A possibilistic approach for measurement uncertainty propagation in prognostics and health management

In this paper, a similarity-based data-driven prognostic algorithm for the estimation of the Remaining Useful Life of a product is proposed. It is based on the exploitation of run-to-failure data of products, which are supposed to be characterized by similar operational conditions. The core of the contribution is the application of a possibilistic framework, namely a Random-Fuzzy Variable approach, for the representation and propagation of the measurement uncertainty, which is a crucial source of uncertainty in Prognostics and Health Management. The results obtained for a real application case as Medium and High Voltage Circuit Breakers, have shown a high prognostic power of the algorithm, which therefore represents a potential tool for an effective Predictive Maintenance strategy.