Bearing life prognosis based on monotonic feature selection and similarity modeling

Abstract

In data-driven prognosis approach, indicator information plays an important role for reliable prediction. Although lots of researches have been carried out on prognosis algorithms, only few have paid attention on developing an effective method to select good' degradation indicators. This paper presents a novel strategy to address the problem, which mainly proposes methods of monotonic feature selection using rank mutual information, and similarity-based modeling for remaining life estimation. The proposed system is demonstrated based on open source data of bearing life cycle. The experiment results show that satisfactory prognostic performance can be obtained with advantages of simplicity, accuracy and generality.