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Foreign objects in coal flow easily cause damage to conveyor belts, and most foreign objects are often occluded, making them difficult to detect. Aiming at solving the problems of low accuracy and efficiency in the detection of occluded targets in a low-illumination and dust fog environment, an image detection method for foreign objects is proposed. Firstly, YOLOv5s back-end processing is optimized by soft non-maximum suppression to reduce the influence of dense objects. Secondly, SimOTA label allocation is used to reduce the influence of ambiguous samples under dense occlusion. Then, Slide Loss is used to excavate difficult samples, and Inner–SIoU is used to optimize the bounding box regression loss. Finally, Group–Taylor pruning is used to compress the model. The experimental results show that the proposed method has only 4.20 × 105 parameters, a computational amount of 1.00 × 109, a model size of 1.20 MB, and an mAP0.5 of up to 91.30% on the self-built dataset. The detection speed on the different computing devices is as high as 66.31, 41.90, and 33.03 FPS. This proves that the proposed method achieves fast and high-accuracy detection of multi-layer occluded coal flow foreign objects. [ABSTRACT FROM AUTHOR]

The efficient monitoring of the belt deviation state will help to reduce unnecessary abnormal wear and the risk of belt tear. This paper proposes a coupling characterization method involving the prediction box features of the target detection network and the linear features of the conveyor belt edge to achieve the quantitative monitoring of conveyor belt deviations. The impacts of the type, location, and number of attention mechanisms on the detection effect are fully discussed. Compared with traditional image-processing-based methods, the proposed method is more efficient, eliminating the tedious process of threshold setting and improving the detection efficiency. In detail, the improved practice and tests are carried out based on the Yolov5 network, and the Grad-CAM technique is also used to explore the effect of attention mechanisms in improving the detection accuracy. The experiments show that the detection accuracy of the proposed method can reach 99%, with a detection speed of 67.7 FPS on a self-made dataset. It is also proven to have a good anti-interference ability and can effectively resist the influence of the conveying material flow, lighting conditions, and other factors on the detection accuracy. This research is of great significance in improving the intelligent operation and maintenance level of belt conveyors and ensuring their safe operation. [ABSTRACT FROM AUTHOR]

Uncertainty can lead to jitter or overshoot in mechanical systems, necessitating the design of multiple constraints to stabilize them. This paper proposes a control structure based on the generalized Udwadia–Kalaba equation to address these constraints simultaneously. An uncertain dynamical model is developed, incorporating both equality and inequality constraints. By integrating diffeomorphism theory, a robust control strategy is designed to ensure compliance with these constraints. Utilizing the Lyapunov approach, the uniform boundedness and uniform ultimate boundedness of the dynamical system are demonstrated. Finally, the feasibility of the proposed control method is validated through its application to a belt conveyor system. • Equality constraints and inequality constraints are simultaneously considered in dynamical system based on the generalized Udwadia–Kalaba approach. • A robust control for handling inequality constraints that does not affect equality constraints is designed. • The robust control can render the uncertain system uniformly bounded and uniformly ultimately bounded. • The method is proposed of applying inequality constraints to belt conveyors to avoid belt breakage and folding faults in engineering. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Topology optimization is a well known and sophisticated method for designing structures. Through a finite element analysis, this method optimizes the design and material distribution to obtain an ideal strength-to-weight ratio and improved strain-to-weight ratio. This study involves the development of a comprehensive model for a brake using the ANSYS Parametric Design Language. The purpose of the model is to accurately characterize the geometry of the disc or drum. The technique of a complex eigenvalue analysis is used to identify the presence of unstable modes occurring at distinct frequencies, indicating instability. A braking force of 17,492 kN was exerted at a rotational velocity of 55 rad/s for 10 s. The optimization process resulted in significant mass reduction while maintaining structural integrity. In the drum brake, the mass was reduced from 114.01 kg to 104.07 kg, while the disc brake's mass decreased from 68.81 kg to 56.68 kg. [ABSTRACT FROM AUTHOR]

