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Aiming to address the problems of nonlinearity, a large time delay, poor adjustment ability, and a difficult parameter setting process of the tension control system of belt conveyor tensioning devices, an adaptive Proportional-Integral-Derivative (PID) parameter self-tuning algorithm based on an improved seeker optimization algorithm (ISOA) is proposed in this paper. The algorithm uses inertia weight random mutation to determine step size. An improved boundary reflection strategy avoids the defect of a large number of out-of-bound individuals gathering on the boundary in a traditional algorithm, and projects the individual reflection beyond the boundary into the boundary, which increases the diversity of the population and improves the convergence accuracy of the algorithm. To improve the system response speed and suppress the overshoot problem of the control target, coefficients related to the proportional term are introduced into the fitness function to accelerate the convergence of the algorithm. The improved algorithm is tested on three test functions such as Sphere and compared with other classical algorithms, which verify that the proposed algorithm is better in accuracy and stability. Finally, the interference and tracking performance of the ISOA-PID controller are verified in industrial experiments, which show that the PID controller optimized using the ISOA has good control quality and robustness. [ABSTRACT FROM AUTHOR]

To address the problem of energy wastage in traditional long-distance belt conveyors with low carrying capacity due to the excessive power installation of a single drive motor, a model of a distributed permanent magnet direct drive belt conveyor driven by multiple small power motors is proposed. A power regulator based on material carrying capacity is designed for energy saving. The minimum number of motors is set as the adjustment target. The number of running motors is adjusted, and the remaining motor power is distributed. The speed constraints of multiple motors are analyzed. The speed difference between adjacent motors is used as feedback to construct a ring coupling speed controller. This realizes the coordinated operation of the distributed permanent magnet direct drive belt conveyor. Co-simulations with AMESIM (version 2020.1) and MATLAB (version 2019b) are conducted to verify the system's stability. The power regulator optimizes motor distribution under varying conditions, reducing operational losses. The experimental results confirm the effectiveness of the proposed power control strategy. [ABSTRACT FROM AUTHOR]

The expert assessment method underlies most decision-making methods in the technical field of activity, from the search for rational design solutions to issues of industrial safety of hazardous production facilities, which is embodied in the form of regulatory documents governing the implementation of a risk-oriented approach to problem solving in the specified areas. The subjectivity of the expert assessment method can often lead to decisions that are objectively not rational in a specific design situation. This article considers a modification of the superstructure of the criteria weight assessment level of the hierarchy analysis method based on statistical processing of a set of possible combinations of criteria priorities using the example of choosing a rational option for a belt conveyor drive system, which is a direct replacement for the expert assessment method and paired comparisons at this hierarchy level. It is shown that when taking into account the proposed modification and paired comparison at the first level of the hierarchy of clearly defined values of options within each criterion (characteristics of technical objects), the method of paired comparisons is excluded from the structure of the methodological apparatus of the hierarchy analysis method, which makes it possible to single out the obtained set of results into a separate decision-making method.

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]

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 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]

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]

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]

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]

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.

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.

To address the problem of energy wastage in traditional long-distance belt conveyors with low carrying capacity due to the excessive power installation of a single drive motor, a model of a distributed permanent magnet direct drive belt conveyor driven by multiple small power motors is proposed. A power regulator based on material carrying capacity is designed for energy saving. The minimum number of motors is set as the adjustment target. The number of running motors is adjusted, and the remaining motor power is distributed. The speed constraints of multiple motors are analyzed. The speed difference between adjacent motors is used as feedback to construct a ring coupling speed controller. This realizes the coordinated operation of the distributed permanent magnet direct drive belt conveyor. Co-simulations with AMESIM (version 2020.1) and MATLAB (version 2019b) are conducted to verify the system’s stability. The power regulator optimizes motor distribution under varying conditions, reducing operational losses. The experimental results confirm the effectiveness of the proposed power control strategy.

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]

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]

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]

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.

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]

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]

As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine's intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms. [ABSTRACT FROM AUTHOR]

As one of the important transportation tools for coal transportation, the operating efficiency of belt conveyor is directly related to the production efficiency of coal. However, belt conveyor failures often occur due to belt deviation. Generally speaking, the transportation distance of belt conveyors is relatively long, and the transportation environment is relatively harsh. The use of manual monitoring of the operation status of belt conveyors is inefficient and increases labor costs. The existing belt deviation detection system can play a good role in some conventional environments. However, under complex working conditions such as heavy dust, insufficient light, and camera deviation, existing detection systems will be affected. Therefore, this paper proposes a belt deviation detection system based on deep learning under complex working conditions. The characteristics of the belt and idler are extracted through the convolutional neural network, so as to position the belt and idler. With the characteristic points on the belt and idler as input, a mathematical model of belt deviation is established to calculate the degree of belt deviation, and alarms according to different degrees of deviation. The system has been tested in the actual field, and has a certain accuracy and real-time, which largely solves the problem of belt deviation detection under complex working conditions, reduces the fault frequency of belt conveyor, and thus improves the working efficiency of belt conveyor. [ABSTRACT FROM AUTHOR]

Belt conveyor adalah salah satu media pengangkutan yang digunakan untuk memindahkan muatan dalam bentuk satuan atau tumpahan curah dari satu sistem operasi yang satu ke sistem yang lain. Di PT Solusi Bangun Indonesia, salah satu belt conveyor digunakan untuk mengangkut dua jenis material secara bergantian yaitu fly ash&bottom ash (FABA) dan silika. Hal itu menyebabkan pengisian bin tidak konsisten ketika kebutuhan material silika dan FABA sedang tinggi pada waktu bersamaan. Selain itu, seringkali terjadi permasalahan pada sistem penggerak belt conveyor yang mengangkut FABA seperti motor trip, chain putus, dan sprocket aus sehingga menyebabkan pengangkutan FABA menjadi terhambat. Oleh karena itu, perlu dilakukan modifikasi pada sistem penggerak belt conveyor agar transport FABA menjadi lebih lancar. Metode diawali dengan pengambilan data di lapangan atau informasi dari pembimbing, studi literatur, melakukan proses perancangan hingga evaluasi hasil. Modifikasi yang dilakukan yaitu dengan penggantian gearmotor dengan daya sebesar 11 kW dengan output putaran 24 rpm, mengganti sistem transmisi dari chain-sprocket menjadi kopling, serta memperbesar diameter head pulley dari 478 mm menjadi 560 mm untuk meningkatkan kapasitas transport belt conveyor menjadi 125 ton per jam.

