Accurate remaining useful life (RUL) estimation is significant in reducing maintenance costs and avoiding catastrophic failures of mechanical systems like an aeroengine. To effectively estimate the RUL of mechanical systems, the long short-term memory (LSTM)-based multi-layer self-attention (MLSA) (LSTM-MLSA) method is proposed by designing MLSA mechanism and LSTM, to improve the modeling precision and computing efficiency. In the MLSA mechanism, the multi-layer is respected to extract the effective features of system degradation data in different subspace, and self-attention is employed to establish the accurate correlation of time steps in raw time series data by parallel computation. The LSTM is used to process the extracted features and capture the degradation process of the mechanical system. The RUL estimation of an aeroengines with life degradation data is implemented, to validate the proposed LSTM-MLSA method by comparing with other RUL estimation methods. The results illustrate that the LSTM-MLSA method has high computational efficiency theoretically, high accuracy, and strong robustness in the RUL estimation of an aeroengines. The efforts of this paper provide a highly-efficient method for the RUL estimation of complex mechanical systems, which is promising to enhance the operation and maintenance of the mechanical system by reducing costs and improving RUL estimation precision.

准确的剩余使用寿命（RUL）估算对于降低维护成本和避免机械系统（如航空发动机）的灾难性故障非常重要。为了有效地估计机械系统的RUL，通过设计MLSA机制和LSTM，提出了基于长短期记忆（LSTM）的多层自我注意（MLSA）（LSTM-MLSA）方法，以提高建模精度和计算效率。在MLSA机制中，尊重多层在不同子空间中提取系统降级数据的有效特征，并通过并行计算利用自我关注来建立原始时间序列数据中时间步长的精确关联。LSTM用于处理提取的特征并捕获机械系统的退化过程。实施了具有寿命退化数据的航空发动机的RUL估计，以通过与其他RUL估计方法进行比较来验证所提出的LSTM-MLSA方法。结果表明，LSTM-MLSA方法在航空发动机的RUL估计中具有理论上的高计算效率，高精度和强鲁棒性。本文的工作为复杂机械系统的RUL估计提供了一种高效的方法，有望通过降低成本和提高RUL估计精度来增强机械系统的运行和维护。在航空发动机的RUL估算中具有强大的鲁棒性。本文的工作为复杂机械系统的RUL估计提供了一种高效的方法，有望通过降低成本和提高RUL估计精度来增强机械系统的运行和维护。在航空发动机的RUL估算中具有强大的鲁棒性。本文的工作为复杂机械系统的RUL估计提供了一种高效的方法，有望通过降低成本和提高RUL估计精度来增强机械系统的运行和维护。