A data-driven prognostic approach based on statistical similarity: An application to industrial circuit breakers. (October 2017)

• Record Type: Journal Article
• Authors: Leone, Giacomo; Cristaldi, Loredana; Turrin, Simone
• Is Part Of: Measurement. Volume 108(2017)
• Page Start: 163
• Page End: 170
• Publication Date: 2017-10
• Subjects: Data-driven -- Industrial circuit breakers -- Prognostics -- Remaining useful life -- Sub-fleet -- Statistical test; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.02.017
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: 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. Abstract: 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.