Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

作     者：Teng, Shuai Chen, Gongfa Liu, Zongchao Cheng, Li Sun, Xiaoli 

作者机构：Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou 510006 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong 999077 Peoples R China Guangzhou Municipal Engn Testing Co Ltd Guangzhou 510520 Peoples R China 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2021年第21卷第12期

页      面：3950-3950页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

主　　题：structural damage detection decision-level fusion 1-D convolutional neural network vibration experiments acceleration signals bridge model 

摘      要：This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering;the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved;for the numerical and experimental models, the detection accuracy was 10% and 16-30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.