Estimation of the aero-engine remaining useful life (RUL) is a significant part of prognostics and health management (PHM) and the basis of condition-based maintenance (CBM) which can improve the reliability and economy. Multiple operating conditions, nonlinear degradation, and early prediction are significant and distinctive issues compared with other prognostics problems. While these issues do not get enough attention and researches in aero-engine RUL estimation. In view of these points, three specific data preparation approaches and a novel loss function are introduced. The data preparation approaches can extract high-quality data for the long short-term memory (LSTM) neural network according to the characteristic of aero-engine degradation data. Among these approaches, operating condition normalization is an effective method to handle the multiple operating conditions problems, and RUL limitation identification is a novel method to identify the turning point of the nonlinear degradation process. The scoring function is an innovative loss function used to replace the mean square error (MSE) loss function which has a preference for early prediction. The comparisons with the original LSTM and some other approaches indicate that the combination of the data preparations and the scoring loss function is an effective solution for the above issues, and can achieve the best performance among the approaches.

航空发动机剩余使用寿命（RUL）的估计是预测和健康管理（PHM）的重要组成部分，也是基于状态的维护（CBM）的基础，可以提高可靠性和经济性。与其他预测问题相比，多重运行条件、非线性退化和早期预测是重要且独特的问题。而这些问题在航空发动机RUL估计中并没有得到足够的重视和研究。鉴于这些观点，介绍了三种特定的数据准备方法和一种新颖的损失函数。数据准备方法可以根据航空发动机退化数据的特点，为长短期记忆（LSTM）神经网络提取高质量的数据。在这些方法中，工况归一化是处理多工况问题的有效方法，RUL极限识别是识别非线性退化过程转折点的新方法。评分函数是一种创新的损失函数，用于替代偏爱早期预测的均方误差 (MSE) 损失函数。与原始 LSTM 和其他一些方法的比较表明，数据准备和评分损失函数的结合是解决上述问题的有效方法，并且可以达到方法中最好的性能。评分函数是一种创新的损失函数，用于替代偏爱早期预测的均方误差 (MSE) 损失函数。与原始 LSTM 和其他一些方法的比较表明，数据准备和评分损失函数的结合是解决上述问题的有效方法，并且可以达到方法中最好的性能。评分函数是一种创新的损失函数，用于替代偏爱早期预测的均方误差 (MSE) 损失函数。与原始 LSTM 和其他一些方法的比较表明，数据准备和评分损失函数的结合是解决上述问题的有效方法，并且可以达到方法中最好的性能。