Prognostic and health management (PHM) effectively reduces the economic loss of sensor-equipped machine downtime caused by under-maintenance and the waste of resources resulted from over-maintenance. The remaining useful life (RUL) prediction is the most critical step in PHM. However, accurate RUL prediction in a multiple sensors data environment faces challenges and difficulties. In this paper, we firstly consider the change point where the sensor-equipped machine drifts from a health state (initial stage) to the degradation stage, and combine the deep learning model with the change point to construct the health indicator (HI). Then, a long short-term memory model combined with an attention mechanism (LSTM_Att) is used to iteratively predict the future HI. Additionally, the predicted RUL distribution of the studied sensor-equipped machine is estimated using the similarity method based on the HI data of historical multiple sensor-equipped machines. Then, the confidence interval of RUL is obtained. Finally, the proposed method is verified on the publicly available turbofan engine degradation data set. The experimental results show that the proposed method outperforms the state-of-art benchmark methods.

预测与健康管理 (PHM) 有效减少了因维护不足而导致的配备传感器的机器停机时间的经济损失和因过度维护而造成的资源浪费。剩余使用寿命 (RUL) 预测是 PHM 中最关键的一步。然而，在多传感器数据环境中准确的 RUL 预测面临挑战和困难。在本文中，我们首先考虑配备传感器的机器从健康状态（初始阶段）漂移到退化阶段的变化点，并将深度学习模型与变化点结合起来构建健康指标（HI）。然后，结合注意力机制（LSTM_Att）的长短期记忆模型用于迭代预测未来的HI。此外，所研究的配备传感器的机器的预测 RUL 分布是使用基于历史多个配备传感器的机器的 HI 数据的相似性方法估计的。然后得到RUL的置信区间。最后，在公开可用的涡扇发动机退化数据集上验证了所提出的方法。实验结果表明，所提出的方法优于最先进的基准方法。