School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Sensors (Basel). 2021 Jun 8;21(12):3950. doi: 10.3390/s21123950.
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.
本文提出了一种通过一维卷积神经网络（1-D CNN）和决策级融合策略来显著提高结构损伤检测精度的新方法。由于结构损伤通常会导致结构的动态响应发生变化，因此 CNN 可以从振动信号中有效地提取结构损伤信息，并将其分类到相应的损伤类别中。然而，在实际工程中很难建立大规模的传感器系统；采集的振动信号通常是非同步的，并且包含不完整的结构信息，这会导致 CNN 决策阶段出现明显的误差。在本研究中，获得了多个采集点的加速度信号，并使用每个采集点的信号来训练 1-D CNN，并使用相应的测试样本评估它们的性能。然后，融合所有 CNN 的预测结果（决策级融合）以获得综合检测结果。该方法使用数值和实验模型进行了验证，并与控制实验（数据级融合）进行了比较，其中所有加速度信号都用于训练 CNN。结果证实：通过融合多个 CNN 模型的预测结果，可以显著提高检测精度；对于数值和实验模型，检测精度分别提高了 10%和 16-30%，优于控制实验。结果表明：使用每个采集点的加速度信号训练 CNN 并做出自己的决策（CNN 输出），然后融合这些决策，可以有效地提高 CNN 对损伤检测的准确性。
