Abstract
The Prognostic and Health Management (PHM) becomes a research topic in its own right and tends to be more and more visible within the scientific community such as in Nasa Society, which has provided datasets for experiments. The purpose of this paper is to evaluate the performance of a data-driven prognostic technique used for predicting Remaining Useful Life (RUL). The methodological support of the proposed approach integrates all data-driven prognostic sequential steps merged in offline and online part. To design the predictive degradation model on the offline part, the Relevance Vector Machine (RVM) algorithm was applied. On the online part, prediction of the RUL is based on the Similarity-Based Interpolation (SBI) algorithm. The different steps of the methodology are described and their implementation undertaken through a case study involving the degradation dataset of turbofan engines from the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). Finally, results are compared with other techniques applied on the same dataset.
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