基于机器视觉的提升机钢丝绳抖动超差检测研究

Detection of Abnormal Jitter of Hoist Wire Rope Based on Machine Vision

针对矿井提升机在运输过程中钢丝绳容易发生故障造成煤矿安全事故的问题,研究了基于机器视觉的矿井提升机钢丝绳动态监控系统。使用矿用摄像机采集钢丝绳运行图像,使用暗通道先验原理将原始图清晰化处理,最后对视频图像进行Candy边缘和直线特征检测,标定钢丝绳运行正常位置,当钢丝绳发生异常抖动偏离正常位置时并且超过设定阈值,系统检测到钢丝绳抖动超差故障并发生报警。试验结果表明,设计的钢丝绳监控系统可以快速有效地检测钢丝绳异常抖动故障,减少煤矿安全事故的发生,具有一定的可靠性。

In view of coal mine safety accidents which is caused by wire rope of mine hoist and prone to failure in the process of transportation,this paper studies the dynamic monitoring system of steel wire rope of mine hoist based on machine vision.The running image of steel wire rope is collected by mine camera,and the original image is clarified by using dark channel prior principle.Then,the video image is detected by candy edge detection and linear feature detection,and the normal running position of steel wire rope is calibrated.When the abnormal shaking of steel wire rope deviates from the normal position and exceeds the set threshold,the system detects the abnormal shaking fault of steel wire rope,and the alarm rings.The test results show that the wire rope monitoring system designed in this paper can quickly and effectively detect the abnormal vibration fault of wire rope,and reduce the occurrence of coal mine safety accidents,which has certain reliability.