杨林顺, 郑伟, 张帅帅, 杨明锦, 彭晨. 基于深度视觉的筛板故障智能检测方法研究[J]. 选煤技术, 2020, 48(2): 96-100. DOI: 10.16447/j.cnki.cpt.2020.02.023
    引用本文: 杨林顺, 郑伟, 张帅帅, 杨明锦, 彭晨. 基于深度视觉的筛板故障智能检测方法研究[J]. 选煤技术, 2020, 48(2): 96-100. DOI: 10.16447/j.cnki.cpt.2020.02.023
    YANG Linshun, ZHENG Wei, ZHANG Shuaishuai, YANG Mingjin, PENG Chen. Study of the deep vision-based screenplate fault intelligent detection method[J]. Coal Preparation Technology, 2020, 48(2): 96-100. DOI: 10.16447/j.cnki.cpt.2020.02.023
    Citation: YANG Linshun, ZHENG Wei, ZHANG Shuaishuai, YANG Mingjin, PENG Chen. Study of the deep vision-based screenplate fault intelligent detection method[J]. Coal Preparation Technology, 2020, 48(2): 96-100. DOI: 10.16447/j.cnki.cpt.2020.02.023

    基于深度视觉的筛板故障智能检测方法研究

    Study of the deep vision-based screenplate fault intelligent detection method

    • 摘要: 针对人工方法检测筛板故障的滞后性,提出了一种基于深度视觉的筛板故障智能检测方法。该方法采用TOF相机获取筛板的深度图像,利用三维空间关系获得疑似故障区域工位与相机之间的距离,并结合该区域的深度图像数据,实现了对筛板故障的智能诊断。试验表明,该方法不仅可实现筛板故障的实时、智能检测,且检测准确率高,为选煤生产系统的正常运行提供了保障。

       

      Abstract: As the fault of screenplate is often detected with delay in time with the use of artificial method, a deep vision-based intelligent detection method is proposed, which can make intelligent diagnosis of fault based on the depth image of Plant using TOF camera, the distance between the station in the suspected fault area and the camera defined according to the 3-dimentional spatial relationship, and the depth image data in this area. As evidenced by test result, the use of the method can realize screenplate fault detection in a real-time and intelligent manner with a high accuracy, providing a quarantee for the normal operation of coal cleaning systems.

       

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