Sun Yuhang, Li Mengxuan, Dong Ruiwen, Chen Weiyu, Jiang Dong
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel). 2022 Jul 11;22(14):5184. doi: 10.3390/s22145184.
Bolted connections have been widely applied in engineering structures, loosening will happen when bolted connections are subjected to continuous cyclic load, and a significant rotation between the nut and the bolt can be observed. Combining deep learning with machine vision, a bolt loosening detection method based on the fifth version of You Only Look Once (YOLOv5) is proposed, and the rotation of the nut is identified to detect the bolt loosening. Two different circular markers are added to the bolt and the nut separately, and then YOLOv5 is used to identify the circular markers, and the rotation angle of the nut against the bolt is calculated according to the center coordinate of each predicted box. A bolted connection structure is adopted to illustrate the effectiveness of the method. First, 200 images containing bolts and circular markers are collected to make the dataset, which is divided into a training set, verification set and test set. Second, YOLOv5 is used to train the model; the precision rate and recall rate are respectively 99.8% and 100%. Finally, the robustness of the proposed method in different shooting environments is verified by changing the shooting distance, shooting angle and light condition. When using this method to detect the bolt loosening angle, the minimum identifiable angle is 1°, and the maximum detection error is 5.91% when the camera is tilted 45°. The experimental results show that the proposed method can detect the loosening angle of the bolted connection with high accuracy; especially, the tiny angle of bolt loosening can be identified. Even under some difficult shooting conditions, the method still works. The early stage of bolt loosening can be detected by measuring the rotation angle of the nut against the bolt.
螺栓连接在工程结构中已得到广泛应用，当螺栓连接承受连续循环载荷时会发生松动，并且可以观察到螺母与螺栓之间有明显的转动。将深度学习与机器视觉相结合，提出了一种基于You Only Look Once（YOLOv5）第五版的螺栓松动检测方法，通过识别螺母的转动来检测螺栓松动。分别在螺栓和螺母上添加两个不同的圆形标记，然后使用YOLOv5识别圆形标记，并根据每个预测框的中心坐标计算螺母相对于螺栓的旋转角度。采用螺栓连接结构来说明该方法的有效性。首先，收集200张包含螺栓和圆形标记的图像制作数据集，将其分为训练集、验证集和测试集。其次，使用YOLOv5训练模型；准确率和召回率分别为99.8%和100%。最后，通过改变拍摄距离、拍摄角度和光照条件，验证了所提方法在不同拍摄环境下的鲁棒性。当使用该方法检测螺栓松动角度时，最小可识别角度为1°，相机倾斜45°时最大检测误差为5.91%。实验结果表明，所提方法能够高精度地检测螺栓连接的松动角度；特别是能够识别螺栓松动的微小角度。即使在一些困难的拍摄条件下，该方法仍然有效。通过测量螺母相对于螺栓的旋转角度，可以检测螺栓松动的早期阶段。
