| Citation: | ZHANG L Y H，YANG Y M，CHEN Y Z，et al. Remaining useful life prediction of variable-operating turbofan engine based on VMD-CNN-BiLSTM[J]. Journal of Beijing University of Aeronautics and Astronautics，2026，52（4）：1279-1289 （in Chinese）
|
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.
| [1] |
郭晓静, 徐晓慧, 郭佳豪. 基于改进GRU的航空发动机寿命预测自注意力优化算法[J]. 航空动力学报, 2024, 39(12): 447-457.
GUO X J, XU X H, GUO J H. Improved GRU-based self-attention optimization algorithm for aero-engine remaining useful life prediction[J]. Journal of Aerospace Power, 2024, 39(12): 447-457(in Chinese).
|
| [2] |
郭晓静, 贠玉晶, 徐晓慧. 基于深度学习方法的航空发动机寿命预测模型[J]. 振动. 测试与诊断, 2024, 44(2): 330-336.
GUO X J, YUN Y J, XU X H. Prediction model of aero-engine remaining useful life based on deep learning method[J]. Journal of Vibration, Measurement & Diagnosis, 2024, 44(2): 330-336(in Chinese).
|
| [3] |
车鲁阳, 高军伟, 付惠琛. 基于双通道模型的航空发动机剩余寿命预测[J]. 空军工程大学学报, 2023, 24(6): 42-49.
CHE L Y, GAO J W, FU H C. Remaining life prediction of aero-engine based on dual-channel model[J]. Journal of Air Force Engineering University, 2023, 24(6): 42-49(in Chinese).
|
| [4] |
宋慧, 陶冠叶, 曲大义, 等. 基于LSTM网络参数优化的航空发动机寿命预测[J]. 青岛理工大学学报, 2023, 44(5): 112-117.
SONG H, TAO G Y, QU D Y, et al. Aeroengine life prediction based on LSTM network parameter optimization[J]. Journal of Qingdao University of Technology, 2023, 44(5): 112-117(in Chinese).
|
| [5] |
LI Y G, NILKITSARANONT P. Gas turbine performance prognostic for condition-based maintenance[J]. Applied Energy, 2009, 86(10): 2152-2161.
|
| [6] |
SON K L, FOULADIRAD M, BARROS A, et al. Remaining useful life estimation based on stochastic deterioration models: a comparative study[J]. Reliability Engineering & System Safety, 2013, 112: 165-175.
|
| [7] |
CHIACHÍO J, JALÓN M L, CHIACHÍO M, et al. A Markov chains prognostics framework for complex degradation processes[J]. Reliability Engineering & System Safety, 2020, 195: 106621.
|
| [8] |
LI X, DING Q, SUN J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety, 2018, 172: 1-11.
|
| [9] |
ZHANG J S, JIANG Y C, WU S M, et al. Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism[J]. Reliability Engineering & System Safety, 2022, 221: 108297.
|
| [10] |
赵昱宇, 索超, 王雨潇. 基于BSVAR的航空发动机剩余使用寿命预测方法[J]. 北京航空航天大学学报, 2025, 51(11): 3790-3798.
ZHAO Y Y, SUO C, WANG Y X. BSVAR-based remaining useful life prediction method for aircraft engines[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(11): 3790-3798(in Chinese).
|
| [11] |
林荣祥, 王海瑞, 朱贵富. 一种融合神经网络的航空发动机寿命预测方法研究[J]. 机械科学与技术, 2025, 44(11): 2034-2046.
LIN R X, WANG H R, ZHU G F. An aero-engine life prediction method incorporating neural networks[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(11): 2034-2046(in Chinese).
|
| [12] |
魏震波, 余雷. 基于FFT, DC-HC及LSTM的短期负荷预测方法[J]. 智慧电力, 2022, 50(3): 37-43.
WEI Z B, YU L. Short-term load forecasting method based on FFT, DC-HC and LSTM[J]. Smart Power, 2022, 50(3): 37-43(in Chinese).
|
| [13] |
杨德州, 刘嘉明, 宋汶秦, 等. 基于改进型自适应白噪声完备集成经验模态分解的工业用户负荷预测方法[J]. 电力系统保护与控制, 2022, 50(4): 36-43.
YANG D Z, LIU J M, SONG W Q, et al. A load forecasting method for industrial customers based on the ICEEMDAN algorithm[J]. Power System Protection and Control, 2022, 50(4): 36-43(in Chinese).
|
| [14] |
李练兵, 代亮亮, 李静鹏, 等. 基于改进VMD-CNN的电缆短路电流预测研究[J]. 中国测试, 2025, 51(8): 137-146.
