CMPASS Dataset

This paper introduces an aircraft engine remaining life prediction model based on an improved Transformer architecture (SBi-Transformer), addressing the computational inefficiencies and inadequate local temporal dependency capturing capabilities of the standard transformer model in processing long sequence data. The SBi-Transformer employs a dual-layer attention mechanism to reduce computational complexity and enhance local temporal dependencies. It eliminates the decoder component, directly using BiLSTM to model the time series of the encoder outputs, supplementing local temporal context information, and strengthening the forward and backward dependency modeling of degradation trends. Ultimately, the integrated global-local features are mapped to remaining useful life (RUL) prediction values through a fully connected layer. Experiments on NASA’s C-MAPSS dataset demonstrate that SBi-Transformer surpasses existing methods in prediction accuracy and uncertainty estimation. Ablation experiments validate the synergistic effects of the modules, indicating that the sparse attention mechanism and BiLSTM complementarily enhance model performance. The model exhibits strong adaptability and generalizability under complex operating conditions and multiple fault modes, with prediction result confidence intervals dynamically converging to the true degradation trajectory. This study provides an efficient and accurate solution for predicting the remaining life of aircraft engines, holding significant engineering application value      

本文提出一种基于改进Transformer（Transformer）架构的航空发动机剩余寿命预测模型（SBi-Transformer），针对标准Transformer（Transformer）模型在处理长序列数据时存在的计算效率低下、局部时序依赖捕捉能力不足的问题进行优化。该模型采用双层注意力机制以降低计算复杂度，同时强化局部时序依赖建模能力。该模型移除了解码器模块，直接利用双向长短期记忆网络（BiLSTM）对编码器输出的时序序列进行建模，补充局部时序上下文信息，同时强化退化趋势的前后向依赖建模。最终，通过全连接层将融合后的全局-局部特征映射至剩余使用寿命（Remaining Useful Life，RUL）预测值。在NASA的C-MAPSS数据集上开展的实验表明，SBi-Transformer在预测精度与不确定性估计方面均优于现有方法。消融实验验证了各模块的协同增效作用，表明稀疏注意力机制与双向长短期记忆网络（BiLSTM）可通过互补方式提升模型性能。该模型在复杂工况与多故障模式下展现出优异的适应性与泛化能力，其预测结果的置信区间可动态收敛至真实退化轨迹。本研究为航空发动机剩余寿命预测提供了一种高效精准的解决方案，具备重要的工程应用价值。

搜集汇总

背景与挑战

背景概述

CMPASS Dataset是一个专注于飞机发动机剩余寿命预测的数据集，基于NASA的C-MAPSS数据集，包含四个子集，覆盖不同运行条件和故障模式下的传感器数据。该数据集用于验证SBi-Transformer模型在预测准确性和不确定性估计方面的优越性。