Boukra, TaharLebaroud, Abdessalam2023-09-072023-09-0720192170-161X2588-2082http://hdl.handle.net/123456789/15162Generally the two main strategies taken in data-driven remaining useful life (RUL) prediction of a component/system can be summarized in 1) identifying a health indicator and predicting its trend until a predefined threshold; 2) mapping directly the health indicator (HI) to RUL by regression. Under the first category, traditional extracted features for RUL prediction usually show undesirable behaviors such as fluctuation, non-monotonicity and abrupt increase at the end which hampers the accuracy of the RUL prediction. To enhance the prediction accuracy, this paper brings a new feature selection method, based on preprocessing further the extracted features in a way that the identified prognostic feature results in an obvious trend quality. A set of established and proposed suitability metrics for the prognostic task are used to assess the identified features qualities. The Particle Filtering technique is adopted as a projection tool as well for the prediction of the RUL due to its capability to carry nonlinear systems in presence of non-Gaussian process/observation noise. Datasets from bearings run-to-failure experiments provided by FEMTO-ST Institute - IEEE PHM 2012 challenge- were used to validate our approach. A mean percentage error of 12.18% was achieved indicating that the model worked accurately and reliably on every tested bearing..enPrognostics and Health ManagementParticle FilteringRemaining Useful LifeArticle