结合深度信念记忆网络的结构损伤识别

Structural damage identification via a deep belief memory network

从结构响应信号中挖掘敏感损伤特征是基于模式分类的损伤识别方法的关键。为此,将深度信念网络和长短期记忆网络进行混合组网,通过混合学习机制有机结合了两种网络在高阶抽象特征提取和考虑数据序列相关性上的优点。将响应信号传递比值输入深度信念网络,实现初步数据压缩和特征提取,以减少响应中的冗余信息;将特征序列依次输入长短期记忆网络,以考虑响应间的相关性并获取敏感损伤特征;利用Softmax分类层对长短期记忆网络输出的特征进行分类,实现对不同结构损伤模式的识别。三维试验钢框架的损伤识别结果表明:混合学习机制能更好地训练网络参数,整体微调后更有利于后续的损伤特征分类;混合组网方式在包含数值或实测噪声的情况下仍可以有效进行数据压缩、特征提取和分类,准确识别了试验框架的多种损伤工况。

features and considering data sequence correlations.First,transmissibility data from response signals are sequentially input into the DBN to achieve the initial data compression and feature extraction,reducing the redundant information in the responses.Then,the extracted feature sequences are input into the LSTM network to consider the correlation between the different responses for acquiring the relevant sensitive damage features.Finally,a classification layer with the Softmax function is used to classify the features output by the LSTM network.Thereby,different structural damage patterns can be identified.The damage identification results on a three-dimensional experimental steel frame demonstrate that the hybrid learning mechanism can better train the network parameters,and the fine-tuning on the whole hybrid network contributes to the subsequent damage feature classification.Under the pollution of numerical or measured noises,the hybrid network can still effectively perform the data com‐pression,feature extraction and classification.The various damage scenarios of the experimental frame are well identified.