无线电工程2025，Vol.55Issue(5)：913-919,7.DOI:10.3969/j.issn.1003-3106.2025.05.002
基于改进BiLSTM算法的车辆涡轮机寿命预测
Research on Life Prediction of Vehicle Turbine Based on Improved BiLSTM Algorithm
摘要
Abstract
Turbines,as core equipment in the fields of energy,aviation,and shipping,may experience performance degradation and lifespan issues,which can lead to reduced production efficiency and safety hazards.Therefore,accurately predicting the State of Health(SOH)and Remaining Useful Life(RUL)of turbines is crucial for achieving predictive maintenance.To address the fault prediction problem of critical mechanical equipment—turbines,a prediction method based on an improved Bidirectional Long Short-Term Memory(BiLSTM)network is proposed.The proposed hybrid model combines the local feature extraction capability of Convolutional Neural Network(CNN),the weight allocation mechanism of Attention Mechanism(AM),and the bidirectional time-series processing ability of BiLSTM to enhance the accuracy and efficiency of fault prediction.By analyzing the operational data of 100 turbines,the experimental results demonstrate that the improved BiLSTM model outperforms other mainstream models such as CNN-BiLSTM,CNN-LSTM,and Recurrent Neural Network(RNN)in terms of prediction accuracy,exhibiting lower Mean Absolute Error(MAE)while maintaining high efficiency and accuracy.关键词
涡轮机/健康状态/使用寿命/改进双向长短期记忆网络/卷积神经网络/注意力机制/故障预测Key words
turbine/SOH/useful life/improved BiLSTM/CNN/AM/fault prediction分类
电子信息工程引用本文复制引用
袁灏诚,吴钦木,李佳恒..基于改进BiLSTM算法的车辆涡轮机寿命预测[J].无线电工程,2025,55(5):913-919,7.基金项目
国家自然科学基金(51867006,52267003)National Natural Science Foundation of China(51867006,52267003) （51867006,52267003）
