基于人工智能的钢丝绳芯输送带断丝检测技术研究

Research on Wire Break Detection Technology of Steel Wire Rope Core Conveyor Belt Based on Artificial Intelligence

针对钢丝绳芯输送带传统断丝断丝监测方法效率较低、工人劳动强度大、检测准确度较低等问题,通过分析钢丝绳芯输送带内的钢丝绳结构,提出了一种基于人工智能的钢丝绳芯输送带断丝检测方法,利用神经网络算法提取胶带内钢丝绳断丝特征,实现了定量准确识别。以含有不同类型断丝缺陷的619钢丝绳的胶带为试验对象,利用断丝检测技术进行断丝检测,结果表明,完成长度为2 m的胶带检测仅需3.8 s,对于表面断丝较多的钢丝绳,反馈得到的信号变化率强度较大,最大值为0.498。本次研究取得了满意的实验结果,验证了方案设计的合理性。

Aiming at the problems of low efficiency,high labor intensity of workers,and low detection accuracy of traditional wire breakage monitoring methods for steel wire core conveyor belts,an artificial intelligence based wire break detection method for steel wire core conveyor belts is proposed by analyzing the structure of the steel wire ropes inside the conveyor belts.Neural network algorithms are used to extract the characteristics of wire breakage inside the belts,achieving quantitative and accurate recognition.Using the tape of 619 steel wire rope with different types of wire breakage defects as the experimental object,wire breakage detection technology was used for wire breakage detection.The results showed that it only took 3.8 s to complete the detection of the tape with a length of 2 m.For steel wire ropes with more surface wire breakage,the feedback signal change rate intensity was relatively high,with a maximum value of 0.498.This study achieved satisfactory experimental results and verified the rationality of the scheme design.