A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine

This paper presents the implementation of an online particle-filtering-based framework for fault diagnosis and failure prognosis in a turbine engine. The methodology considers two autonomous modules, and assumes the existence of fault indicators (for monitoring purposes) and the availability of real-time measurements. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant, and a particle filtering algorithm to calculate the probability of a crack in one of the blades of the turbine; simultaneously computing the state probability density function (pdf) estimates that will be used as initial conditions in the prognosis module. The failure prognosis module, on the other hand, computes the remaining useful life (RUL) pdf of the faulty subsystem in real-time, using a particle-filtering-based algorithm that consecutively updates the current state estimate for a nonlinear state-space model (with unknown time-varying parameters), and predicts the evolution in time of the probability distribution for the crack length. The outcome of the prognosis module provides information about precision and accuracy of long-term predictions, RUL expectations and 95% confidence intervals for the failure condition under study. Data from a seeded fault test is used to validate the proposed approaches.