Abstract：To solve the problem of predicting equipment residual useful life (RUL) which is non-linear and non-Gaussian, a particle filtering framework for system’s RUL prediction is proposed. This framework uses a non-linear state-space model of the system (with unknown time-varying parameters) and a particle filtering (PF) algorithm to estimate the probability density function (PDF) of the state. The state PDF estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the PDF of the remaining useful life (RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of the predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a full life test for a gearbox are used to validate the proposed methodology, and comparisons are made between PHM and PF method, the outcome shows that the PF method has a better effect in the aera of PHM for RUL prediction.
2013, Vol. 32 
