在长期监测过程中，由于传感器设备故障、供能中断、网络传输问题等诸多因素存在，导致结构健康监测系统采集的数据存在不完整性。针对这一问题，结合集合经验模态分解（EEMD）与双向长短期记忆网络（BiLSTM）在时序处理方面的优势，本研究提出一种基于EEMD-BiLSTM的结构监测缺失数据重构方法。该方法利用EEMD自适应地分解监测时序数据为一组代表不同时间尺度的本征模态分量（IMF），从而使非线性、非平稳序的时序信号平稳化。然后将IMF分量输入到BiLSTM网络中进行缺失数据重构，从而提高BiLSTM预测精度。针对六层框架结构缩尺模型和Benchmark有限元仿真模型进行分析，试验结果表明，相比EEMD-LSTM、BiLSTM、LSTM主流方法，本文提出的EEMD-BiLSTM具有最高预测精度，在5%、10%、15%缺失数据情况下，其R2指标保持在0.8以上。因此，采用EEMD方法对非稳态结构加速度响应数据进行预处理，可有效提高BiLSTM的预测精度，对于结构监测缺失数据问题，提供了更具适应性的方法。

During the long-term monitoring process, the data collected by the structural health monitoring system are in-complete due to the presence of many factors such as sensor equipment failure, energy supply interruption, and network transmission problems. To address this problem, combining the advantages of ensemble empirical modal decomposition (EEMD) and bi-directional long and short-term memory network (BiLSTM) in timing processing, this study proposes an EEMD-BiLSTM-based reconstruction method of missing data for structural monitoring. The method utilizes EEMD to adaptively decompose the monitoring time-series data into a set of intrinsic modal components (IMFs) representing different time scales, thereby smoothing the nonlinear, nonsmoothly ordered time-series signals. The IMF components are then input into the BiLSTM network for missing data reconstruction, thus improving the BiLSTM prediction accuracy. The six-story frame structure scale model and Benchmark finite element simulation model are analyzed. The experimental results show that, compared with the mainstream methods of EEMD-LSTM, BiLSTM and LSTM, the EEMD-BiLSTM proposed in this study has the highest pre-diction accuracy. In the case of 5% missing ratio, its R2 metrics is 0.85478 and 0.86245 respectively. Therefore, the preprocessing of unsteady structural acceleration response data by EEMD method can effectively improve the prediction accuracy of BiLSTM, which provides a more adaptable method and theory for the problem of missing data in structural monitoring.

TP

重庆市教委科学技术研究项目(KJQN202201805，KJQN202301801)资助；重庆人文科技学院科学技术研究项目(CRKZK2023007，JSJGC202201，JSJGC202205)资助