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

Various working conditions and bearing structures make remaining useful life (RUL) prediction more challenging. This paper 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 indexes are too poor to be applied by introducing the new intermediate domain. At first, high-quality feature degradation indexes are selected using the joint evaluation index and the principal component analysis (PCA) algorithm. Then, a maximum correlated kurtosis de-convolution (MCKD) algorithm is carried out to obtain the demarcation point between healthy and degradation stages. After selecting high quality domain, the objective function of intermediate domain SVM is designed, based on 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 existing models, which proves the advantage of multi-optimization transfer learning.