采用BP神经网络优化的振动信号压缩感知方法  

An approach for compressive sensing of vibration signal using BP neural network optimization

作　　者：朱一凯 陈安妮 余哲帆 万华平 ZHU Yi-kai;CHEN An-ni;YU Zhe-fan;WAN Hua-ping(Key Laboratory of Concrete and Pre‐stressed Concrete Structures of Ministry of Education,Southeast University,Nanjing 211189,China;College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]东南大学混凝土及预应力混凝土结构教育部重点实验室,江苏南京211189 [2]浙江大学建筑工程学院,浙江杭州310058

出　　处：《振动工程学报》2023年第5期1234-1243,共10页Journal of Vibration Engineering

基　　金：国家重点研发计划资助项目(2021YFF0501001);浙江省重点研发计划资助项目(2021C03154);国家自然科学基金资助项目(51878235);混凝土及预应力混凝土结构教育部重点实验室开放课题(CPCSME2020‐05)。

摘　　要：无线传感网络逐渐应用于结构健康监测,但是因能耗问题难以实现长期、高频的数据采集工作。压缩感知技术可利用少量的采样点重构原始信号,有望降低无线传感网络的能耗。实测振动信号因受到噪声干扰而导致稀疏性有限,常用于压缩感知的LASSO算法难以精确求解稀疏系数,进而影响振动信号重构效果。引入BP神经网络优化LASSO算法解得的稀疏系数,BP神经网络经ADAM优化算法训练后,可有效提升振动信号重构精度。用三层框架结构的模拟加速度数据和广州塔的监测加速度数据验证方法的有效性,并探讨了正则化参数和优化迭代次数的影响。结果表明,基于BP神经网络优化的压缩感知方法的信号重构效果在不同压缩率下均优于非优化的压缩感知方法。Wireless sensor networks(WSNs)are gradually applied to structural health monitoring.Due to the involved energy con-sumption issue,it is difficult for WSNs to achieve long-term and high-frequency data acquisition.Compressive sensing(CS)is able to use a small number of sampling points to reconstruct the original signal,which is expected to reduce the energy consump-tion of the WSNs.The sparsity of the measured vibration signal is limited due to the noise contamination.This causes the failure of the LASSO,a widely-used CS algorithm,in seeking the accurate sparse coefficient,which hinders the reconstruction performance of CS of the vibration signal.This paper proposes a method to optimize the sparse coefficient to effectively improve the accuracy of reconstructed vibration signal by using BP neural network.The simulated acceleration data of a three-floor frame and the monitored acceleration data of Canton Tower are both used to verify the effectiveness of the proposed CS method.The effects of regulariza-tion parameters and the number of optimization iterations are explored in detail.The result shows that the proposed optimized CS method performs better than the non-optimized one under different compressed ratios.

关 键 词：结构健康监测 压缩感知 BP神经网络 稀疏系数 LASSO算法 

分 类 号：TU312.3[建筑科学—结构工程] TN957.52[电子电信—信号与信息处理]