基于双目视觉的振动筛运行状态在线检测方法

摘要

In view of problems that existing detection methods of operating state of vibrating screen can only detect local operating state of vibrating screen,and has shortcomings such as low precision and poor timeliness,an on-line detection method of whole operating state of vibrating screen based on binocular vision technology was put forward.Firstly,the method uses binocular vision detection device to capture movement images of the vibrating screen.Then it makes graying processing for the images,uses multi-scale Harris corner detection algorithm to obtain the feature points of the images,and introducing image pyramid theory to improve Lucas-Kanade optical flow estimation algorithm,so as to enhance on-line tracking ability of motion track for feature points.Finally,BP neural network classifier is designed to complete analysis and identification of motion trajectory of the feature points,so as to realize detection of whole running condition of vibrating screen.Test results show that the detection method has high accuracy and good timeliness,which can realize on-line detection and analysis of whole running state of vibrating screen with omni-directional and multi-angle tracking and identification for the trajectory of vibrating screen.Accuracy of the method respectively reached 97.917％,90.667％,96.431％ and 93.181％ when vibrating screen was in four kinds of states of stopping,normality and suspected fault and fault.%针对现有振动筛运行状态检测方法仅能对振动筛局部运行状态进行检测,且存在精度低、时效性差等问题,提出了一种基于双目视觉的振动筛运行状态在线检测方法.首先通过双目视觉检测装置对振动筛的运动图像进行采集;然后对图像进行灰度化处理,利用多尺度Harris角点检测算法获取图像的特征点,引入图像金字塔理论改进Lucas-Kanade光流估计算法,提高图像特征点运动轨迹的在线追踪性能;最后设计BP神经网络分类器,完成对特征点运动轨迹的分析与辨识,实现对振动筛整体运行状态的检测.试验结果表明,该检测方法准确性高、时效性好,可对振动筛运动轨迹进行全方位、多角度的追踪和辨识,实现了振动筛整体运行状态的在线检测和分析.振动筛在停止、正常、疑似故障和故障4种状态下,该方法的准确率分别达到了97.917％、90.667％、96.431％和93.181％.