Remaining useful life (RUL) prediction under time-varying operating conditions is critical to the prognostics and health management of rotating machinery. In the literature, both the degradation rate and variation of a machinery component are often assumed to be solely dependent on operating conditions. However, this strong assumption is usually violated in many industrial applications. In this article, a systematic method for RUL prediction for a rotating machinery component is developed by considering the joint dependency of degradation rate and variation on time-varying operating conditions. In particular, a system state function and an observation function are utilized to characterize the component's degradation process. A quantitative relationship between the drift and diffusion parameters is established to reflect their joint dependency on the operating conditions. A two-stage hybrid approach that jointly implements maximum likelihood estimation and least squares estimation methods is proposed to facilitate parameter estimation in model development based on offline degradation data, and a Bayesian algorithm based on online condition monitoring data is utilized for RUL prediction in online implementation. A simulation study and a real application to rolling element bearings are provided to illustrate the effectiveness of the proposed method in practice.

中文翻译：

---

考虑退化率和时变工况变化的联合相关性的剩余使用寿命预测

在时变操作条件下的剩余使用寿命 (RUL) 预测对于旋转机械的预测和健康管理至关重要。在文献中，机械部件的退化率和变化通常被假定为完全取决于操作条件。然而，在许多工业应用中通常违反了这一强假设。在本文中，通过考虑退化率和随时间变化的操作条件的变化的联合依赖性，开发了一种用于旋转机械部件的 RUL 预测的系统方法。特别地，系统状态函数和观察函数被用来表征组件的退化过程。建立漂移和扩散参数之间的定量关系以反映它们对操作条件的联合依赖性。提出了一种联合实现最大似然估计和最小二乘估计方法的两阶段混合方法，以方便基于离线退化数据的模型开发中的参数估计，并在在线实现中利用基于在线状态监测数据的贝叶斯算法进行RUL预测. 提供了对滚动轴承的仿真研究和实际应用，以说明所提出方法在实践中的有效性。提出了一种联合实现最大似然估计和最小二乘估计方法的两阶段混合方法，以方便基于离线退化数据的模型开发中的参数估计，并在在线实现中利用基于在线状态监测数据的贝叶斯算法进行RUL预测. 提供了对滚动轴承的仿真研究和实际应用，以说明所提出方法在实践中的有效性。提出了一种联合实现最大似然估计和最小二乘估计方法的两阶段混合方法，以方便基于离线退化数据的模型开发中的参数估计，并在在线实现中利用基于在线状态监测数据的贝叶斯算法进行RUL预测. 提供了对滚动轴承的仿真研究和实际应用，以说明所提出方法在实践中的有效性。