Steel cord conveyor belts are widely used in long-distance transportation of various materials. The steel cord belt is composed of rubber and steel cord core, exhibiting excellent viscoelasticity and complex dynamic characteristics during operation. The existing static elastic modulus cannot accurately reflect the real dynamic properties of the conveyor belt. In this paper, based on the catenary theory, the formula for the dynamic elastic modulus of the conveyor belt is derived by introducing the sag and elastic deformation of the steel cord conveyor belt on the basis of the static elastic modulus. The dynamic modeling of the steel cord conveyor belt is carried out using rigid belt block element + flexible beam method. Then, the RecurDyn software is used to establish the dynamic simulation system of the belt conveyor and conduct simulations, which are validated through a test bench. The results show that dynamics simulation based on the dynamic elastic modulus is closer to the real dynamic characteristics of the belt conveyor. Finally, the dynamic characteristics of belt conveyors under different start-up times, different start-up speeds, and idler spacing are analyzed using this modeling and simulation method. The results demonstrate that the dynamic elastic modulus can accurately reflect the real dynamic properties of the conveyor belt, providing a more accurate dynamic modeling and simulation method for the design of belt conveyors. [ABSTRACT FROM AUTHOR]

During transportation, bulk materials are susceptible to spillage due to equipment instability and environmental factors, resulting in increased maintenance costs and environmental pollution. Thus, intelligent and efficient condition monitoring is crucial for maintaining operational efficiency of transfer equipment. It facilitates the timely identification of potential safety hazards, preventing accidents from occurring or their impact from spreading, thereby minimizing production and maintenance costs. This study presents a deep-learning-based multioperation synchronous monitoring method suitable for belt conveyors that integrate target segmentation and detection networks to simultaneously diagnose belt deviation, measure conveying load, identify idlers, and do other tasks on a self-made dataset. This method effectively reduces the complexity of multistate simultaneous monitoring and monitoring costs, thereby avoiding environmental pollution caused by transportation accidents. Experimental results show that the segmentation accuracy of the proposed method can be up to 88.72%, with a detection accuracy of 91.3% and an overall inference speed of 90.9 frames per second. Furthermore, by extending the dataset, the proposed method can incorporate additional tasks, such as belt damage, scattered material, and foreign object identifications. This study has practical significance in ensuring the normal and eco-friendly operation of bulk material transportation. Our source dataset is available at https://github.com/zhangzhangzhang1618/dataset-for-turnning-section [ABSTRACT FROM AUTHOR]

In response to the problems of missing image details and poor fitting effect in multiple fractures or areas with large fracture spacing in existing machine vision based coal flow detection methods for belt conveyors, a high-precision coal flow detection system for belt conveyors based on machine vision is proposed. It is based on the principle of direct beam oblique collection laser triangulation. The line laser emitter is arranged directly above the measurement position of the belt conveyor and vertically irradiates the coal pile. The coal pile moves uniformly with the belt conveyor, and a camera at an oblique angle is used to capture real-time images of the surface of the coal pile containing laser stripes. The method calibrates the coal flow detection system, including camera internal parameter calibration and laser plane calibration, to obtain the height information of the coal pile. The processing of laser stripe images on coal flow cross-sections is carried out. The gray center of gravity method and regional skeleton method are compared and analyzed from multiple perspectives such as extraction precision and algorithm real-time performance. Based on the comparison results, the regional skeleton method is selected to extract the center of laser stripes. Aiming at the problem of poor fitting effect of laser stripe fracture repair using image dilation operation, the least squares method is proposed as the laser stripe fracture repair algorithm. Compared with closed operations, the least squares method has better smoothing effect and higher precision in fitting processing. The method establishes a coal flow cross-sectional area calculation model. By calculating the cross-sectional area of the coal pile at each frame, the coal flow volume at different belt speeds can be obtained. The experimental results show that when the belt speeds are 0.25, 0.5, and 1 m/s respectively, the detection system errors are relatively small, with maximum errors of 2.78%, 3.61%, and 3.89%. It verifies that the coal flow detection system has high accuracy.

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. 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 longitudinal tear of conveyor belts is the most common accident occurring at the workplace. Given the limitations on accuracy and stability of current single-modal approaches to detecting the longitudinal tear of conveyor belts, a solution is proposed in this paper through Audio-Visual Fusion. According to this method, a linear CCD camera is used to capture the images of the conveyor belt and a microphone array for the acquisition of sound signals from the operating belt conveyor. Then, the visual data is inputted into an improved Shufflenet_V2 network for classification, while the preprocessed sound signals are subjected to feature extraction and classification using a CNN-LSTM network. Finally, decision fusion is performed in line with Dempster-Shafer theory for image and sound classification. Experimental results show that the method proposed in this paper achieves an accuracy of 97% in tear detection, which is 1.2% and 2.8% higher compared to using images or sound alone, respectively. Apparently, the method proposed in this paper is effective in enhancing the performance of the existing detection methods. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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 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.)

