A Lebesgue-Time-Space-Model and Particle Filter based Diagnosis and Prognosis Method

In order to reduce the computation requirement of fault diagnosis and prognosis (FDP) method, the Lebesgue- sampling-based FDP (LS-FDP) is proposed recently. So far from now, it has been successfully applied in many different situations. However, the design of LS-FDP is more complicated than traditional Riemann sampling-based FDP (RS-FDP). To simplify the design of LS-FDP, a Lebesgue-Time-Space-Model and Particle Filter based FDP (LTSM-PF-FDP) method is proposed in this paper. This LTSM-PF-FDP solution, designed in Lebesgue sampling framework but with a simpler structure, can further reduce the computation without sacrificing the performance. A numerical example is presented to verify and demonstrate the effectiveness of the proposed solution.