导波受载荷影响补偿的深度学习神经网络方法  

Guided Wave Load Influence Compensation Method Based on Deep Learning Neural Network

作　　者：杨宇[1] 王彬文[1] 曹雪洋 郑辉[2] 邱雷[2] YANG Yu;WANG Binwen;CAO Xueyang;ZHENG Hui;QIU Lei(Aircraft Strength Research Institute of China Xi'an,710065,China;State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics and Astronautics Nanjing,210016,China)

机构地区：[1]中国飞机强度研究所,西安710065 [2]南京航空航天大学机械结构力学及控制国家重点实验室,南京210016

出　　处：《振动．测试与诊断》2022年第4期812-819,832,共9页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51921003);国家自然科学基金重点资助项目(51635008);教育部霍英东青年教师基金资助项目(161048);江苏省六大人才高峰资助项目(GDZB⁃035);江苏高校优势学科建设工程资助项目。

摘　　要：针对基于导波的结构健康监测(structural health monitoring,简称SHM)中急需解决的环境载荷影响问题,开展碳纤维复合材料板结构与加筋结构载荷影响实验,研究了载荷对导波的影响规律。在此基础上,提出了一种基于深度学习神经网络的载荷补偿方法,该方法通过建立补偿标准,对网络结构及网络参数进行设计,有效减少了参考信号存储,实现了大范围载荷补偿。通过实验数据验证了该方法的有效性,结果表明,在补偿范围0~90 MPa内,补偿精度达到-20 dB。将补偿方法结合损伤成像方法应用在碳纤维复合材料板结构的损伤监测,结果表明,补偿前无法实现损伤定位,补偿后损伤定位误差≤0.9 cm,有效提高了载荷影响下的损伤诊断可靠性。To eliminate the influence of environmental load on the guided wave-based structural health monitor⁃ing(SHM),a load compensation method based on deep learning neural network(DLNN)is proposed based on the load influence experiments carried out on carbon fiber composite structures.This method utilizes compensa⁃tion standards for the establishment of the DLNN with the optimized network structure and network parameters.As a result,the storage of reference signals is effectively reduced and a wide range of load can be compensated by the proposed method.The effectiveness of the method is verified by experimental data.Experimental results show that the compensation accuracy reaches-20 dB in the load range of 0~90 MPa.Finally,the compensa⁃tion method combined with the damage imaging method is demonstrated on the damage monitoring of the carbon fiber composite structures.Results show that the damage localization error is less than 0.9 cm and the reliability of damage diagnosis under the influence of load is effectively improved.

关 键 词：结构健康监测 载荷影响 载荷补偿 深度学习 导波 

分 类 号：TH114[机械工程—机械设计及理论] TB55[理学—物理]