基于多尺度LSTM预测模型研究

航空发动机剩余寿命（remaining useful life，RUL）预测是设备故障预测与健康管理（prognostics and health management，PHM）的核心问题。针对发动机数据维度高、滞后性强和复杂度高等挑战，提出了一种基于自训练权重的多尺度注意力双向长短期记忆神经网络模型。通过不同尺度的双向长短期记忆神经网络（bidirectional long short-term memory neural network，BiLSTM）提取多尺度特征；提出一种基于自训练权重的融合算法，通过引入注意力机制进行不同尺度的特征筛选，以提高预测精度。将各模型在NASA的C-MAPSS数据集上进行实验对比，结果证明，所提出预测模型在准确率和均方根误差指标上均有所提升。

Research on Prediction of Model Based on Multi-scale LSTM

Aircraft engine remaining useful life (RUL) prediction is the core issue in equipmentfailure prognostics and health management (PHM). Aiming at the characteristics of high dimensionality, high lag and complexity of engine data, a multi-scale attention-based bidirectional long short-term memory neural network model based on self-training weights is proposed. Multi-scale features are extracted through bidirectional long short-term memory neural network (BiLSTM) of different scales. A fusion algorithm based on self-training weights is proposed, and an attention mechanism is introduced to screen features at different scales to improve prediction accuracy. Various models are compared on the NASA's C-MAPSS data set. The results prove that the proposed prediction model improves in both accuracy and root mean square error indicators.