Abstract Massive training samples are usually difficult to obtain in practice for remaining useful life (RUL) prediction of rolling element bearings (REBs). Building simulated data sets is an alternate solution. Some effective dynamic models have been established for instantaneous vibration behaviors of REBs. However, few researches have been done on the modeling of their long-term degradation processes. Thus, plenty of degradation data are obtained by dynamic modeling in this paper, and a novel performance degradation dictionary (PDD) is constructed for RUL prediction based on similarity. Firstly, the degradation process is divided into steady stage, defect initiation, defect propagation and damage stage. Considering the coupling excitation of time-varying morphology and stiffness, a comprehensive dynamic model is established to simulate the fault propagation. Secondly, a PDD which serves as the reference sets for RUL prediction is constructed by solving the response of the model. Hence, the similarity method can be enhanced to estimate the uncertainty of RUL. Finally, the effectiveness of the proposed method is verified by experimental degradation data under different working conditions and fault types.

中文翻译：

---

基于模拟性能退化字典的滚动轴承剩余使用寿命预测

摘要 滚动轴承（REB）剩余使用寿命（RUL）预测在实践中通常难以获得大量训练样本。构建模拟数据集是另一种解决方案。已经为 REB 的瞬时振动行为建立了一些有效的动力学模型。然而，关于其长期降解过程的建模研究很少。因此，本文通过动态建模获得了大量的退化数据，并构建了一种新的性能退化字典（PDD）用于基于相似性的 RUL 预测。首先，退化过程分为稳定阶段、缺陷引发、缺陷传播和损伤阶段。考虑时变形态与刚度的耦合激励，建立了一个综合的动态模型来模拟故障的传播。其次，通过求解模型的响应，构建作为 RUL 预测参考集的 PDD。因此，可以增强相似度方法来估计 RUL 的不确定性。最后，通过不同工况和故障类型下的实验退化数据验证了所提出方法的有效性。