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Copyright of China Rubber Industry is the property of Editorial Office of China Rubber Industry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conveyor belt is one of the most important transportation equipment in underground coal mines. Anchor rod, channel steel, large gangue, and other foreign objects that are mixed with the conveyor belt during conveying operations cause the belt to tear, clog the coal drop opening, and cause other serious safety incidents. They seriously affect the efficiency of transportation and even threaten the lives of workers. Aiming at the existing conveyor belt foreign object detection algorithm’s problems such as weak characterization of slender objects and poor weak semantic feature extraction, a foreign object detection algorithm based on low-level feature enhancement with Transformer is designed, notated as FET–YOLO. Firstly, to address the problem that existing detection networks have difficulty in extracting features of elongated objects, deformable convolution is introduced to enhance the network’s adaptability to the shape characteristics of elongated foreign objects, and the MobileViT module is used to increase the differentiation between the foreign objects and the background in the image, in order to extract features that are more consistent with the diversity of the elongated foreign objects and to weaken the interference of background noise.Secondly, constructing a low-level feature enhancement module LFEM, to improve the representation of weak semantic features of foreign objects in the detection network in order to reduce the probability of wrong detection.Finally, the introduction of gsconv reduces the information loss due to changes in the size of the feature map and ensures that the network extracts features efficiently while reducing the number of model parameters. The training set and validation set are produced by using the video of conveyor belt work in an underground coal mine. The proposed algorithm is compared to three other conveyor belt detection algorithms, and the experimental results show that the proposed algorithm can better solve the problems of poor detection of elongated objects and difficulty in weak semantic feature extraction in conveyor belt foreign object target detection, with higher detection accuracy. For images with a resolution size of 640×640, the performance metrics mAP@0.5 can be up to 0.875, mAP@0.5:0.95 can be up to 0.543, and the detection speed is 75 fps.

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rubber Industry is the property of Editorial Office of China Rubber Industry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the defects of existing longitudinal tearing monitoring system of conveyor belt, such as few monitoring points, poor expansibility, and still need to be on duty, a remote monitoring system for longitudinal tearing of conveyor belt suitable for multi -point monitoring based on cloud technology was designed. The system consists of a word line laser, embedded processing terminal, area array CMOS camera, cloud server, PC monitoring software and mobile phone monitoring software.The embedded processing terminal drives area array CMOS camera to collect laser fringe projected by the laser onto lower surface of the conveyor belt, and analyzes each frame of collected image, extracts the laser fringe skeleton, and calculates the distortion value.When the distortion value is greater than the preset threshold value, it indicates that the conveyor belt has longitudinal tear and the embedded processing terminal drives the alarm with voice alarm.When the distortion value is less than the threshold value, it indicates that the conveyor belt works normally.The embedded processing terminal transfers image to PC via file transfer protocol and saves it locally.PC monitoring software uses secure file transfer protocol to send the received image information to the cloud server. Mobile phone monitoring software uses the Web server provided by Tomcat to access the cloud server and obtain the image information in real time.The experimental results show that the system can realize a full range of remote real -time monitoring of longitudinal tearing of conveyor belts on site, PC terminal and mobile terminal, and can monitor the running condition of the conveyor belt at any monitoring point in anytime and at anywhere.

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The conveyor belt tear detection methods based on traditional two-dimensional Otsu segmentation algorithm adopts threshold selection method of exhaustive search. The image segmentation has a long time and poor real-time performance, which cannot meet requirements of tear detection accuracy and speed at the same time. For the above problems, a conveyor belt tear detection method based on lion group optimization two-dimensional Otsu was proposed. Firstly, the conveyor belt image is collected by the conveyor belt tear detection device, median filtering and histogram equalization were used to denoise and enhance the collected image to highlight the torn part. Then, the close to actual segmentation threshold of preprocessed image is obtained by lion group optimization two-dimensional Otsu algorithm, and the conveyor image is segmented by this threshold. Finally, the tear diagnosis is performed by calculating the number of black pixels in the segmented image. The simulation results show that the optimization ability of proposed method is more powerful than the traditional two-dimensional Otsu algorithm, and segmentation effect of conveyor belt is better, the cracks can be accurately segmented from the conveyor belt image. The correct rate of tear recognition of the method is 98.2%, improves the efficiency and shortens the running time, which can meet the accuracy and real-time requirements of the conveyor belt tear detection.

