重庆大学学报2025，Vol.48Issue(2)：35-49,15.DOI:10.11835/j.issn.1000-582X.2025.02.004
融合EEMD与BiLSTM深度网络的结构监测缺失数据重构
Reconstructing missing health monitoring data using a deep network integrating EEMD and BiLSTM
摘要
Abstract
In long-term monitoring processes,the structural health monitoring(SHM)system often encounters data incompleteness due to various factors,including sensor malfunctions,power interruptions,and network transmission issues.To address this challenge,this study proposes a missing data reconstruction method for structural monitoring based on ensemble empirical mode decomposition(EEMD)and bidirectional long short-term memory(BiLSTM)networks,leveraging their advantages in time-series processing.The proposed approach utilizes EEMD to adaptively decompose the monitoring time-series data into a set of intrinsic mode functions(IMFs),each representing different time scales.This decomposition effectively transforms the nonlinear and non-stationary time-series signals into stationary components.The IMFs are then input into a BiLSTM network for missing data reconstruction,enhancing the prediction accuracy of the BiLSTM model.Analysis is conducted on a six-story scaled structural model and a benchmark finite element simulation model.Experimental results demonstrate that,compared to the mainstream methods such as EEMD-LSTM,BiLSTM,and LSTM,the proposed EEMD-BiLSTM approach achieves the highest prediction accuracy.In cases of 5%,10%and 15%missing data,the R2 value remains above 0.8.Therefore,the use of the EEMD method for preprocessing non-stationary structural acceleration response data significantly improves the prediction accuracy of BiLSTM,providing a more adaptive solution to the problem of missing data in structural monitoring.关键词
结构健康监测/数据重构/集合经验模态分解/双向长短期记忆网络Key words
structural health monitoring/data reconstruction/ensemble empirical mode decomposition/bi-directional long short-term memory network分类
计算机与自动化引用本文复制引用
何盈盈,黄正洪,张利凯,赵智航,关腾达..融合EEMD与BiLSTM深度网络的结构监测缺失数据重构[J].重庆大学学报,2025,48(2):35-49,15.基金项目
重庆市教委科学技术研究项目(KJQN202201805,KJQN202301801) （KJQN202201805,KJQN202301801）
重庆市合川区科技计划项目(HCKJ-2024-110) （HCKJ-2024-110）
重庆人文科技学院科学技术研究项目(CRKZK2023007,JSJGC202201,JSJGC202205).Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202201805,KJQN202301801),Science and Technology Program of Hechuan District of Chongqing(HCKJ-2024-110),and Science and Technology Research Program of Chongqing College of Humanities Science&Technology(CRKZK2023007,JSJGC202201,JSJGC202205). （CRKZK2023007,JSJGC202201,JSJGC202205）
