Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network

Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved fruitful results. However, the existing methods heavily rely on the quality and quantity of the available data. For some critical bearings in industrial scenarios, the real run-To-failure data are insufficient, which impair the applicability of data-based methods for industrial practices. To address these issues, this article proposes a novel dynamic model-Assisted RUL prediction approach for rolling bearing, in which sufficient simulation data are applied as the training data to solve the problem caused by insufficient real data. More specifically, a dynamic rolling bearing model is introduced for simulating the degradation process of physical structures. Then, a multilayer cross-domain transformer network is developed to implement RUL prediction and adapt the learned prediction knowledge from simulation to the actual measurements. Furthermore, a mutual information loss is utilized to preserve the generalized prediction knowledge of the measured data. The proposed approach can achieve a high RUL prediction accuracy with only limited measured data, which tackles the drawbacks of the existing data-driven methods. The experimental results of the rolling bearing degradation datasets demonstrate the effectiveness and superiority of the proposed RUL prediction approach.