To address the shortcomings of existing conveyor belt deviation detection methods, such as poor fault location accuracy, a low automation level and low reliability, a method that utilizes machine vision technology to detect belt deviations in belt conveyors is proposed. This method involves preprocessing operations on captured video images, including Region of Interest (ROI) extraction, grayscale processing, and noise reduction, thereby eliminating image noise and interference. To address the edge blurring due to Gaussian filtering and threshold setting issues in Canny detection, an enhanced edge detection technique using a guided filter and the Otsu method modifies the traditional Canny operator is introduced. Subsequent application of Hough Transform and least squares fitting processes delineate the edges of the conveyor belt and its rollers during operation. Utilizing the detected edges of the conveyor belt and rollers as references, a dual-baseline positioning method is for the first time proposed to quantify the deviation degree, facilitating the identification of deviation faults. After detection with the improved Canny algorithm, clearer contour binary images with fewer noise and impurities were obtained. Experiments conducted on images from various deviation scenarios yielded an average detection accuracy of 95.4% and a detection speed of 26 frames per second (FPS). This approach not only enhances the detection speed and accuracy but also reduces the frequency of conveyor belt failures and improves the operational efficiency of belt conveyors. [ABSTRACT FROM AUTHOR]

Belt conveyors often work in more complex environments, although many are indoors. However, due to the structure of the plant and other reasons, it is difficult to install the camera directly above the belt when using the camera to monitor the belt or identify faults. At this time, the staff can only find a location as far as possible can play a role in monitoring the belt conveyor to install the camera. In this case it is more difficult to analyze whether the belt is running away. On the other hand, the previous belt deflection detection method is through the artificial delineation of the standard position of the belt, the actual position of the belt and the standard position for comparison, which to a large extent there are human factors interference, resulting in the detection results are not precise enough. Therefore, in this paper, for the belt that can only monitor the type of unilateral rollers, we propose a method to recognize whether the belt is empty or not, and then determine the standard position of the belt through a multi-task learning model with joint target detection and semantic segmentation. This enables adaptive recognition of the standard position of the belt. The degree and direction of belt deflection is determined by obtaining the line connecting the straight line at the edge of the belt and the uppermost corner point of the rollers. The overall accuracy of the system is 94.9%, which is obtained by experimenting with the multi-task learning model and the belt runout judgment model respectively. It can be concluded that the belt runout detection method proposed in this paper can be a good solution to the situation where the camera installation position is not ideal. It reduces the failure rate of the belt conveyor and provides a guarantee for the normal operation of the belt conveyor. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow response speed, poor regulation, time-varying and nonlinear tensioning control of traditional belt conveyor tensioning system in mine, a Fuzzy-PID control (PID control based on fuzzy algorithm) method of hydraulic tensioning system of belt conveyor in mine is proposed. Firstly, a mathematical model is established according to the hydraulic tensioning device, and secondly, the hydraulic tensioning system with Fuzzy-PID controller and PID controller are simulated by the built-in Simulink simulation library of Matlab, respectively, to derive the start-up response stage of the conveyor belt tensioning force and the regulation response graph of the sudden change of tension, and to make a comparative analysis. Finally, the effectiveness of the algorithm model is verified by experimental tests. The simulation results show that the hydraulic belt conveyor tensioning Fuzzy-PID control system has better steady state performance and faster response speed not only in the start-up phase but also during the sudden change of tension. The overshoot of tension in the start-up response phase of the tensioning device is reduced by 13.5%, and the time to reach the desired value is shortened by 0.5 s. The tension change of the tensioning device is the simulated tensioning and loosening stage, when the tension is increased, the speed of regulation of the Fuzzy-PID controller is shortened by 0.4 s, and the overshoot is reduced by 4%. When the tension decreases, the regulation speed of the Fuzzy-PID controller is shortened by 0.3 s and the overshoot is reduced by 2%. The experimental results show that the effect of using Fuzzy-PID control is better and more stable with less loss. Compared with PID control, Fuzzy-PID control is more effective, the average time is reduced by 31% and the overall tendency is stable. For the mine belt conveyor such as continuous transportation equipment, Fuzzy-PID control technology for the mine belt conveyor smooth operation provides a certain guarantee, not only reduces the waste of electricity, but also reduces the maintenance cost of maintenance belt conveyor.

