We propose an approach for failure prognosis based on the estimation of the Remaining Useful Life (RUL) of a system in a situation in which monitoring signals providing information about its degradation evolution are not measured and no operating model of the system is available. These conditions are of practical interest for industrial applications such as mechanical (e.g. rolling bearing) or electrical (e.g. wind turbine) devices or equipment-critical components (e.g. batteries) in which the addition of sensors to the system is not feasible (e.g. space limitations for sensors, cost, etc.). The approach is based on an estimation of the system degradation using residual generation (where the difference between the system and the observer outputs is processed) and Hidden Markov Models with discrete observations. The prediction of the system RUL is given by the Markov property concerning the mean time before absorption. The approach comprises two phases: a training phase to model the degradation behavior and an “on-line” use phase to estimate the remaining life of the system. Two case studies were conducted for RUL prediction to verify the effectiveness of the proposed approach.

我们提出了一种基于系统剩余使用寿命 (RUL) 估计的故障预测方法，在这种情况下，未测量提供有关其退化演变信息的监控信号，并且没有可用的系统运行模型。这些条件对于工业应用具有实际意义，例如机械（例如滚动轴承）或电气（例如风力涡轮机）设备或设备关键部件（例如电池），其中向系统添加传感器是不可行的（例如空间限制）传感器、成本等）。该方法基于使用残差生成（其中处理系统和观察者输出之间的差异）和具有离散观察的隐马尔可夫模型来估计系统退化。系统 RUL 的预测由关于吸收前平均时间的马尔可夫特性给出。该方法包括两个阶段：模拟退化行为的训练阶段和估计系统剩余寿命的“在线”使用阶段。对 RUL 预测进行了两个案例研究，以验证所提出方法的有效性。