北京航空航天大学学报2026，Vol.52Issue(4)：1279-1289,11.DOI:10.13700/j.bh.1001-5965.2024.0051
基于VMD-CNN-BiLSTM的变工况涡扇发动机剩余使用寿命预测
Remaining useful life prediction of variable-operating turbofan engine based on VMD-CNN-BiLSTM
摘要
Abstract
In order to address the issue of low prediction accuracy in traditional forecasting methods for residual life of turbofan engines under variable working conditions,a variational mode decomposition convolutional neural network bidirectional long short term memory(VMD-CNN-BiLSTM)model is proposed.Firstly,variational mode decomposition(VMD)is used to normalize the data and split it into sub-data at predetermined intervals.This allows for the thorough extraction of hidden temporal features in multidimensional data as well as the removal of singular samples and dimensional variations.Secondly,a VMD-CNN-BiLSTM model is constructed for predicting the residual life of turbofan engines under variable working conditions.The convolutional neural network(CNN)is employed for feature extraction and fusion to generate multiple mappings.These mappings are then input into the BiLSTM network to capture time dependencies in the time series data and produce accurate predictions of remaining engine life.Finally,hyperparameter optimization using the Sparrow algorithm enhances the prediction performance of the model.As shown by root mean squared error(RMSE)values of 13.74±0.51 and mean absolute error(MAE)values of 11.24±0.49 when predicting remaining engine life under variable operating conditions,experimental results on the commercial modular aero-propulsion system simulation(C-MAPSS)dataset show that VMD-CNN-BiLSTM achieves high accuracy and generalization performance even with noisy data.关键词
航空发动机/剩余使用寿命预测/变分模态分解/麻雀优化/深度学习Key words
aeroengine/remaining useful life prediction/variational mode decomposition/sparrow optimization/deep learning分类
信息技术与安全科学引用本文复制引用
张鲁一航,杨彦明,陈永展,李军亮,戴豪民..基于VMD-CNN-BiLSTM的变工况涡扇发动机剩余使用寿命预测[J].北京航空航天大学学报,2026,52(4):1279-1289,11.基金项目
国家社科基金(SKJJ-2022-B-037) （SKJJ-2022-B-037）
山东省自然科学基金(ZR2020ME131) National Fund for Social Science(SKJJ-2022-B-037) （ZR2020ME131）
Natural Science Foundation of Shandong Province(ZR2020ME131) （ZR2020ME131）
