测控技术2025，Vol.44Issue(3)：1-8,8.DOI:10.19708/j.ckjs.2025.03.304
基于CPO-CNN-LSTM的起落架系统故障诊断方法研究
Research on Fault Diagnosis Method of Landing Gear System Based on CPO-CNN-LSTM
摘要
Abstract
Landing gear braking system is an important part of aircraft.Timely and accurate diagnosis of fault in the landing gear braking system can avoid accidents caused by fault and improve the safety of aircraft.Aiming at the problems of low recognition accuracy and lack of system parameter optimization of existing diagnosis al-gorithms for landing gear braking system,a fault diagnosis method for aircraft landing gear braking system is proposed,which uses crested porcupine optimizer(CPO)algorithm to optimize convolutional neural network-long short term memory(CNN-LSTM).Using the fast optimization ability of CPO,the optimal parameters are substituted into CNN-LSTM to reconstruct the model,and the landing gear flight parameters are trained and classified and the results are output.In the diagnostic experiment,the real flight parameter data of the landing gear braking system of a certain type of aircraft is taken as input to classify the fault modes of the landing gear braking system.The experimental results show that the proposed fault diagnosis method has good fault diagnosis performance and practical application value.关键词
起落架刹车系统/故障诊断/冠豪猪优化器算法/卷积神经网络/长短时记忆神经网络Key words
landing gear braking system/fault diagnosis/CPO algorithm/CNN/LSTM引用本文复制引用
唐凌云,苏艳,易子超..基于CPO-CNN-LSTM的起落架系统故障诊断方法研究[J].测控技术,2025,44(3):1-8,8.基金项目
国家重点实验室项目(SGNR0000KJJS2007673) （SGNR0000KJJS2007673）