Real-time coal flow monitoring is crucial for efficient coal mine transportation. Traditional vision acquisition devices generate two-dimensional images that are not accurately identified in the complex underground coal mine environment, such as dust, water mist, and low light. In this paper, we propose a conveyor belt coal flow detection method that integrates laser scanning and binocular vision to address this problem. Our proposed method has several advantages over traditional approaches. Firstly, we calibrate the binocular camera using Zhang’s calibration method to enhance the accuracy of the system. Secondly, we extract the centerline of the laser stripe using the grayscale center of gravity method, which improves the system’s performance in complex environments. Thirdly, we calculate the cross-sectional area of the material accurately using the trapezoidal area accumulation method, and visualize it in two dimensions based on a single frame, while the point cloud data from multiple consecutive frames are visualized in a 3D model at a realistic scale. Finally, we use the continuous multi-frame cross-sectional area to calculate the current conveyor flow, and apply the BP neural network to establish an energy-saving optimization model for the belt conveyor. We also design a PLC fuzzy controller based on fuzzy control algorithms to adjust the belt’s operating speed intelligently according to the coal flow size, achieving energy-saving operation and intelligent control of the belt conveyor. Our experiments show that our method can accurately obtain coal volume and control the belt speed of the conveyor in real-time, making it an innovative and practical solution for coal flow detection and energy-saving operation in underground coal mines.

This paper examines the working process of a belt conveyor with a working drive that can change the length of transportation. The conveyor can be used for tunneling, development of minerals in mines and quarries, transportation of materials in warehouses. The use of such a conveyor makes it possible to reduce the time for operations to increase or decrease the length of transportation, to exclude reloaders between the working equipment and conveyor itself from the transport chain. It was established that when changing the length of transportation of a working conveyor, the static and dynamic load on the belt increases. The change in the static load of the belt on the drum of the mobile station depends on the speed of the mobile station and the speed of the beltgenerated by the conveyor drive. The dynamic loading on the belt depends on the acceleration of the belt, which is related to the acceleration of the mobile station during the change in the length of the conveyor. For a working conveyor that changes the length of transportation, the static tension of the belt on the drum of the mobile station can increase by 1.1-1.4 times from the initial one. The dynamic loading of the belt can have a significant increase if the acceleration of the mobile station is not stretched over time and acquires large values. Based on the dependencesderived in the current work, a calculation procedure is proposed for the design of a belt conveyor with a working drive that can change the length of transportation. The Mathcad software was applied to verify the designcalculation procedure. The results make it possible to employ new design methods in the construction of competitive machines equipped with a belt conveyor with a variable length of transportation [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering 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.)

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.)

Conveyors play a very important role in modern manufacturing processes, and one of the most popular types is the belt conveyor. The main elements of a conveyor include a conveyor belt, roller sets, a supporting frame and a drive and control system. The reliable operation of the conveyor depends on the strength and durability of individual elements (especially the belt). Conveyor belts are made from various materials and have received a lot of attention in the scientific and research community. This article presents tests of the strength of the rubber belt material and its damage under load. The belt consists of two internal layers covered with a PVC coating on the outside, and the nominal belt thickness was 2 mm. In the experiment, various configurations of longitudinal and transverse damage were verified, and statistical methods were used to analyze the results. The obtained test results provided a new understanding of the propagation of conveyor belt damage and helped to improve the strain gauge-based monitoring system. [ABSTRACT FROM AUTHOR]

The optimization problem is formulated for the cross-section outline of bulkload on a belt conveyor and for the kinematic parameters of the latter. The analytical solution on the optimum angle of side rollers and cross-section outline of bulkload on the belt is presented. The influence of the friction angle on the optimum cross-section outline of bulkload on the belt is illustrated. The optimization of velocity and acceleration of belt conveyor in the start period is described. The influence of the angle of the side rollers on the rolling friction is determined. [ABSTRACT FROM AUTHOR]

Existing idler fault diagnosis methods have problems in failing to fully obtain global context information and providing poor diagnostic accuracy. To address these problems, this paper investigates a new method for diagnosing faults in belt conveyor idlers, based on analysis of their acoustic signals. The method is also applied to existing databases of bearing fault data. Firstly, an eight-element microphone array sound signal collector is designed to suppress environmental noise and raise the signal-to-noise ratio of the idler sound signal. Secondly, a multi-scale feature fusion (MSFF) module is constructed to learn complementary information between features at different scales. Then, a residual mask convolutional attention (MCA) module is designed to raise the modelling capability of local features and global contextual information. Finally, the structure of the ResNet-18 network is optimised to improve model fitting performance. Experimental results on self-made and public datasets show that the suggested method outperforms other comparative methods, achieving real-time accurate detection and classification of belt conveyor idler faults and typical bearing faults. [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.)

