中南大学学报（自然科学版）2026，Vol.57Issue(7)：3017-3030,14.DOI:10.11817/j.issn.1672-7207.2026.07.012
基于LSTM增量网络的载荷识别方法研究
Research on load identification based on LSTM incremental network
摘要
Abstract
Load identification problem includes load location and load time history reconstruction.In view of the ill-posedness of matrix inversion in load identification,the limitation of solution accuracy of model-driven methods by modeling accuracy,and the requirements of load location,a load location and time history reconstruction method driven jointly by Long Short-Term Memory(LSTM)neural networks and incremental learning was proposed.Firstly,the singular value decomposition of the acceleration signal was carried to dimensionality reduction to extract the core features and reduced noise and redundant information.Secondly,combined with the LSTM network,an implicit function model between the structural loading location,the magnitude of the load and the vibration response was gradually constructed,and an incremental learning mechanism was introduced to improve the generalization ability of the network model.Then,the network hyperparameters were automatically adjusted in combination with the Bayesian optimization method.Finally,numerical analysis was conducted on the rectangular cantilever plate model,and experimental verification was carried out using the aluminum alloy cantilever plate and the pantograph frame.The results show that the proposed method can accurately identify the load location and reconstruct the load time-history curve.The load location accuracy reaches 95.8%,and the peak errors are within 1.5%.This method has certain adaptability to the time-history reconstruction and load location problems of relatively complex structures subjected to impact loads.Only a small number of sensors need to be arranged in combination with the structural characteristics to effectively identify the impact loads.关键词
载荷识别/不适定性/增量学习/LSTM神经网络Key words
load identification/ill-posedness/incremental learning/long short-term memory(LSTM)neural network分类
机械制造引用本文复制引用
彭珍瑞,卢志鹏..基于LSTM增量网络的载荷识别方法研究[J].中南大学学报（自然科学版）,2026,57(7):3017-3030,14.基金项目
国家自然科学基金资助项目(62161018)(Project(62161018)supported by the National Natural Science Foundation of China) （62161018）
