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基于光学投影与机器视觉的超深井提升机钢丝绳排列检测方法

Inspection Method of Rope Arrangement in the Ultra-Deep Mine Hoist Based on Optical Projection and Machine Vision.

作者信息

Shi Lixiang, Tan Jianping, Xue Shaohua, Deng Jiwei

机构信息

School of Mechanical and Electrical Engineering, Central South University, Changsha 410006, China.

Nanjing Institute of Electronic Technology, Nanjing 210039, China.

出版信息

Sensors (Basel). 2021 Mar 4;21(5):1769. doi: 10.3390/s21051769.

DOI:10.3390/s21051769
PMID:33806401
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7961565/
Abstract

Due to the importance of safety detection of the drum's rope arrangement in the ultra-deep mine hoist and the current situation whereby the speed, accuracy and robustness of rope routing detection are not up to the requirements, a novel machine-vision-detection method based on the projection of the drum's edge is designed in this paper. (1) The appropriate position of the point source corresponding to different reels is standardized to obtain better projection images. (2) The corresponding image processing and edge curve detection algorithm are designed according to the characteristics of rope arrangement projection. (3) The Gaussian filtering algorithm is improved to adapt to the situation that the curve contains wavelet peak noise when extracting the eigenvalues of the edge curve. (4) The DBSCAN (density-based spatial clustering of applications with noise) method is used to solve the unsupervised classification problem of eigenvalues of rope arrangement, and the distance threshold is calculated according to the characteristics of this kind of data. Finally, we can judge whether there is a rope arranging fault just through one frame and output the location and number of the fault. The accuracy and robustness of the method are verified both in the laboratory and the ultra-deep mine simulation experimental platform. In addition, the detection speed can reach 300 fps under the premise of stable detection.

摘要

由于超深井提升机卷筒钢丝绳排列安全检测的重要性，以及目前钢丝绳路径检测在速度、精度和鲁棒性方面达不到要求的现状，本文设计了一种基于卷筒边缘投影的新型机器视觉检测方法。（1）对不同卷筒对应的点光源的合适位置进行标准化，以获得更好的投影图像。（2）根据钢丝绳排列投影的特点设计相应的图像处理和边缘曲线检测算法。（3）改进高斯滤波算法，以适应在提取边缘曲线特征值时曲线包含小波峰值噪声的情况。（4）采用DBSCAN（基于密度的带有噪声的空间聚类）方法解决钢丝绳排列特征值的无监督分类问题，并根据这类数据的特点计算距离阈值。最后，仅通过一帧就能判断是否存在钢丝绳排列故障，并输出故障位置和数量。该方法的准确性和鲁棒性在实验室和超深井模拟实验平台上均得到验证。此外，在检测稳定的前提下，检测速度可达300帧/秒。

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