Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines

Abstract

Determining the residual life time of systems is a determinant factor for machinery and environment safety. In this paper the problem of estimate the residual useful life (RUL) of turbo-fan engines is addressed. The adopted approach is especially suitable for situations in which a large amount of data is available offline, by allowing the processing of such data for the determination of RUL. The procedure allows to calculate the RUL through the following steps: features extraction by Artificial Neural Networks (ANN) and determination of remaining life time by-prediction models based on a Hidden Markov Model (HMM). Simulations confirm the effectiveness of the proposed approach and the promising power of Bayesian methods.