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Proceedings Paper

A new fault detection method of conveyer belt based on machine vision
Author(s): Bingxia Shen; Muyan Ma
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Paper Abstract

A new fault detection and measurement method of conveyer belt based on machine vision is proposed. The conveyer belt used in coal mine transportation usually goes two kinds of faults: joint's elongation and local rust. Under this engineering background, the system focuses on detecting the state of conveyer belt and measuring the fault size. This paper brings forward a modified BP neural network to detect and classify different faults. The new BP algorithm's detecting speed is rapid, and the correct recognition rate of the joint and erosion has a great improvement. The measurements of joint's length and erosion's area are realized on the machine vision platform which built by LabVIEW IMAQ Vision module. And the measurements have a high accuracy. The results demonstrate that the new method is effective and efficiency.

Paper Details

Date Published: 26 May 2011
PDF: 7 pages
Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79972L (26 May 2011); doi: 10.1117/12.888358
Show Author Affiliations
Bingxia Shen, Beijing Information Science & Technology Univ. (China)
Muyan Ma, Beijing Information Science & Technology Univ. (China)


Published in SPIE Proceedings Vol. 7997:
Fourth International Seminar on Modern Cutting and Measurement Engineering
Jiezhi Xin; Lianqing Zhu; Zhongyu Wang, Editor(s)

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