Hunan University of Technology, Zhuzhou, 412007, China.
Central South University, Changsha, 410083, China.
Sci Rep. 2022 Apr 20;12(1):6491. doi: 10.1038/s41598-022-10191-2.
In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction, inability to effectively describe degradation trends, and poor remaining useful life (RUL) prognosis effects, a remaining useful life prognosis model combining an improved stack sparse autoencoder (imSSAE) and an improved echo state network (imESN) is proposed in this paper. First, the 3-sigma criterion is adopted to remove the noise and reconstruct the data, and then the deep features of the engine are extracted by using an imSSAE and fused into health indicator (HI) curves describing the engine degradation trend. Finally, an attention mechanism is introduced into an imESN to adaptively process different types of data and obtain the RUL. The experimental results based on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset show that compared with the other popular RUL prediction models, the combined model proposed in this paper has higher prediction accuracy, and the evaluation indices also show the effectiveness and superiority of the model.
在涡扇发动机数据集，为了解决噪声干扰、数据类型多样、数据量大、特征提取复杂、无法有效描述退化趋势以及剩余使用寿命（RUL）预测效果差等问题，提出了一种结合改进堆叠稀疏自编码器（imSSAE）和改进回声状态网络（imESN）的剩余使用寿命预测模型。首先，采用 3σ 准则去除噪声并对数据进行重构，然后使用 imSSAE 提取发动机的深度特征，并融合到描述发动机退化趋势的健康指标（HI）曲线中。最后，在 imESN 中引入注意力机制，自适应处理不同类型的数据，并获取 RUL。基于商用模块化航空推进系统仿真（C-MAPSS）数据集的实验结果表明，与其他流行的 RUL 预测模型相比，本文提出的组合模型具有更高的预测精度，评估指标也表明了该模型的有效性和优越性。
