Guo Xiang, Su Xin, Yuan Yingtao, Suo Tao, Liu Yan
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.
International Research Laboratory of Impact Dynamics and Its Engineering Application, Xi'an 710072, China.
Sensors (Basel). 2021 Mar 22;21(6):2207. doi: 10.3390/s21062207.
Pipe structures are at the base of the entire industry. In the industry structure, heat and vibration are transmitted in each pipe. The minimum distance between each pipe is significant to the security. The assembly error and the deformation of the pipeline positions after multiple runs are significant problems. The reconstruction of the multi-pipe system is a critical technical difficulty in the complex tube system. In this paper, a new method for the multi-pipes structure inspection is presented. Images of the tube system are acquired from several positions. The photogrammetry technology calculates positions, and the necessary coordination of the structure is reconstructed. A convolution neural network is utilized to detect edges of tube-features. The new algorithm for tube identification and reconstruction is presented to extract the tube feature in the image and reconstruct the 3D parameters of all tubes in a multi-pipes structure. The accuracy of the algorithm is verified by simulation experiments. An actual engine of the aircraft is measured to verify the proposed method.
管道结构是整个行业的基础。在行业结构中,热量和振动在每根管道中传递。每根管道之间的最小距离对安全性至关重要。多次运行后管道位置的装配误差和变形是重大问题。多管道系统的重建是复杂管道系统中的一个关键技术难题。本文提出了一种多管道结构检测的新方法。从多个位置获取管道系统的图像。利用摄影测量技术计算位置,并重建结构的必要坐标。利用卷积神经网络检测管道特征的边缘。提出了一种新的管道识别和重建算法,以提取图像中的管道特征并重建多管道结构中所有管道的三维参数。通过仿真实验验证了算法的准确性。对飞机的实际发动机进行测量,以验证所提出的方法。