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Proceedings Article

A new fault detection method of conveyer belt based on machine vision

[+] Author Affiliations
Bingxia Shen, Muyan Ma, Junmin Leng

Beijing Information Science & Technology Univ. (China)

Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79972L (May 26, 2011); doi:10.1117/12.888358
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From Conference Volume 7997

  • Fourth International Seminar on Modern Cutting and Measurement Engineering
  • Jiezhi Xin; Lianqing Zhu; Zhongyu Wang
  • Beijing, China | December 10, 2010

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.

© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Bingxia Shen ; Muyan Ma and Junmin Leng
"A new fault detection method of conveyer belt based on machine vision", Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79972L (May 26, 2011); doi:10.1117/12.888358; http://dx.doi.org/10.1117/12.888358


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