In prognostics and health management (PHM), remaining useful life (RUL) estimation has become a major focus for guaranteeing the safety and reliability of systems. Similarity-based RUL estimation methods, which predict a system’s RUL based on the RULs of other systems with similar degradation behaviors, have been proven effective when no or limited mechanism knowledge is available. Global similarity-based approaches apply the whole-life history to find similar degradation patterns and may lead to few or even no candidates. In contrast, local similarity-based methods only utilize the data close to the prediction time, and then, false positives are inevitable. A given system may have experienced several events before being tested for RUL, and each event may impact its RUL. Systems that have undergone similar events will probably degrade similarly in the future. Hence, the past events must be effectively identified and fully utilized. This paper proposes estimating a system’s RUL by using multiple impacts from its past. The system’s history is transformed into a set of local segments by which the degradation events are represented. Then, a coarse-to-fine strategy is introduced to efficiently locate the events similar to the test. The most similar segments are regarded as references, and their corresponding RULs are a natural data basis for RUL estimation. Since segments may correspond to different features, we adopt two adjustment strategies to make reference RULs more applicable. A self-adaptive weight allocation method is also proposed to further improve the prediction performance. The experimental results show the effectiveness and advantages of our proposed method.

在预测和健康管理（PHM）中，剩余使用寿命（RUL）估计已成为保证系统安全性和可靠性的主要重点。基于相似度的RUL估计方法可以在没有可用机制知识或机制知识有限的情况下，基于具有类似退化行为的其他系统的RUL来预测系统的RUL。基于全局相似性的方法将整个生命历史应用于发现相似的降解模式，并且可能导致很少甚至没有候选者。相反，基于局部相似性的方法仅利用接近预测时间的数据，因此，误报是不可避免的。给定系统在测试RUL之前可能经历了几次事件，并且每个事件都可能影响其RUL。经历过类似事件的系统将来可能会类似地降级。因此，必须有效地识别和充分利用过去的事件。本文建议通过使用过去的多种影响来估计系统的RUL。系统的历史记录被转换为一组局部片段，通过这些局部片段表示退化事件。然后，引入了从粗到精的策略来有效地定位类似于测试的事件。最相似的段被视为参考，并且它们对应的RUL是RUL估计的自然数据基础。由于细分可能对应于不同的功能，因此我们采用两种调整策略以使参考RUL更适用。还提出了一种自适应权重分配方法，以进一步提高预测性能。