A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation

Remaining useful life estimation (RUL) is an essential part in prognostics and health management. This paper addresses the problem of estimating the RUL from the observed degradation data. A Wiener-process-based degradation model with a recursive filter algorithm is developed to achieve the aim. A novel contribution made in this paper is the use of both a recursive filter to update the drift coefficient in the Wiener process and the expectation maximization (EM) algorithm to update all other parameters. Both updating are done at the time that a new piece of degradation data becomes available. This makes the model depend on the observed degradation data history, which the conventional Wiener-process-based models did not consider. Another contribution is to take into account the distribution in the drift coefficient when updating, rather than using a point estimate as an approximation. An exact RUL distribution considering the distribution of the drift coefficient is obtained based on the concept of the first hitting time. A practical case study for gyros in an inertial navigation system is provided to substantiate the superiority of the proposed model compared with competing models reported in the literature. The results show that our developed model can provide better RUL estimation accuracy.

► Both a recursive filter and EM algorithm are used to update the model parameters.
► The model depends on the observed degradation data history by updating above.
► An exact RUL distribution considering the distribution of the drift coefficient is obtained.
► A practical case study for gyros in an inertial navigation system is provided.
► The results show that our developed model can provide superior RUL estimation accuracy.