This paper investigates the use of deep transfer learning based on convolutional neural networks (CNNs) to monitor the condition of bolted joints using acoustic emissions. Bolted structures are critical components in many mechanical systems, and the ability to monitor their condition status is crucial for effective structural health monitoring. We evaluated the performance of our methodology using the ORION-AE benchmark, a structure composed of two thin beams connected by three bolts, where highly noisy acoustic emission measurements were taken to detect changes in the applied tightening torque of the bolts. The data used from this structure is derived from the transformation of acoustic emission data streams into images using continuous wavelet transform, and leveraging pretrained CNNs for feature extraction and denoising. Our experiments compared single-sensor versus multiple-sensor fusion for estimating the tightening level (loosening) of bolts and evaluated the use of raw versus prefiltered data on the performance. We particularly focused on the generalization capabilities of CNN-based transfer learning across different measurement campaigns and we studied ordinal loss functions to penalize incorrect predictions less severely when close to the ground truth, thereby encouraging misclassification errors to be in adjacent classes. Network configurations as well as learning rate schedulers are also investigated, and super-convergence is obtained, i.e., high classification accuracy is achieved in a few number of iterations with different networks. Furthermore, results demonstrate the generalization capabilities of CNN-based transfer learning for monitoring bolted structures by acoustic emission with varying amounts of prior information required during training.

翻译：本文研究了基于卷积神经网络（CNN）的深度迁移学习方法在利用声发射技术监测螺栓连接状态中的应用。螺栓连接结构是许多机械系统中的关键部件，监测其状态能力对于有效的结构健康监测至关重要。我们使用ORION-AE基准结构评估了所提方法的性能，该结构由三个螺栓连接的两根薄梁组成，通过采集高噪声声发射测量数据来检测螺栓施加扭矩的变化。该结构使用的数据通过连续小波变换将声发射数据流转换为图像，并利用预训练CNN进行特征提取与去噪。实验比较了单传感器与多传感器融合在螺栓紧固程度（松动状态）估计中的性能，并评估了原始数据与预滤波数据对结果的影响。我们特别关注了基于CNN的迁移学习在不同测量任务间的泛化能力，研究了序数损失函数以降低接近真实标签的错误预测的惩罚强度，从而使误分类误差倾向于相邻类别。同时探究了网络配置与学习率调度策略，实现了超收敛现象，即不同网络在少量迭代次数内即可达到高分类精度。此外，实验结果证明了基于CNN的迁移学习在声发射监测螺栓结构中的泛化能力，该方法在训练过程中所需先验信息量具有灵活性。