This article deals with the development of small-sized wind power equipment as a viable solution for decentralized renewable energy production. To improve operational specifications of conventional turbine models with rotating blades, it is proposed to use a new design of wind power plant synthesized on the base of a closed loop conveyor equipped with flatshaped blades. In this design, blades are mounted on a belt with an opportunity to move together with it in one straight line direction. Air flow interaction with flat blade that performs translation motion is studied by computer simulation using a superposition principle. In accordance with this approach, a fast-chaotic motion of air particles (Brownian motion) is separated from the slow-directed air motion, with the given average velocity. Dynamic analysis of flat blade interaction with air flow is performed for the stationary air flow with constant speed and also for non-stationary flows with wind gusts. Optimization of the system parameters is made using the generated power as a criterion. Simulation results confirm the serviceability and efficient operation of the proposed conveyor type wind power equipment. It can be mounted on the roofs of buildings or rooftops of vehicles, also device is befriended to nature and people. [ABSTRACT FROM AUTHOR]

Relevance. It is justified by the necessity to confirm the operability of the algorithm of accurate movement of the selected wood sorting to the corresponding storage bunker in the technological process of roundwood sorting at winter warehouses of timber processing companies to solve scientific and engineering problems both at present and in the future. Aim. To investigate the process of moving piece load on the physical model of longitudinal chain or belt conveyor, providing scaled reduction of positioning error. When sorting roundwood on chain longitudinal conveyors of considerable length, there is an error of movement of the selected roundwood sort to the place of discharge into the accumulator, which increases in proportion to the length of the sorting line. At present, conveyors of limited length are used. The accumulators are located on both sides of the conveyor. This reduces the transport error. Sometimes the sorting yard does not allow placing a short conveyor on it or there is a considerable amount of assortments. In this case, it is necessary to use a conveyor of considerable length. The paper proposes a new method of sorting roundwood on longitudinal chain conveyors of considerable length. This reduces the tracking error of the movement of the assortment. Optoelectronic sensors of angular displacements of the leading and driven shafts of the belt conveyor are used as sensors of angular displacements of the shaft. Methods. Physical modelling, information-static processing methods. Results and conclusions. The authors have carried out the analysis of the structure of the physical similarity stand of the longitudinal conveyor; sensors of the technological process, namely, angular frequency of rotation; software and hardware means of processing the obtained information to minimize the error of transporting piece cargo on the conveyor belt. The results of the study confirm the possibility of practical application of the device for operation on longitudinal conveyors.

A modification of the design method for dump belt conveyors of mobile construction and road machines is proposed, based on the introduction into its structure of refined recommendations for choosing the coefficient of resistance to the movement of the belt, developed on the basis of experimental studies of the heating process of conveyor elements from the moment of its start to exit to operating temperature. Using the example of a dump conveyor of a mobile washing plant, patterns of operation of short conveyors have been identified, which make it possible to increase the efficiency of their design process, as well as to obtain a positive economic result from the use of the proposed modification.

Foreign objects mixed in during coal mining may cause accidents such as blockage or even tearing of conveyor belt connections. Most existing machine learning algorithms for coal flow foreign objects use supervised learning to automatically recoginze item categories. However, in real industrial and mining scenarios, the scarcity of abnormal samples leads to problems of serious imbalanced sample distribution and significant features lost in the modeling dataset. In order to solve the above problems, a coal flow foreign object intelligent detection method based on dual-attention Skip-GANomaly (DA-GANomaly) is proposed. This method adopts a semi supervised learning approach, which only requires normal samples to complete the training of the foreign object detection model, effectively solving the problems of low recognition accuracy and poor robustness caused by imbalanced sample distribution. On the basis of Skip-GANomaly, a dual attention mechanism is introduced to enhance the information exchange between the encoder and decoder and suppress irrelevant features and noise. It highlights the interesting features that are conducive to distinguishing abnormal samples, and further improves the accuracy of model classification. The experimental results show that the classification accuracy of the DA-GANomaly model is 79.5%, the recall rate is 83.2%, and the area under the precision recall curve (AUPRC) is 85.1%. Compared with 5 classic anomaly detection models such as AnoGAN, the DA-GANomaly model has the best overall performance.