The transverse free vibration model of the rubber conveyor belt with the steel rope core is established based the small deflection theory of the orthogonal anisotropy sheet model. By using the classical solution method, the analytical solutions of the SFSF boundary model of the lower branch conveyor belt, the SCSC boundary model of the upper branch slot shape bottom, and the SFSC boundary model of the side. The main stiffness of the conveyor belt with the steel rope core is derived by using the Khobar approximation. The previous-order natural frequencies of the lower branch conveyor belt and the upper branch conveyor belt, the vibration type and the variation law of natural frequency with the tension variation are obtained by employing the numerical analysis. The results show that the natural frequencies of the lower and the upper branch of the conveyor belt increase with the increasement of the belt tension, and the tension influences the frequency of the lower branch conveyor belt greatest and the frequency of the upper branch conveyor belt bottom smaller. The researching results provide the bending vibration response analysis and the resonance avoiding design of the conveyor belt with the basis.

It is the key for the development of green energy-saving belt conveyor to analysis and research on the rubber conveyor belt of steel core about its dynamic characteristics. Mathematical model of the rubber conveyor belt of steel core were established based on the space coordinate transformation theory. Then 3-D microscopic virtual simulation model were established by using parametric modeling, scanning and tensile of Pro/E software. The Moonery-Rivlin and Bilinear Isotropic model of ANSYS were used to define attribute about rubber covering and wire rope. Simulated dynamic characteristics of rubber conveyor belt of steel core under sinusoidal pressure by setting tied between wire rope and rubber covering. The result show that, under the axial displace load, the axial deformation of steel core presented layered distribution trend along wire rope section. The axial deformation of side strand’s side wire presented two-time-helix change distribution. Every side strand deformation amount was almost same and axial deformation of side strand’s side wire gradually enlarged along handed, the axial deformation amount of side strand changed remarkably relative to Core strand. The axial deformation amount of rubber covering exist little deformation amount in the middle port and large deformation amount all around. The average axial deformation of rubber is larger than the deformation amount of wire rope. which reveal mechanism of energy consumption about rubber conveyor belt of steel core, then that provide theoretical basis for research and development of green energy-saving belt conveyor.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering the viscoelastic properties of belt and combining finite element method and mutibody dynamics theory,a new discrete model of belt using rigid elements describing the rigid property and spring-damping elements describing the flexible property is brought out. Based on Lagrange equations of the second kind,the dynamics differential equations of rigid elements are established. Then,the flexible multibody dynamics equations of belt conveyor system are established by using recursive synthesis method. The running speed of a belt conveyor in starting phase is simulated by this modeling method. The correctness of the simulation results and the practicability of the method are validated by the field testing results of a belt conveyor. The simulation results provides not only the basic data for dynamic design but also valuable theoretical basis for further research on belt conveyor.

In view of problem of unclear image of existing longitudinal tear detection of conveyor belt, a novel vision sensor based on infrared and visible image fusion was proposed. In the proposed sensor, coaxial light from the same lens is projected to infrared CCD and visible CCD respectively by use of beam splitter prism. Then, infrared image and visible image are collected by the infrared CCD and the visible CCD respectively at the same time. Finally, fusion image is obtained by use of pixel-level fusion method. The experimental results show that the fusion image collected by the proposed sensor can meet requirement of high-quality collection of conveyor belt image with clear tear information and detailed background information.

