By informing the onset of the degradation process, health status evaluation serves as a significant preliminary step for reliable remaining useful life (RUL) estimation of complex equipment. This paper proposes a novel temporal dynamics learning-based model for detecting change points of individual devices, even under variable operating conditions, and utilises the learnt change points to improve the RUL estimation accuracy. During offline model development, the multivariate sensor data are decomposed to learn fused temporal correlation features that are generalisable and representative of normal operation dynamics across multiple operating conditions. Monitoring statistics and control limit thresholds for normal behaviour are dynamically constructed from these learnt temporal features for the unsupervised detection of device-level change points. The detected change points then inform the degradation data labelling for training a long short-term memory (LSTM)-based RUL estimation model. During online monitoring, the temporal correlation dynamics of a query device is monitored for breach of the control limit derived in offline training. If a change point is detected, the device's RUL is estimated with the well-trained offline model for early preventive action. Using C-MAPSS turbofan engines as the case study, the proposed method improved the accuracy by 5.6\% and 7.5\% for two scenarios with six operating conditions, when compared to existing LSTM-based RUL estimation models that do not consider heterogeneous change points.

翻译：通过告知退化过程的起始点，健康状态评估为复杂设备的可靠剩余寿命（RUL）估计提供了重要的初步步骤。本文提出了一种新颖的基于时序动态学习的模型，用于检测单个设备的变点，即使在变工况条件下也能实现，并利用学习到的变点来提高RUL估计精度。在离线模型开发过程中，多变量传感器数据被分解以学习融合的时序相关性特征，这些特征具有泛化能力，能代表多个工况下的正常运行动态。从这些学习的时序特征中动态构建监控统计量和控制限阈值，用于无监督检测设备级变点。检测到的变点随后用于指导退化数据标注，以训练基于长短时记忆网络（LSTM）的RUL估计模型。在线监测时，监测查询设备的时序相关性动态，判断是否超出离线训练得到的控制限。若检测到变点，则利用训练好的离线模型估计设备RUL，以实施早期预防措施。以C-MAPSS涡轮风扇发动机为案例研究，与忽视异构变点的现有LSTM-based RUL估计模型相比，所提方法在具有六个工况的两个场景下分别将精度提升了5.6% 和 7.5%。