A data-driven prognostic approach based on statistical similarity: An application to industrial circuit breakers

•A data-driven prognostic algorithm for the RUL estimation of a product is proposed.•It is based on the exploitation of run-to-failure data of fleet of products.•Sub-fleets of products with similar degradation profile are identified.•The sub-fleet identification is based on degradation rate statistical similarity.•Better prognostic performances than distance-based approaches have been achieved. In this paper, a data-driven prognostic algorithm for the estimation of the Remaining Useful Life (RUL) of a product is proposed. It is based on the acquisition and exploitation of run-to-failure data of homogeneous products, in the followings referred as fleet of products. The algorithm is able to detect the set of products (sub-fleet of products) showing highest degradation pattern similarity with the one under study and exploits the related monitoring data for a reliable prediction of the RUL. In particular, a novel methodology for the sub-fleet identification is presented and compared with other solution found in literature. The results obtained for a real application case as Medium and High Voltage Circuit Breaker, have shown a high prognostic power for the algorithm, which therefore represents a potential tool for an effective Predictive Maintenance (PdM) strategy.