Technology Research Institute of Shanxi Jincheng Coal Group Co. , Ltd.
Jinneng Holding Equipment Manufacturing Group Staff Quality Improvement Center

针对煤矿钢丝绳芯输送带采用人工巡检效率低、准确度差的问题,研发了一种基于X射线和激光三维重构技术的矿用钢丝绳芯输送带智能无损探伤监测系统,该系统通过卷积神经网络、点云数据三维重构和数据调度算法进行数据分析处理,实现了对钢丝绳芯输送带内部及外部的失效特征检测预警、外部磨损区域的框选面积及磨损体积计算和钢丝绳芯断丝、抽头、扭曲、接头长度变化等失效特征故障诊断功能,经过实验验证,系统对输送带表面损伤识别分辨率小于1.5mm×1.5mm,磨损区域识别面积精度小于1mm2,鼓包识别体积精度小于1.5mm3,对输送带断绳(丝)、鼓包和接头抽动位移3类损毁失效识别正确率约为98%。该系统根据钢丝绳芯输送带数字化检测数据分析,可准确判断输送带的故障点并发出预警,有效保障输送带安全使用。

Aiming at the problems of low efficiency and poor accuracy of manual inspection of wire rope core conveyor belt in coal mine, based on X-ray and laser 3D reconstruction technology, an intelligent non-destructive inspection system for mine wire rope core conveyor belt is developed, the system analyzes and processes data through 3D reconstruction of convolutional neural network and point cloud data and data scheduling algorithms, the functions of failure feature detection and early warning of the inner and outer parts of the wire rope core conveyor belt, the calculation of the frame selection area and wear volume of the outer wear area, and the fault diagnosis of the broken wire, tap, twist and the change of the joint length of the wire rope core are realized, the experimental results show that the system can identify the surface damage of conveyor belt with the resolution of less than 1. 5 mm×1. 5 mm, the area precision of wear area is less than 1 mm2, and the volume precision of drum is less than 1. 5 mm3, the correct recognition rate of belt breakage, bulge and joint displacement is about 98% . Based on the analysis of the digital test data of the wire rope core conveyor belt, the system can accurately judge the fault point of the conveyor belt and give an early warning, which ensures the safe use of the conveyor belt.

晋能控股装备制造集团科技项目(JMJS-JSKF-2020-0022)

唐建军, 赵田田, 田 康, 等. 矿用钢丝绳芯输送带智能无损探伤监测系统研究 [J]. 煤炭工程, 2023, 55(12): 198-202.