摘要
为保证结构的安全性和耐久性,宜对结构早期的损伤情况进行健康监测.采用电阻抗方法和人工神经网络技术对钢制薄梁进行了损伤识别的实验研究,但将测得的电阻抗信号都作为神经网络的输入参数则显得不切实际,所以采用主成分分析的降维方法进行实验数据的预处理,降维后包含着最重要主成分的电阻抗信号代替原始数据将作为神经网络的输入参数.研究表明:采用该技术得到的仿真结果与实验观察非常吻合.
To ensure the safety and durability of the structures,a robust state monitoring technique is generally employed to detect incipient damages in the structures.Based on the experiment,an investigation into damage identification for thin steel beams is presented in this paper using electro-mechanical impedance（EMI） signatures and artificial neural networks（ANNs）.The impracticality of using full-size EMI data to feed ANNs as input is noted.The principal component analysis（PCA）-based is adopted for the measured EMI data for its dimension reduction purposes.The compressed EMI data,represented by the principal components,are then used as ANN input variables instead of the raw EMI data.It is shown that the identification results from the proposed method agree fairly well with the experimental observations.
出处
《宁波大学学报（理工版）》
CAS
2009年第4期553-557,共5页
Journal of Ningbo University：Natural Science and Engineering Edition
基金
浙江省自然科学基金(Y107796)
宁波市自然科学基金(2008A610101
2009A610148)
宁波大学2008年胡岚优秀博士基金
关键词
电阻抗
神经网络
主成分分析
结构健康监测
electro-mechanical impedance artificial neural networks principal component analysis structural health monitoring
