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- Title
Belt Deviation Detection System Based on Deep Learning under Complex Working Conditions.
- Authors
Peng Zhang; Shaochuan Xu; Wenzhu Wang
- Abstract
As one of the important transportation tools for coal transportation, the operating efficiency of belt conveyor is directly related to the production efficiency of coal. However, belt conveyor failures often occur due to belt deviation. Generally speaking, the transportation distance of belt conveyors is relatively long, and the transportation environment is relatively harsh. The use of manual monitoring of the operation status of belt conveyors is inefficient and increases labor costs. The existing belt deviation detection system can play a good role in some conventional environments. However, under complex working conditions such as heavy dust, insufficient light, and camera deviation, existing detection systems will be affected. Therefore, this paper proposes a belt deviation detection system based on deep learning under complex working conditions. The characteristics of the belt and idler are extracted through the convolutional neural network, so as to position the belt and idler. With the characteristic points on the belt and idler as input, a mathematical model of belt deviation is established to calculate the degree of belt deviation, and alarms according to different degrees of deviation. The system has been tested in the actual field, and has a certain accuracy and real-time, which largely solves the problem of belt deviation detection under complex working conditions, reduces the fault frequency of belt conveyor, and thus improves the working efficiency of belt conveyor.
- Subjects
DEEP learning; CONVOLUTIONAL neural networks; CONVEYOR belts; BELT conveyors; COAL transportation; LABOR costs
- Publication
IAENG International Journal of Applied Mathematics, 2023, Vol 53, Issue 3, p863
- ISSN
1992-9978
- Publication type
Article