Abstract
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.
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