The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.
Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.
The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.
The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.