Aiming at the problem of inaccurate segmentation caused by the adhesion and edge blurring of the ore image in the conveyor belt, a method for ore image segmentation of conveyor belt based on U-Net and Res_UNet models is proposed. Firstly, the image to be segmented is processed by gray-scale, median filtering and adaptive histogram equalization, and then the pre-trained U-Net model is used to extract the image contour. Then, after binary image contour, the pre-trained Res_UNet model is used for contour optimization. Finally, OpenCV is used to obtain the segmentation result. Compared with watershed algorithm based on morphological reconstruction and NUR method for 10 test images, the proposed method for ore contour detection and optimization based on deep learning is more accurate, proving its effectiveness for image segmentation of conveyor belt ores. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The determination of the dynamics parameters for the steel cord rubber conveyor belt is the key problem to research the dynamic characteristics and the operation energy consumption of the belt conveyor.Firstly,the transition state and the stable state for the dynamics parameters are defined according to the relaxation and the time-variant characteristics of the rubber conveyor belt.Secondly,the stress analytic solution of the conveyor belt under the dynamic strain is derived by taking the standard solid model as the viscoelastic constitutive model,the transition state and the stable state are analyzed according to the attenuation term in the analytic solution,and the general identification model and the simplified identification model for the standard solid model parameters of the conveyor belt are established.Finally,the dynamic strain loading experiment using st1600 steel cord rubber conveyor belt is performed,and the Matlab cftool tool box is employed to identify the experiment result.The result shows that under the experimental parameters,the difference between the general model identifying result and the simplified model identifying result decreases with the increase of loading cycling times,and the general model identifying result and the simplified model identifying result are the same and maintain stable when the loading cycling times is greater than 67.The loading cycling times or the loading cycling time that the general model identifying result and the simplified model identifying result are the same first can be taken as the criterion to distinguish the transition state and the stable state for the dynamics parameters of the conveyor belt.

A surface fault location of conveyor belt based on saliency and deep convolution neural network was proposed. The method imprints figures on the edge of upper and lower surfaces of conveyor belt, and uses image processing technology to detect the number in belt image, so as to indirectly locate surface fault of the conveyor belt. Firstly, the acquired image of the conveyor belt is preprocessed by Gaussian filtering and gray-scale linear transformation to improve image quality and enhance contrast between the background and the target. Then, visual saliency treatment is conducted to the preprocessed image according to spectral residual theory, and a visual saliency map containing numeric regions is obtained. Finally, saliency map is classified by using the convolution neural network to distinguish digital region from non-digital region. The experimental results show that the method can detect number of conveyor belt image and realize surface fault location of conveyor belt.

In view of problems of low detection precision of existing detection methods of belt longitudinal tear and difficulty to eliminate influence of coal mine complex environment, a detection method of belt longitudinal tear based on support vector machine and infrared image segmentation was proposed. Firstly, the infrared image of belt longitudinal tear is collected. Then, the infrared image is segmented by the method of support vector machine. Finally, the belt longitudinal tear or its tendency is tested accurately by calculating quantity of torn pixels. The test results show that the image segmentation time of the method is short and detection precision is high to 99.1%.

The images collected to detect the fault of conveyor belt have some problems of non-uniform illumination,and poor image quality.It makes fault detection and recognition become difficult.In order to improve the accuracy of fault detection of mine conveyor belt,a method for adaptive enhancement of multi-view images has been proposed in this paper.Firstly,a multi-scale Retinex method with scale adaptive is used to extract the illumination components of an image.According to the extracted illumination component,the non-uniform illumination correction of multi-view images is achieved by using the adaptive gamma function.Finally,the image contrast is enhanced.The experimental results show that this method has many effects,such as removing the effect of non-uniform illumination and coal mine harsh environments,making the average intensity of the image collected by the linear array camera under different viewpoints and illumination conditions consistent,compressing the image dynamic range,improving the image contrast,making the detail of dark area more obvious in the image.This method contributes to improve the accuracy of fault detection,and is suitable for the on-line fault detection of mine conveyor belts. [ABSTRACT FROM AUTHOR]

In order to research the conveyor belt vibration law during the working process under the random load, the random forced vibration model of the conveyor belt is established by using the forced string theory. The initial mathematical boundary conditions of the conveyor belt is derived, and the forced stimulus resulted from eccentric condition of the conveyor belt rollers on the both sides and the vertical load stimulus resulted from the random distribution of the materials on the conveyor belt are established. The model analytical solution is derived by employing the superposition principle and the separation of variables. Finally, the numerical analysis of the vibration characteristics of the conveyor belt is performed, and the validity of the model analytical solution is validated by the analysis results. The research results provide the basis for the dynamic reliability design of the conveyor belt. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the engineering background of mine conveyor belt prone to damage the surface in running process, an improved noise morphology conveyor belt breakage detection algorithm based on multiple structure elements is developed with machine vision technology. By introducing the vector edge detection algorithm and combining with multiple structure elements, the algorithm can effectively detect the damaged gray image edge, and has good noise resistance. The experimental results verify the effectiveness of the proposed detection method. [ABSTRACT FROM AUTHOR]