Xiaoqiang Guo

During the operation of the guide rail pair, the accuracy of the guide rail pair is decreased by friction and wear, and the overall performance of the machine tool is directly affected. The influence of surface micro-features of the slider raceway on the dynamic friction factor of the guide rail pair is analyzed in this paper. The relationship between the geometric characteristics of the surface micro-features and the dynamic friction factor is studied, and a prediction model of the dynamic friction factor is established. Meanwhile, the correctness and rationality of the predicted model is verified by the orthogonal experiments of the friction force. It is found that the dynamic friction factor increases with the increase of the groove depth, the height of the peaks and peak–valleys on the surface of the slider raceway. The above research provides a basis for the study of the friction and wear characteristics of guide rail pairs and lays a foundation for improving its friction and wear performance.

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

A complex stratum formed due to the influence of internal and external dynamic geological processes will lead to extremely complex mining conditions in deep exploration and development of oil, gas, coal and other resources, processes mainly threatened by disasters such as coal and gas conflict, mine water inrush, and rock burst. Combined with formation identification and measurement while drilling technology, the drilling level of underground drilling robot in coal mines is constantly developing. In order to prevent coal mine accidents and achieve safe and efficient mining, efficient and accurate drilling is the key, and should be based on research on the influence of complex stratum on the drilling trajectory. In order to comprehensively and systematically summarize the research on the influence of a complex stratum on drilling tool mechanics, this paper describes the history and current situation of complex stratum exploration, measurement while drilling technology, borehole bending conditions, stress analysis of complex coal seams on drilling tools, formation force theory and method, and geosteering drilling technology. In addition, the research and application of directional drilling technology in gas control, water hazard prevention and geological anomaly detection are also discussed.

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 the harsh environment of underground coal seam it is difficult to capture high-quality images, which limits the performance of the machine vision based techniques for the conveyor belt damage detection. To solve this issue, an image denoising and enhancement approach is proposed based on the Invertible Neural Network (INN). Firstly, a new res-block is introduced to improve the feature extraction ability of the proposed model. Then, to take advantage of the INN architecture, input images are split by GLOW Coupling Block in forward path and the noise among the high frequency features are discarded in inverse path. An improved Trainable Guided Filter is proposed to reconstruct the high-frequency images. Besides, an improved multiple loss function is designed to keep detailed feature information and eliminate the effect of noise. The comparisons and experimental results demonstrate that the proposed Denoising Invertible Neural Network (DeINN) presents excellent image denoising ability, and outperforms several existing popular methods. Lastly, a real world application demonstrates that the proposed DeINN satisfies the industrial requirements on belt damage detection.

It is essential to determine the running state of a production line to monitor the production status and make maintenance plans. In order to monitor the real-time running state of an A-class insulation board production line conveniently and accurately, a novel state prediction method based on deep learning and long short-term memory (LSTM) network is proposed. The multiple layers of the Res-block are introduced to fuse local features and improve hidden feature extraction. The transfer learning strategy is studied and the improved loss function is proposed, which makes the model training process fast and stable. The experimental results show that the proposed Res-LSTM model reached 98.9% prediction accuracy, and the average R²-score of the industrial experiments can reach 0.93. Compared with other mainstream algorithms, the proposed Res-LSTM model obtained excellent performance in prediction speed and accuracy, which meets the needs of industrial production.

To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better.

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.

The belt conveyor is the most commonly used conveying equipment in the coal mining industry. As the core part of the conveyor, the belt is vulnerable to various failures, such as scratches, cracks, wear and tear. Inspection and defect detection is essential for conveyor belts, both in academic research and industrial applications. In this paper, we discuss existing techniques used in industrial production and state-of-the-art theories for conveyor belt tear detection. First, the basic structure of conveyor belts is discussed and an overview of tear defect detection methods for conveyor belts is studied. Next, the causes of conveyor belt tear are classified, such as belt aging, scratches by sharp objects, abnormal load or a combination of the above reasons. Then, recent mainstream techniques and theories for conveyor belt tear detection are reviewed, and their characteristics, advantages and shortcomings are discussed. Furthermore, image dataset preparation and data imbalance problems are studied for belt defect detection. Moreover, the current challenges and opportunities for conveyor belt defect detection are discussed. Lastly, a case study was carried out to compare the detection performance of popular techniques using industrial image datasets. This paper provides professional guidelines and promising research directions for researchers and engineers based on the leading theories in machine vision and deep learning.