Due to the complex structure of most frame structure, a large amount of sensor data needs to be processed for damage diagnosis, which increases the computational cost of diagnosis models and poses a serious challenge to their fast, accurate, and efficient damage diagnosis. In order to address this issue, this paper proposes a novel lightweight damage diagnosis method of frame structure for mobile devices based on convolutional neural networks. This method first uses mean filtering to process the vibration data collected by sensors, and then innovatively combines two convolutional neural network models, ShuffleNet and GhostNet, to form a new lightweight convolutional neural network model called SGNet, thereby reducing the computational cost of the model while ensuring diagnosis accuracy. In order to test the performance of the method proposed in this article, experimental research on damage degree diagnosis and damage type diagnosis is conducted by taking the frame structure provided by Columbia University as the research object, and comparative experiments of performance are conducted with MobileNet, GhostNet, and ShuffleNet. The experimental results show that the lightweight damage diagnosis method for frame structure proposed in this article not only has high damage diagnosis accuracy, but also has fewer computational parameters, when the highest accuracy is 99.8%, the computational parameters are only 1 million. At the same time, it is superior to MobileNet, GhostNet, ShuffleNet in terms of diagnosis accuracy and computational cost, so it is an effective high-precision lightweight damage diagnosis method for frame structure.

由于大多数框架结构结构复杂，损伤诊断需要处理大量传感器数据，增加了诊断模型的计算成本，对其快速、准确、高效的损伤诊断提出了严峻的挑战。针对这一问题，本文提出一种基于卷积神经网络的移动设备框架结构轻量化损伤诊断方法。该方法首先利用均值滤波处理传感器采集到的振动数据，然后创新性地将ShuffleNet和GhostNet这两种卷积神经网络模型结合起来，形成一种新的轻量级卷积神经网络模型SGNet，从而在降低模型计算成本的同时保证诊断的准确性。为了测试本文提出的方法的性能，以哥伦比亚大学提供的框架结构为研究对象，进行损伤程度诊断和损伤类型诊断的实验研究，并与MobileNet进行性能对比实验， GhostNet 和 ShuffleNet。实验结果表明，本文提出的框架结构轻量化损伤诊断方法不仅损伤诊断精度高，而且计算参数少，最高准确率达到99.8%时，计算参数仅为100万个。同时在诊断精度和计算成本上优于MobileNet、GhostNet、ShuffleNet，是一种有效的框架结构高精度轻量化损伤诊断方法。