火力与指挥控制2025，Vol.50Issue(2)：55-61,69,8.DOI:10.3969/j.issn.1002-0640.2025.02.008
基于改进LSTM循环神经网络的装备故障预测方法
Method of Equipment Faults Prediction Based on Improved LSTM Recurrent Neural Network
摘要
Abstract
Focusing on solving the practical contradiction of high difficulty and low accuracy in equipment faults prediction,which affects the determination of equipment maintenance period and the financing of equipment maintenance resources,on the basis of summary and analysis of the current equipment faults prediction study,according to the characteristics of strong temporality of equipment faults data,the current latest popular LSTM algorithm which has the best effect on time series data processing is introduced.A method for predicting the number of equipment faults based on the LSTM recurrent neural network is proposed,and the network structure is designed in detail,the realization of algorithm of network training and network prediction is given,the advantages of FOA algorithm are combined to optimize the network parameters,and the actual data of faults occurrence of a certain type of equipment is used for experiments.The performance comparison of such predictive models as BP neural network,GNN,wavelet neural network,SENet,etc.proves that the method is effective and provides an effective method for scientifically predicting the number of equipment faul ts and improving the efficiency of equipment maintenance.关键词
长短时记忆神经网络/装备故障预测/神经网络/果蝇算法Key words
lstm/equipment faults prediction/neural network/foa分类
计算机与自动化引用本文复制引用
王龙,宋卫星,张鑫,武婧婧..基于改进LSTM循环神经网络的装备故障预测方法[J].火力与指挥控制,2025,50(2):55-61,69,8.基金项目
陕西省重点研发一般计划基金资助项目(2023-YBGY-175,2023-YBGY-317) （2023-YBGY-175,2023-YBGY-317）
