Remaining useful life (RUL) is a critical metric in prognostics and health management (PHM) because it reflects the future health status and fault progression of products. Most RUL estimation methods are based on degradation data. In practice, due to changing degradation mechanisms during products' whole life cycle, the degradation data may consist of two or more distinct phases, and the time points of these mechanisms switching are usually nondeterministic. This property makes RUL estimation a difficult task. To solve this problem, this paper proposes a switchable state-space degradation model to characterize degradation paths with nondeterministic switching manner dynamically. To update the model parameters by newly available data, a novel statistical procedure based on Rao-Blackwellized filter/smoother and an expectation maximization algorithm is derived. To improve the robustness and efficiency of the RUL prediction, a semianalytic prediction model is developed, which can avoid significant fluctuation in RUL estimation. The developed methodologies can automatically track different degradation phases and adaptively update parameters related to prior distributions. Two real products degradation cases are used to verify our methodologies.

中文翻译：

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使用递归滤波器/平滑器切换状态空间退化模型以预测剩余使用寿命


剩余使用寿命 (RUL) 是预测和健康管理 (PHM) 中的一个关键指标，因为它反映了产品未来的健康状态和故障进展。大多数 RUL 估计方法都是基于退化数据。在实践中，由于产品整个生命周期中退化机制的变化，退化数据可能由两个或多个不同的阶段组成，并且这些机制切换的时间点通常是不确定的。这一特性使得 RUL 估计成为一项艰巨的任务。为了解决这个问题，本文提出了一种可切换的状态空间退化模型，以动态地表征非确定性切换方式的退化路径。为了通过新的可用数据更新模型参数，导出了一种基于 Rao-Blackwellized 滤波器/平滑器和期望最大化算法的新颖统计过程。为了提高RUL预测的鲁棒性和效率，开发了半解析预测模型，可以避免RUL估计的显着波动。开发的方法可以自动跟踪不同的退化阶段并自适应更新与先验分布相关的参数。使用两个真实的产品降解案例来验证我们的方法。