Abstract
The deviation of the conveyor belt is a common failure that affects the safe operation of the belt conveyor. In this paper, a deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed to detect the deviation at its any position. Firstly, the inspection robot captures the image and the region of interest (ROI) containing the conveyor belt edge and the exposed idler is extracted by the optimized MobileNet SSD (OM-SSD). Secondly, Hough line transform algorithm is used to detect the conveyor belt edge, and an elliptical arc detection algorithm based on template matching is proposed to detect the idler outer edge. Finally, a geometric correction algorithm based on homography transformation is proposed to correct the coordinates of the detected edge points, and the deviation degree (DD) of the conveyor belt is estimated based on the corrected coordinates. The experimental results show that the proposed method can detect the deviation of the conveyor belt continuously with an RMSE of 3.7 mm, an MAE of 4.4 mm, and an average time consumption of 135.5 ms. It improves the monitoring range, detection accuracy, reliability, robustness, and real-time performance of the deviation detection of the belt conveyor.
Highlights
Belt conveyor is continuous transportation equipment in modern production with the advantages of large capacity, being suitable for long distance, low freight, high efficiency, stable operation, convenient loading and unloading, being suitable for bulk material transportation, etc
A deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed in this paper, which provides a more intelligent solution for the monitoring of the belt conveyor. e main idea is to combine the deep learning algorithm and the digital image processing technology to detect the deviation of the conveyor belt on an inspection robot
To illustrate the advantages of the proposed ROI detector, OM-SSD is compared with MobileNet SSD (M-SSD) in the self-built dataset. en, the performance of the deviation detection algorithm is verified at different standard deviation degree (DD) and shooting heights, and the preferred shooting height is given through the comparative experiments
Summary
Belt conveyor is continuous transportation equipment in modern production with the advantages of large capacity, being suitable for long distance, low freight, high efficiency, stable operation, convenient loading and unloading, being suitable for bulk material transportation, etc. It has become one of the three main industrial conveyances together with automobile and train and has been widely used in coal, mines, ports, electric power, metallurgy, chemical industry, and other fields [1]. The main detection method of the deviation fault is to install two sets of deviation switches on the racks on both sides of the conveyor belt. If the conveyor belt continues to deviate to the set stop position, it triggers another set of deviation switch and it will control the belt conveyor to stop
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