Prediction of the remaining useful life: An integrated framework for model estimation and failure prognostics

Machine failure prognostic is concerned with the generation of long term predictions and the estimation of the probability density function of the remaining useful life. Nowadays, a commonly used approach for this task is to make the prediction using a dynamical state-space model of the fault evolution. However, the main limitation of this approach is that it requires the values of the model parameters to be known. This work aims to alleviate the need for extensive prior efforts related to finding the exact model. For this we propose a framework for data-driven prediction of RUL with on-line model estimation. This is achieved by combining the state estimation algorithm with Maximum-Likelihood parameter estimation in the form of the Expectation-Maximization algorithm. We show that the proposed algorithm can be used with different classes of both black-box and grey-box models. First, a detailed solution for linear black-box models with the Kalman filter is presented followed by the extension to nonlinear models using either the Unscented Kalman filter or the particle filter. The performance of the algorithms is demonstrated using the experimental data from a single stage gearbox.