LI L B, DAI L L, LI J P, et al. Research on cable short-circuit current prediction based on improved VMD-CNN[J]. China Measurement & Test, 2025, 51(8): 137-146(in Chinese).
|
| [15] |
龚循强, 邱万锦, 吕开云, 等. 变分模态分解与自适应图卷积门控循环网络的交通流量组合预测模型[J]. 武汉大学学报(信息科学版), 2024, 49(12): 2329-2341.
GONG X Q, QIU W J, LYU K Y, et al. A combined traffic flow prediction model based on variational mode decomposition and adaptive graph convolutional gated recurrent network[J]. Geomatics and Information Science of Wuhan University, 2024, 49(12): 2329-2341(in Chinese).
|
| [16] |
秦志沁, 韩玉环, 张毅, 等. 基于VMD分解和随机矩阵理论的异常用电状态检测[J]. 太原理工大学学报, 2024, 55(1): 66-72.
QIN Z Q, HAN Y H, ZHANG Y, et al. Detection of abnormal power consumption state based on VMD decomposition and random matrix theory[J]. Journal of Taiyuan University of Technology, 2024, 55(1): 66-72(in Chinese).
|
| [17] |
邵必林, 纪丹阳. 基于VMD-SE的电力负荷分量的多特征短期预测[J]. 中国电力, 2024, 57(4): 162-170.
SHAO B L, JI D Y. Multi-feature short-term prediction of power load components based on VMD-SE[J]. Electric Power, 2024, 57(4): 162-170(in Chinese).
|
| [18] |
王勇, 李邑灵, 苗夺谦, 等. 基于Transformer-CNN混合架构的跨模态融合抓取检测[J]. 控制与决策, 2024, 39(11): 3607-3616.
WANG Y, LI Y L, MIAO D Q, et al. Cross-modal interaction fusion grasping detection based on Transformer-CNN hybrid architecture[J]. Control and Decision, 2024, 39(11): 3607-3616(in Chinese).
|
| [19] |
孙兴伟, 杨铜铜, 杨赫然, 等. 基于CNN-GRU组合神经网络的数控机床进给系统热误差研究[J]. 仪器仪表学报, 2023, 44(10): 219-226.
SUN X W, YANG T T, YANG H R, et al. Research on thermal error of CNC machine tool feed system based on CNN-GRU combined neural network[J]. Chinese Journal of Scientific Instrument, 2023, 44(10): 219-226(in Chinese).
|
| [20] |
杨沛东, 黄华, 尉卫卫, 等. 基于注意力机制改进的PSO-BiLSTM刀具磨损预测[J]. 北京航空航天大学学报, 2025, 51(10): 3589-3598.
YANG P D, HUANG H, WEI W W. Tool wear prediction based on attention mechanism and PSO-BiLSTM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(10): 3589-3598(in Chinese).
|
| [21] |
方斯顿, 刘龙真, 孔赖强, 等. 基于双向长短期记忆网络含间接健康指标的锂电池SOH估计[J]. 电力系统自动化, 2024, 48(4): 160-168.
FANG S D, LIU L Z, KONG L Q. State-of-health estimation for lithium-ion batteries incorporating indirect health indicators based on bi-directional long short-term memory networks[J]. Automation of Electric Power Systems, 2024, 48(4): 160-168(in Chinese).
|
| [22] |
陶唐飞, 周文洁, 况佳臣, 等. 融合多小波分解的深度卷积神经网络轴承故障诊断方法[J]. 西安交通大学学报, 2024, 58(5): 31-41.
TAO T F, ZHOU W J, KUANG J C. Bearing fault diagnosis method of deep convolutional neural network based on multiwavelet decomposition[J]. Journal of Xi’an Jiaotong University, 2024, 58(5): 31-41(in Chinese).
|
| [23] |
单伽锃, 张茜, 吕西林, 等. 多元特征驱动的超高层建筑变形状态智能学习与预测[J]. 建筑结构学报, 2024, 45(3): 101-112.
SHAN J Z, ZHANG X, LYU X L, et al. Multi-feature driven intelligent learning and prediction of deformation state of super-tall buildings[J]. Journal of Building Structures, 2024, 45(3): 101-112(in Chinese).
|
| [24] |
丁丁, 刘文哲, 盛常冲, 等. 神经网络架构搜索研究进展与展望[J]. 国防科技大学学报, 2023, 45(6): 100-131.
DING D, LIU W Z, SHENG C C, et al. State of the art and prospects of neural architecture search[J]. Journal of National University of Defense Technology, 2023, 45(6): 100-131(in Chinese).
|
Figures(12) / Tables(5)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