The images of coal mine underground belt transportation have the features of low illumination, unclear details, and background interference. The existing foreign object detection models for belt conveyors have problems such as low precision, poor flexibility, large computational complexity, and differences in optimization space. In order to solve the above problems, a foreign object detection method for belt conveyors based on generative adversarial nets (GAN) is proposed. The method preprocesses the video files of the tape transportation process, classifies them into normal and abnormal images. The method creats an experimental dataset to train the improved GANomaly model, and then uses the trained model to detect foreign objects in the belt conveyor. During the training phase, the image of the belt conveyor without foreign objects is used as input. In the testing phase, the image of the belt conveyor containing foreign objects is used as input. The reconstructed image obtained is subtracted from the original image of the input network to obtain the specific position of the foreign object. The lightweight improvement method of GANomaly model adds a depthwise separable convolution residual module to the GANomaly basic network model, and uses depthwise separable convolution to replace the convolution operation in the original backbone network. It greatly reduces the computational complexity of the model and reduces the redundant calculation of parameters, which can significantly improve the speed of foreign object detection. By merging multiple batch normalization (BN) layers, the convergence iteration speed of the model is accelerated, the generalization convergence capability of the model is improved, and gradient vanishing is effectively avoided. The experimental results show that the improved GANomaly model has improved the running speed by 6.27% compared to the traditional GANomaly model. The evaluation indicators F1 score, AUC, Recall, and mean average precision (mAP) have increased by 19.05%, 22.22%, 15.00%, and 17.14%, respectively.

Currently, the inspection robot for belt conveyors equipped with infrared acquisition devices is limited in movement. There are problems such as inability to collect data, process data, upload data to monitoring terminals in real-time and complete remote fault detection, insufficient endurance and so on. A fault detection method of belt conveyor roller based on thermal infrared images has been proposed. The belt conveyor inspection robot is equipped with a roller fault detector and an infrared thermal imager. The infrared thermal imager transmits the collected roller thermal infrared image sequence and temperature data to the roller fault detector for roller fault detection. The WH-L101 wireless transmission module in the roller fault detector is used to send the detection results to the upper computer. A belt conveyor roller fault detection algorithm is proposed. The algorithm uses the YOLOv5s object detection algorithm to extract the region of interest (ROI) of the roller thermal infrared image. The image of the ROI is filtered using Wiener filtering and adaptive median filtering algorithms. The filtered ROI image is enhanced by using adaptive histogram equalization and image sharpening algorithms. The Otsu image segmentation algorithm based on morphology is used to segment the enhanced ROI image, obtaining the roller image to be detected. The Harris corner detection algorithm is used to extract the features of the roller image, and obtain the position information of the roller. The temperature information of the corresponding position is extracted, and a roller fault detection algorithm based on the relative temperature difference method is used to determine the idler fault. The experimental results show: ① The average accuracy of object detection in the roller ROI extracted by YOLOv5s network model is 99.12%. ② The proposed roller fault detection algorithm has an average accuracy of 97.625% and a frame rate of 16 frames per second for detecting roller faults (no faults, bearing rust, roller jamming, and cylinder wear). ③ The detection results are transmitted to the upper computer through a wireless transmission module, which can display the fault type and key area temperature, and provide an alarm.

The efficient monitoring of the belt deviation state will help to reduce unnecessary abnormal wear and the risk of belt tear. This paper proposes a coupling characterization method involving the prediction box features of the target detection network and the linear features of the conveyor belt edge to achieve the quantitative monitoring of conveyor belt deviations. The impacts of the type, location, and number of attention mechanisms on the detection effect are fully discussed. Compared with traditional image-processing-based methods, the proposed method is more efficient, eliminating the tedious process of threshold setting and improving the detection efficiency. In detail, the improved practice and tests are carried out based on the Yolov5 network, and the Grad-CAM technique is also used to explore the effect of attention mechanisms in improving the detection accuracy. The experiments show that the detection accuracy of the proposed method can reach 99%, with a detection speed of 67.7 FPS on a self-made dataset. It is also proven to have a good anti-interference ability and can effectively resist the influence of the conveying material flow, lighting conditions, and other factors on the detection accuracy. This research is of great significance in improving the intelligent operation and maintenance level of belt conveyors and ensuring their safe operation.

Foreign objects in coal flow easily cause damage to conveyor belts, and most foreign objects are often occluded, making them difficult to detect. Aiming at solving the problems of low accuracy and efficiency in the detection of occluded targets in a low-illumination and dust fog environment, an image detection method for foreign objects is proposed. Firstly, YOLOv5s back-end processing is optimized by soft non-maximum suppression to reduce the influence of dense objects. Secondly, SimOTA label allocation is used to reduce the influence of ambiguous samples under dense occlusion. Then, Slide Loss is used to excavate difficult samples, and Inner–SIoU is used to optimize the bounding box regression loss. Finally, Group–Taylor pruning is used to compress the model. The experimental results show that the proposed method has only 4.20 × 105 parameters, a computational amount of 1.00 × 109, a model size of 1.20 MB, and an mAP0.5 of up to 91.30% on the self-built dataset. The detection speed on the different computing devices is as high as 66.31, 41.90, and 33.03 FPS. This proves that the proposed method achieves fast and high-accuracy detection of multi-layer occluded coal flow foreign objects.

