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.

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

Cristaldi, L.;Ferrero, A.;Leone, G.;Salicone, S.
2018-01-01

Abstract

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.
2018
I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings
9781538622223
Data-driven; Measurement Uncertainty; Prognostics; Random Fuzzy Variable; Remaining Useful Life; Similarity; Safety, Risk, Reliability and Quality; Instrumentation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1084074
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