A Data-Driven Prognostic Approach Based on Sub-Fleet Knowledge Extraction

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 peculiar feature of the proposal is that the products composing the fleet are not strictly required to belong to the same system or plants, since the only constraint is that they are characterized by similar operating conditions (f.i. installed in the same region or operating in the same industrial application). The algorithm, indeed, is able to detect the set of products (sub-fleet of products) showing highest usage and degradation pattern similarity with the one under study and exploits the related monitoring data for a reliable prediction of the RUL, resulting in a potential tool for an effective Predictive Maintenance (PdM) strategy.

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