Key points are not available for this paper at this time.
Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm has proven its capability to track gradual deterioration with a good accuracy. On the other hand, its response to rapid deterioration is either a long delay in recognising the fault, and/or a spread of the estimated fault on several components. The main reason of this deficiency lies in the transition model of the parameters that is blended in the Kalman filter and assumes a smooth evolution of the engine condition. This contribution reports the development of an adaptive diagnosis tool that combines a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements a Generalised Likelihood Ratio Test in order to detect and estimate an abrupt fault. The enhancement in terms of accuracy and reactivity brought by this adaptive Kalman filter is highlighted for a variety of simulated fault cases that may be encountered on a commercial aircraft engine.
Borguet et al. (Tue,) studied this question.