Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

IF 2.1 3区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Machines Pub Date : 2023-11-22 DOI:10.3390/machines11121039

Xiangfan Wu, Chusen Wang, Zuzhi Tian, Xiankang Huang, Qian Wang

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引用次数: 0

Abstract

Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

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基于机器视觉的带式输送机皮带偏差故障检测技术研究

传统的煤矿井下带式输送机皮带偏差检测装置存在功能单一、故障定位和分析精度差、自动化程度低、可靠性低等问题。为了解决传统检测设备的缺陷，需要对井下带式输送机运输过程中的带偏故障进行有效、可靠的检测。本文提出了一种基于机器视觉的皮带偏差检测方法。该方法利用全局自适应高动态范围成像方法完成井下图像的亮度增强处理。然后利用 Canny 边缘检测和 Hough 变换算法提取传送带边缘的直线特征。此外，还提出了双基线定位判断方法，实现了带偏故障的识别。最后，建立了皮带机偏差测试台。针对不同的偏差进行了测试实验。胶带偏差检测的准确率达到了 99.45%。本研究提出的方法提高了煤矿井下带式输送机胶带偏差故障检测的可靠性，在煤矿领域具有广泛的应用前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Machines Multiple-

CiteScore

3.00

自引率

26.90%

发文量

1012

审稿时长

11 weeks

期刊介绍： Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.