Topology optimization is a well known and sophisticated method for designing structures. Through a finite element analysis, this method optimizes the design and material distribution to obtain an ideal strength-to-weight ratio and improved strain-to-weight ratio. This study involves the development of a comprehensive model for a brake using the ANSYS Parametric Design Language. The purpose of the model is to accurately characterize the geometry of the disc or drum. The technique of a complex eigenvalue analysis is used to identify the presence of unstable modes occurring at distinct frequencies, indicating instability. A braking force of 17,492 kN was exerted at a rotational velocity of 55 rad/s for 10 s. The optimization process resulted in significant mass reduction while maintaining structural integrity. In the drum brake, the mass was reduced from 114.01 kg to 104.07 kg, while the disc brake’s mass decreased from 68.81 kg to 56.68 kg.

The belt conveyor is a key piece of equipment for thermal power plants. Belt mistracking causes higher economic costs, lower production efficiency, and more safety accidents. The existing belt correction devices suffer from poor performance and high costs. Therefore, a design method for coal conveying belt correction devices is proposed in this paper based on fault tree analysis (FTA), quality function deployment (QFD), and inventive problem solving (TRIZ) theory. A user requirement mapping model was constructed to extract five user requirements based on the shortcomings of the existing belt correction devices. Moreover, the FTA model of product characteristics was established to obtain nine key product characteristics by dividing the minimum cut-set. Thus, a QFD model for the belt correction device design was constructed. The relationship matrix R–C relating the user requirements and the product characteristics was deduced based on the binary decision diagram theory to screen the key product characteristics. Furthermore, an innovative design model of the belt correction device was constructed, and the key design elements and contradiction matrix were determined. TRIZ theory was used to resolve the contradictions, and nine novel correction device design schemes were proposed. Finally, the correction performances of the schemes were compared and verified based on the experimental platform, and the final design scheme was determined. The test results showed that the device had good correction performances. The rationality and effectiveness of the innovative design method were verified. [ABSTRACT FROM AUTHOR]

Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining. [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.)

Aiming at the problem that the sound signal of gearbox bearing of belt conveyor was seriously disturbed by reverberation and running noise of other components, which led to the difficulty of acoustic diagnosis, the composition of acoustic signal, the cause of reverberation, the signal transmission path and the characteristics of each interference component were analyzed, and an acoustic signal feature enhancement method combining Kalman filter and optimal sub-band selection was proposed. Firstly, the Kalman filter hyperparameters were set according to the maximum kurtosis criterion, and Kalman filtering was used to reduce the interference of the reverberant noise to the fault acoustic signal of the bearing. Then, the wavelet packet noise reduction algorithm was used to process the signal after de-reverberation, the energy difference was compared between the sub-bands of the signal in the state to be measured and the normal state, the optimal sub-band containing the most fault information was selected, and the bearing fault features were extracted by the envelope spectrum analysis. Finally, the effectiveness of the proposed method was verified by bearing fault simulation tests and compared with the combination of improving singular value decomposition (ISVD) and resonance-based sparse signal decomposition (RSSD). The results of the study show that the proposed method is effective in de-reverberation and noise reduction, and the envelope spectrum contains obvious fault features and their associated components. The proposed method can realize the enhancement of bearing acoustic signal in indoor measurement environment and accurately extract the bearing fault characteristics. [ABSTRACT FROM AUTHOR]

Both rule-based reasoning (RBR) and case-based reasoning (CBR) methods are applicable to the field of belt conveyor fault diagnosis. Aiming at the problem that both of them focus on cause analysis or maintenance means in fault diagnosis, the reasoning mechanism based on RBR+CBR dual reasoning was studied, and an expert system was established to diagnose the fault of belt conveyor drive system. Firstly, the expert system for fault diagnosis was constructed on the basis of the structure of traditional expert system. Then, the fault tree of the belt conveyor drive system was established according to the fault mechanism, the expert system fault rule base was designed by using the generative representation method, and according to the fault cases and expert experience, the expert system fault case base was designed by using the matrix representation method. The expert system reasoning mechanism was designed based on the rule-based reasoning (RBR) and case-based reasoning (CBR) methods. And the human-computer interaction interface of the expert system was designed by using the LabVIEW program development platform, which realized the series communication process between the sensor, knowledge base and client. Finally, in order to verify the feasibility of the above expert system, experiments were conducted on a belt conveyor operating site in a certain mine, and the results were analyzed. The results of the study show that, the fault cause analysis accuracy of the belt conveyor expert system exceeds 95%, and the effectiveness of the fault repair plan of the expert system exceeds 94%. In comparison with the expert system supported by RBR and CBR algorithms, this expert system has better convergence, effectiveness and accuracy, and it proposes an effective method for the intelligent fault diagnosis of the belt conveyor. [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 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.)

