Belt Tear Detection for Coal Mining Conveyors

Surface Evaluation of Gyroid Structures for Manufacturing Rubber-Textile Conveyor Belt Carcasses Using Micro-CT

Gyroid structures are among the most widely used three-dimensional elements produced by various additive manufacturing technologies. This paper focuses on a metrological analysis of Flexfill 92A material specimens with a relative density (25 to 85%) using industrial computer tomography. The results show that for a given structure, the best method is to use surface determination with the closure of internal defects in the material. The analysis implies that the smallest deviations of the specimens' external dimensions were achieved with respect to the CAD model at the highest relative densities. The wall thickness shows the smallest percentage change of 0.5685 at 45% relative density and the largest at 25% and 85% relative density. The nominal-actual comparison of manufactured specimens to the CAD model shows the smallest cumulative deviation of 0.209 mm at 90% and 25% relative density, while it slightly increases with increasing relative density. All produced specimens have a smaller material volume than their theoretical volume value, while the percentage change in volume is up to 8.6%. The surface of specimens is larger compared with the theoretical values and the percentage change reaches up to 25.3%. The percentage of pores in the specimens increases with increasing relative density and reaches 6%. The acquired knowledge will be applied in the framework of research focused on the possibilities of using additive manufacturing to produce a skeleton of rubber-textile conveyor belts. This paper presents initial research on the possibility of replacing the carcass of rubber-textile belts with an additive technology use.

Real-time classification of longitudinal conveyor belt cracks with deep-learning approach

Long tunnels are a necessary means of connectivity due to topological conditions across the world. In recent years, various technologies have been developed to support construction of tunnels and reduce the burden on construction workers. In continuation, mountain tunnel construction sites especially pose a major problem for continuous long conveyor belts to remove crushed rocks and rubbles out of tunnels during the process of mucking. Consequently, this process damages conveyor belts quite frequently, and a visual inspection is needed to analyze the damages. Towards this, the paper proposes a model to configure the damage and its size on conveyor belt in real-time. Further, the model also localizes the damage with respect to the length of conveyor belt by detecting the number markings at every 10 meters of the belt. The effectiveness of the proposed framework confirms superior real-time performance with optimized model detecting cracks and number markings with mAP of 0.850 and 0.99 respectively, while capturing 15 frames per second on edge device. The current study marks and validates the versatility of deep learning solutions for mountain tunnel construction sites.

Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model

As an equipment failure that often occurs in coal production and transportation, belt conveyor failure usually requires many human and material resources to be identified and diagnosed. Therefore, it is urgent to improve the efficiency of fault identification, and this paper combines the internet of things (IoT) platform and the Light Gradient Boosting Machine (LGBM) model to establish a fault diagnosis system for the belt conveyor. Firstly, selecting and installing sensors for the belt conveyor to collect the running data. Secondly, connecting the sensor and the Aprus adapter and configuring the script language on the client side of the IoT platform. This step enables the collected data to be uploaded to the client side of the IoT platform, where the data can be counted and visualized. Finally, the LGBM model is built to diagnose the conveyor faults, and the evaluation index and K-fold cross-validation prove the model's effectiveness. In addition, after the system was established and debugged, it was applied in practical mine engineering for three months. The field test results show: (1) The client of the IoT can well receive the data uploaded by the sensor and present the data in the form of a graph. (2) The LGBM model has a high accuracy. In the test, the model accurately detected faults, including belt deviation, belt slipping, and belt tearing, which happened twice, two times, one time and one time, respectively, as well as timely gaving warnings to the client and effectively avoiding subsequent accidents. This application shows that the fault diagnosis system of belt conveyors can accurately diagnose and identify belt conveyor failure in the coal production process and improve the intelligent management of coal mines.

Hybrid Compression Optimization Based Rapid Detection Method for Non-Coal Conveying Foreign Objects

The existence of conveyor foreign objects poses a serious threat to the service life of conveyor belts, which will cause abnormal damage or even tearing, so fast and effective detection of conveyor foreign objects is of great significance to ensure the safe and efficient operation of belt conveyors. Considering the need for the foreign object detection algorithm to operate in edge computing devices, this paper proposes a hybrid compression method that integrates network sparse, structured pruning, and knowledge distillation to compress the network parameters and calculations. Combined with a Yolov5 network for practice, three structured pruning strategies are specifically proposed, all of which are proven to have achieved a good compression effect. The experiment results show that under the pruning rate of 0.9, the proposed three pruning strategies can achieve more than 95% compression for network parameters, more than 90% compression for the computation, and more than 90% compression for the size of the network model, and the optimized network is able to accelerate inference on both Central Processing Unit (CPU) and Graphic Processing Unit (GPU) hardware platforms, with a maximum speedup of 70.3% on the GPU platform and 157.5% on the CPU platform, providing an excellent real-time performance but also causing a large accuracy loss. In contrast, the proposed method balances better real-time performance and detection accuracy (>88.2%) when the pruning rate is at 0.6~0.9. Further, to avoid the influence of motion blur, a method of introducing prior knowledge is proposed to improve the resistance of the network, thus strongly ensuring the detection effect. All the technical solutions proposed are of great significance in promoting the intelligent development of coal mine equipment, ensuring the safe and efficient operation of belt conveyors, and promoting sustainable development.

Non-Destructive Testing of Pipe Conveyor Belts Using Glass-Coated Magnetic Microwires

Belt conveyors have been used in a wide range of applications because in comparison to the alternative solutions represented by the rail or road transportation, their operation is typically more cost effective, with lower energy demands and the possibility of utilizing renewable energy sources, and during their operation, less noise and air pollution is produced. The presented article is focused on pipe belt conveyors that are even more sustainable and in harmony with the environment, especially considering transportation of fine and dusty materials. More specifically, pipe belt conveyors have the possibility of utilizing microwires as a sensing element for microwire-based sensors for the pipe belt conveyor diagnostics from a mechanical loading point of view. This is because during the enclosing of the pipe conveyor belt, periodical cyclical mechanical loading is applied due to the bending. From the results of the performed set of FEM (Finite Element Method) analyses of the glass-coated magnetic microwires, it can be concluded that during the selection process of the microwires, emphasis should be directed the thickness of the glass coating, which can affect the lifetime of the microwire significantly. The microwire length has negligible influence on the estimated number of bending cycles until the damage or crack occurs.

Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network

The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface.