. A set of statistical detection variables is formed from the residuals of the bias and measurement estimates of the filter. These variables are then used in a threshold based hypothesis test to declare the occurrence of a fault and through a binary logic filter to identify the fault type. The simulation results showed that the developed fault detection and isolation scheme fulfilled some of the benchmark requirements reasonably well in the face of some prescribed perturbations in the model and disturbances of external signals.