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.
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