A fault/anomaly system prognosis using a data-driven approach considering uncertainty

This paper presents a data-driven prognostic strategy for failure prediction and computing the remaining useful life (RUL) using an autoregressive (AR) model combined with the recursive least squares (RLS) algorithm. The proposed method not only provides an estimation of the remaining useful life (RUL), but also a confidence interval based on modeling the uncertainty as a probabilistic Gaussian variable. To illustrate the performance of the proposed approach, a conveyor belt system that uses an AC electric motor to move a cart from one end to the other is used.