基于ROI边缘图像直线特征的井下带式输送机跑偏故障检测方法

Detection method of downhole belt conveyor deviation fault based on ROI edge image straight line feature

针对井下带式输送机跑偏故障,传统故障检测容易受到噪点干扰,导致故障检测结果与实际不符的问题,提出结合ROI边缘图像直线特征对井下带式输送机跑偏进行故障检测,利用ROI方法锁定目标图像边缘,构建边缘检测器,设计图像边缘搜索路径,获取图像边缘信息。围绕某一特定区域进行模板匹配,采用外接正方形作为最优外接点,根据定位接点像素,按照空间递增顺序进行排序形成点集,避免噪点干扰。试验结果表明,根据ROI边缘图像直线特征变换原理,通过对点线对称,计算特征直线任意一点与原点距离,实现了从图像到参量空间的映射。同时,根据MT-CNN方法实现多层协同检测,结合ROI边缘图像直线坐标之间约束关系,实现了输送机跑偏故障检测。该方法图像灰度图谱与实际图谱一致,检测结果与实际数值误差最大值为50 mm,具有精准检测结果,可为井下带式输送机跑偏故障检测提供决策依据。

In view of the problem that traditional fault detection of downhole belt conveyor deviation is prone to noise interference,resulting in fault detection results inconsistent with the reality,this paper proposes to combine the line features of ROI edge images for fault detection of downhole belt conveyor deviation.ROI method is used to lock target image edge,build edge detector,design image edge search path,and obtain image edge information.The template matching is carried out around a certain area,and the external square is used as the optimal external contact point.According to the positioning contact pixels,the point set is formed according to the spatial increasing order to avoid noise interference.Experiment results show that the mapping from image to parameter space is realized by calculating the distance between any point of the feature line and the origin according to the principle of ROI edge image feature transformation.At the same time,multi-layer cooperative detection is realized according to MT-CNN method,and the constraint relationship between line coordinates of ROI edge image is combined to realize the detection of conveyor deviation fault.The gray scale map of the method is consistent with the actual map,and there is only a maximum error of 50 mm between the detection result and the actual value,which has accurate detection results,and can provide decision-making basis for the detection of downhole belt conveyor deviation fault.