A Framework for Predicting Remaining Useful Life Curve of Rolling Bearings Under Defect Progression Based on Neural Network and Bayesian Method

Abstract

In order to improve Remaining Useful Life (RUL) prediction accuracy for rolling bearings under defect progressing, the robustness for individual differences and the fluctuation of vibration features are challenging issues. In this research, we propose a novel RUL prediction framework based on a Convolutional Neural Network (CNN) and Hierarchical Bayesian Regression (HBR) for considering the degradation conditions and individual differences of RUL to improve the prediction accuracy. The characteristics of the proposed framework are: (1) In order to reduce the effect of the fluctuation of vibration features, the proposed framework uses an intermediate variable indicating the degradation condition instead of predicting RUL from vibration features. (2) The proposed framework considers not only present but also past degradation conditions in CNN. We conducted the experiment on rolling bearings under defect progression and evaluated the RUL prediction accuracy of the proposed framework. The proposed framework can generate a monotonous RUL prediction curve with a probability distribution and improve the RUL prediction accuracy under defect progression.

Keywords

• Convolutional neural network
• feature fusion
• hierarchical Bayesian regression
• remaining useful life
• rolling bearings