Research on conditional characteristics vision real-time detection system for conveyor belt longitudinal tear

Journal article (2017)

Authors

Tiezhu Qiao Taiyuan University of Technology
Xinyu Li Taiyuan University of Technology
Y. Pang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Yuxiang Lü Taiyuan University of Technology
Feng Wang Taiyuan University of Technology
Baoquan Jin Taiyuan University of Technology
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2017 Tiezhu Qiao, Xinyu Li, Y. Pang, Yuxiang Lü, Feng Wang, Baoquan Jin
DOI
help_outline
https://doi.org/10.1049/iet-smt.2017.0100
Coal Belts Real-time systems Conveyors Mining equipment
More Info
expand_more

Published Date

2017

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

Conveyor belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of conveyor belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and conveyor belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of conveyor belt, thus the problem of starting warning only based on single feature can be effectively avoided.