As a crucial element in coal transportation, conveyor belts play a vital role, and monitoring their health is essential for the coal mine transportation system's safe and efficient operation. This paper introduces a new 'hardware-friendly' method for monitoring belt conveyor damage, aiming to address the issue of large parameters and computational requirements in existing deep learning-based foreign object detection methods and their challenges in deploying on edge devices with limited computing power. This method is tailored towards edge computing and aims to reduce the parameters and computational load of foreign object recognition networks deployed on edge computing devices. This method improves the YOLOv8 object detection network and redesigns a novel lightweight ShuffleNetV2 network as the backbone network, making the network more delicate in recognizing foreign object features while reducing redundant parameters. Additionally, a simple parameter-free attention mechanism called SimAM is introduced to further enhance recognition efficiency without imposing additional computational burden. Experimental results demonstrate that the improved foreign object recognition method achieves a detection accuracy of 95.6% with only 1.6 M parameters and 4.7 G model computational load (FLOPs). Compared to the baseline YOLOv8n, the detection accuracy has improved by 3.3 percentage points, while the number of parameters and model computational load have been reduced by 48.4% and 42.0%, respectively. These works are more friendly to edge computing devices that tend to "hardware friendly" algorithms. The improved algorithm can reduce latency in the data transmission process, enabling the accurate and timely detection of non-coal foreign objects on the conveyor belt. This provides assurance for the subsequent host computer system to promptly identify and address foreign objects, thereby ensuring the safety and efficiency of the belt conveyor. [ABSTRACT FROM AUTHOR]

The paper presents the research on revealing the causes of belt conveyors downtime at open-pit mining operations. By results of researches it is established that the reasons of the above mentioned problems are technological dust formed at crushing of mineral ore, further loading and unloading, and also at transporting by conveyors. Dense dust-air mixture settling on the rotating mechanisms of conveyors, gets into the bearing units and on the surfaces of friction gears, which accelerates the process of wear of parts several times. The problem of determining the dependence of the wear of the working surface of roller bearings on the intensity of accumulation of process dust deposited on the parts and units of conveying machines has been set and solved. The analysis has shown that one of the methods of protection of parts of conveyor equipment as a whole is removal of aggressive medium. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor accuracy, real-time and reliability of coal pile detection at the belt conveyor drop port,a machine vision-based coal pile detection method for the belt conveyor drop port is proposed. An industrial camera is used to capture the image of the drop port and transmit it to the coal pile detector through Ethernet,and the heap coal detector processes and analyzes the image to achieve coal pile detection by using the coal pile detection algorithm,and transmits the detection results to the host computer via Ethernet;the ShuffleNet V2 network model is improved by replacing the standard convolution with the null convolution in the model,which increases the perceptual field without increasing the computation,and the efficient channel attention module ECA is added to the basic unit to improve the feature extraction capability;the heap coal detection algorithm with improved ShuffleNet V2 network model is proposed,and the image dehazing enhancement algorithm of the dark channel is used to dehaze and enhance the collected images,the image data set of drop port is constructed.The improved ShuffleNet V2 network model is used to train the improved ShuffleNet V2 network model,and then the trained network model is used for coal pile detection.The heap coal detector hardware and software were designed using a Cortex-A57 architecture 4-core Jetson Nano development board.The experimental results show that the method can achieve real-time detection of heap coal at the belt conveyor dropout with an accuracy of 98.34% and an image processing speed of 23 frames/second. [ABSTRACT FROM AUTHOR]

The task of ensuring smooth movement of the conveyor belt during the operation of powerful belt conveyors with a long route and several intermediate electric drives is an urgent technical problem. One of the effective ways to solve this problem is to create drives that ensure the joint operation of an electric motor and a hydraulic coupling. In this article, a mathematical model of the joint operation of an electric motor and a hydraulic coupling is proposed. When constructing the mechanical characteristics of their joint work, possible deviations of the electric motor slip from the nominal values are taken into account. To simulate various possible approximating functions that describe the mechanical characteristics of the drive, two of the most accurate mathematical expressions that are used are the Kloss equation and the equation for interpolating hyperbolic functions. With regard to the electric drive of a powerful belt conveyor, the analysis of the appropriate areas of application of these dependencies is carried out, their main advantages and disadvantages are considered. [ABSTRACT FROM AUTHOR]