提升机卷筒排绳状态监测是保证设备正常工作的重要方法。通过建立不同排绳状况下的卷筒外轮廓模型并分析边缘特征，归纳出层间跳变次数可以作为排绳状态的评价指标。针对监测过程中视频图像倾斜、振动等影响监测稳定性的问题，采用层级聚类方法，能够快速且准确地进行图像的纠偏与判断。最后对多种类型的卷筒进行监测实验，并与目前的主要方法进行了对比分析，得出该算法具有耗时短，应用范围广，稳定性高，准确率高的优点，具有很好的监测效果。
Abstract
Checking the rope-arranging is an important way to ensure the normal operation of hoist drums.Establishing the external contour model of the reel in different rope laying conditions and analyzing the edge features, the number of inter-layer jump was used as an evaluation index of rope laying conditions.In order to solve the problem of video image skew and dithering, the hierarchical clustering method was proposed to make the image judgment and the algorithm deviation rectification faster and more effective.A detection experiment was carried out with various kinds of drums and the results were compared with those by previous main methods.By comprehensive analysis, it is shown that the new algorithm has the advantages of low time consumption, wide application range, high stability, high accuracy and good detection effect.
关键词
超深矿井 /
提升机 /
排绳故障 /
边缘检测 /
层次聚类
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Key words
ultra-deep mine /
hoist /
rope-arranging fault /
edge detection /
hierarchical clustering
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