This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametrize a statistical, linear degradation model. The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient, system-dependent wear progression within the RUL prediction. A case study is used to show the suitability of the proposed method. The results show that the method can be applied to three distinct types of post-wearing-in behavior: wearing-in with subsequent hydrodynamic, stationary wear, and progressive wear operation. While hydrodynamic operation leads to an infinite lifetime, the method is successfully applied to predict RUL in cases with stationary and progressive wear.

本研究介绍了一种预测在静止、磨损临界条件下运行的滑动轴承的剩余使用寿命 (RUL) 的方法。在此方法中，耦合弹流动力润滑（混合 EHL）和磨损模拟方法的瞬态磨损数据用于参数化统计线性退化模型。该方法结合贝叶斯推理来更新整个运行时间的线性退化模型，从而在 RUL 预测中考虑瞬态、系统相关的磨损进程。通过案例研究来证明所提出方法的适用性。结果表明，该方法可应用于三种不同类型的磨合后行为：随后的流体动力磨合、静态磨损和渐进磨损操作。虽然流体动力操作可带来无限的使用寿命，但该方法已成功应用于预测静态和渐进磨损情况下的 RUL。