This paper proposes a data-driven framework for Remaining Useful Life (RUL) prediction of an operating equipment unit in the case of noisy and limited data. It consists of introducing the Brownian Motion model (BM) in the similarity framework and computing the RUL based on a collection of generated HIs (Health Indicators). For that, the percentile filter is used to pre-process the HIs by generating a collection of profiles from the operating equipment unit's HI and a set of references from a given R2F (Run-to-Failure) indicator. Then, the similarity is computed between each profile of this collection and the references in order to pick the most similar reference to each profile for modeling and RUL prediction. The final RUL is calculated as a weighted aggregation of the obtained RULs of the profile collection. A numerical application using simulated data illustrates the accuracy of this approach.

No abstract

This paper proposes a data-driven framework for Remaining Useful Life (RUL) prediction, based on the Brownian Motion model (BM) and the similarity principle, for an operating system given its health indicator. It addresses the issues of noisy and limited run-to-failure (R2F) data. The Percentile filtering is used to extract, from the R2F data, 100 monotonic profiles used as references in the modeling and the RUL prediction. Then, the similarity is computed between these references and the Health Indicator (HI) of the operating system. Fitting the most similar reference into the BM improves the RUL prediction. A numerical application using simulated data justifies the accuracy of this approach.

No abstract