the rapid identification and monitoring of non-coal foreign objects on conveyor belts are imperative. As condition monitoring technology advances, the monitoring methods for
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· During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the conveyor belt, resulting in tears and fractures, which affect transportation efficiency and production safety. In this paper, we propose a lightweight target detection algorithm, GhostNet-CBAM-YOLOv4, to resolve
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· At present, conveyor belt tearing is the most serious fault of belt conveyor, causing the greatest loss. This paper aims to overcome the issues of poor accuracy, low precision, and poor real-time performance in the longitudinal tear detection of the conveyor belt of the belt conveyor. Specifically, this paper presents a method of
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· The improved YOLOv5 algorithm can provide a reference for the accurate recognition of targets in a complex underground environment and is better than SSD, Y OLOv3, and YOLov5. The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden
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· This conveyor belt image acquisition system collects some images in a coal mine, 5 frames are picked from the collected images, as shown in Fig. 2.Fig. 2 (1) shows no failure on the conveyor belt; Fig. 2 (2)–(4) from left to right images show the tearing crack arranged from big to small; Fig. 2 (5) shows the conveyor belt cracks.
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· Abstract. To tackle the issues of high training expense, inadequate real-time functionality, and insufficient reliability in deep neural network-based visual detection of conveyor belt damage
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The foreign object doped in the conveying belt is the most important factor to cause the tearing of the conveying belt. In order to solve the problem of low accuracy and poor real-time performance of foreign object detection, a new method based on improved Nanodet is proposed in this paper. The hardware of conveyor belt foreign object detection system
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· An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and images acquired with low quality, recursive filtering and MSRCR image
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safe and efficient operation of belt conveyors. 2. Algorithm Improvement 2.1. Video Image Preprocessing The monitoring image of the belt conveyor in the Huangling coal mine is presented in Figure1. It can be seen from Figure1that the image is affected by
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· Belt conveyors are one of the most widely used bulk solids handling systems across the mining and mineral processing industries (Shen et al. 2018). According to the Chinese Standard GB/T10595-20 A visual detection method for conveyor belt misalignment based on the improved YOLACT network: Particulate Science and
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· Improved Technology: New conveyor belts often incorporate improved materials and technology that can enhance the efficiency and longevity of your conveyor system. Warranty and Support: Replacement belts typically come with a manufacturer’s warranty and support, offering peace of mind and protection against future issues.
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In this study, we propose a new depth learning method specifically designed for detecting longitudinal tears in conveyor belts. This method employs a linear Charge-Coupled
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In order to reduce the noise of a defect electromagnetic signal of the steel cord conveyor belt used in coal mines, a new signal noise reduction method by combined use of the improved threshold wavelet and Empirical Mode Decomposition (EMD) is proposed. Firstly, the denoising method based on the improved threshold wavelet is applied to reduce the
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· In the realm of industrial automation, conveyor belt systems stand as the backbone of production lines, facilitating the seamless movement of goods across various stages of manufacturing and packaging. Integral to the optimization and safety of these systems are conveyor belt sensors, sophisticated devices engineered to monitor and
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· By implementing a conveyor belt system with food-grade materials, easy-to-clean surfaces, and gentle product handling features, the facility achieved a 30% reduction in product waste and improved
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· Problems with such systems, however, can have a major impact on operations, which is why belt conveyors must be monitored constantly. JFE Steel’s new monitoring system (Figure 1) uses AI image-recognition and other data science technologies to automatically detect damage to conveyor belts by examining images captured with
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· The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching, tearing, and even broken belts. Aiming at the problem that it was difficult to
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· Powerful belt conveyor is the crucial transportation equipment for coal mining. Keeping its stable operation is essential to ensure the coal mining efficiency. At present, damage detection systems based on machine vision are widely studied and deployed. Clear images are the foundation for successful damage detection. However, in
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conveyor belts, resulting in insufficient detection accuracy, overlooked detection, and elevated false improvement in deep learning-based damage detection methodsforconveyorbelts.Zhangetal.[16
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· There are many common longitudinal tear detection methods for conveyor belts (Li and Miao 2016), such as the pre-fill coil detection method (Pang and Lodewijks 2006;Tang et al. 2013), contact
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· The improved method performed better than the original model on the other two datasets and could effectively avoid misdetection and omission detection, and demonstrated good performance, verifying its effectiveness. During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the
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· Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image, a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection (FCOS) algorithm. The regression performance
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· Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environment March 2024 DOI: 10
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· The improved YOLOv7 [1 7] model is primarily used for. longitudinal tear detectio n of conveyor belts. T he schematic. diagram of the improved YOLOv7 show in Figure 4. The overall structure of the
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Finally, the improved algorithm is compared with four classical detection algorithms using the damage feature dataset of steel wire rope-core conveyor belts. The experimental result shows that the proposed algorithm achieves an average detection precision of 91.8% and a detection speed of 40 frames per second (FPS) for images collected in harsh mining
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· Cleated belt conveyors can improve efficiency in manufacturing processes by reducing the amount of manual labor required to transport materials. This can save time and labor costs, leading to improved overall efficiency in the manufacturing process. 5. Durability. Durable materials, such as PVC or rubber, are used to create cleated
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· Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet January 2023 IEEE Access PP(99):1-1 DOI:10. 1109/ACCESS.2023.3253624 License CC BY-NC-ND 4.0 Authors: Di Miao Di Miao
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· PET monofilament is one of the important materials for making conveyor belts, and the monofilament extrusion machine plays a crucial role to ensure the consistency in diameter and shrinkage of PET monofilament. In this article, we'll explore the key features of a pet monofilament extrusion line that can greatly improve the quality of
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· An improved YOLOv3 replace the original backbone, Darknet53, as EfficientNet was proposed in [] to detect and locate conveyor belt damage region in real-time. Due to the balanced network architecture in depth and image resolution, it has reached a good balance between detection speed and mAP (mean Average Precision).
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· Checking for Material Build-up: Material build-up on conveyor roller material, pulleys, and the underside of the belt can cause the belt to misalign. Regular cleaning of these components is essential to maintain proper alignment. Square the Frame: The conveyor frame itself must be square and parallel to the belt.
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