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In situation where the development of
degradation model based on first principles is difficult or
the extracted features from data-driven model do not
exhibit an obvious trend in order to enhance the
prediction of the Remaining Useful Life (RUL), we must,
therefore, be addressed to the identification of new
features having an obvious trending quality. In this
context, this paper brings a new feature selection method,
based on preprocessing further the extracted features in
such a way that the identified prognostic feature results in
an obvious trending quality. This method was validated on
a set of experimental data collected from bearings run-tofailure