A New Intermediate-Domain SVM-Based Transfer Model for Rolling Bearing RUL Prediction

Abstract

Various working conditions and bearing structures make remaining useful life (RUL) prediction more challenging. This article presents a new intermediate-domain support vector machine (SVM) based transfer model for rolling bearing RUL prediction. This transfer model aims to solve the problem when the source degradation indices are too poor to be applied by introducing the new intermediate domain. At first, the high-quality feature degradation indices are selected using the joint evaluation index and the principal component analysis algorithm. Then, a maximum correlated kurtosis deconvolution algorithm is carried out to obtain the demarcation point between the healthy and degradation stages. After selecting the high-quality domain, the objective function of intermediate-domain SVM is designed based on the classical-domain-independent SVM to optimize “source-to-intermediate” and “intermediate-to-target” transfer processes simultaneously. Finally, experiments using both ball and conical bearing datasets indicate that the proposed method has higher RUL prediction performance than the existing models, which proves the advantage of multioptimization transfer learning.