A new enhanced feature extraction strategy for bearing Remaining Useful Life estimation

Abstract

Accurate Remaining Useful Life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and to decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries that need to be monitored and the user should predict its RUL. The challenge of this study is to propose a new strategy for RUL feature extraction. The proposed methodology provides better features in term of monotonicity. This specification ensures a better RUL prediction by comparing the test degradation features to the library of instance. Experimental results show that the proposed methodology is very promising for RUL estimation by industry.