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.

In order to study the influence of temperature on the indentation rolling resistance of belt conveyor, theoretical analysis, numerical simulation analysis and experimental study of conveyor belt indentation rolling resistance with temperature characteristics were carried out in this paper, and the influence rules of different factors on the indentation rolling resistance of belt conveyor were obtained. First, the three-component Maxwell model is chosen as the viscoelastic model for the conveyor belt rubber material, and the viscoelastic modulus function based on temperature effects is constructed from the DMA experimental data fitting. Second, we introduce the one-dimensional Winkler foundation model to derive a mathematical expression for the indentation rolling resistance based on temperature properties. Then, a mathematical model of the indentation rolling resistance of a conveyor belt with temperature characteristics is developed in MATLAB and numerical simulations are performed. Finally, using the existing experimental equipment to conduct experiments, the experimental results are compared with the numerical simulation result. The results show that the theoretical numerical simulation results of the indentation rolling resistance with temperature effect presented in this paper have a consistent change trend with the experimental results; at constant temperature and constant load, the indentation rolling resistance increases with increasing band velocity; at constant temperature and constant velocity, the indentation rolling resistance increases with increasing load; at constant load and speed, when the temperature is lower than 0 °C or higher than 25 °C, the rolling resistance increases with the increase of temperature, when the temperature is between 0 °C and 25 °C, the rolling resistance decreases gradually with the increase of temperature. [ABSTRACT FROM AUTHOR]

An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and images acquired with low quality, recursive filtering and MSRCR image enhancement algorithms were utilized to preprocess the dynamic images collected by underground monitoring devices, substantially enhancing image quality. The YOLOv5 model has been improved by introducing a multi-scale attention module (MSAM) during the channel map slicing, thereby increasing the model's resistance to interference from redundant image features. Deep separable convolution was utilized in place of conventional convolution to detect, identify, and process large foreign objects on the belt conveyor as well as to increase detection speed. The MSAM-YOLOv5 model was trained before being installed on the NVIDIA Jetson Xavier NX platform and utilized to identify videos gathered from the coal mine belt conveyor. According to the experimental findings, the upgraded MSAM-YOLOv5 model has a greater recognition accuracy than YOLOv5L, with an average recall rate for different foreign objects of 96.27%, an average detection accuracy of 97.35%, and a recognition speed of 44 frames/s. The algorithm assures detection accuracy while increasing detection speed, satisfying the requirements for large foreign object detection on belt conveyors in coal mines. [ABSTRACT FROM AUTHOR]

Coal flow in belt conveyors is often mixed with foreign objects, such as anchor rods, angle irons, wooden bars, gangue, and large coal chunks, leading to belt tearing, blockages at transfer points, or even belt breakage. Fast and effective detection of these foreign objects is vital to ensure belt conveyors' safe and smooth operation. This paper proposes an improved YOLOv5-based method for rapid and low-parameter detection and recognition of non-coal foreign objects. Firstly, a new dataset containing foreign objects on conveyor belts is established for training and testing. Considering the high-speed operation of belt conveyors and the increased demands for inspection robot data collection frequency and real-time algorithm processing, this study employs a dark channel dehazing method to preprocess the raw data collected by the inspection robot in harsh mining environments, thus enhancing image clarity. Subsequently, improvements are made to the backbone and neck of YOLOv5 to achieve a deep lightweight object detection network that ensures detection speed and accuracy. The experimental results demonstrate that the improved model achieves a detection accuracy of 94.9% on the proposed foreign object dataset. Compared to YOLOv5s, the model parameters, inference time, and computational load are reduced by 43.1%, 54.1%, and 43.6%, respectively, while the detection accuracy is improved by 2.5%. These findings are significant for enhancing the detection speed of foreign object recognition and facilitating its application in edge computing devices, thus ensuring belt conveyors' safe and efficient operation. [ABSTRACT FROM AUTHOR]

Belt conveyors are critical in coal transportation systems; however, they are supported by rollers, which incur high energy costs. Magnetic levitation support is adopted. The measurement of a magnetic levitation air gap is important, and a displacement sensor is usually used in real-time. To address the problems of poor linearity and low measurement stability of single displacement sensors in magnetic levitation systems, a magnetic levitation support system based on multi-sensor data fusion is proposed. First, an industrial camera sensor is used to collect images, and the suspended air gap is obtained through image processing. Second, using an extended Kalman filter, the photoelectric position sensor and industrial camera sensor are fused to the suspension air gap value in the process of suspension. The electromagnet current is controlled by the PID (proportion integration differentiation) control algorithm, and experiments are carried out. Experimental results showed that, compared with an estimation of the suspension state of two sensors before and after EKF fusion, the root mean square error is reduced by 0.0597 and 0.0081, respectively, compared with the measurement value of a single sensor. Moreover, the fusion data were more robust, thereby meeting the requirements of magnetic levitation control. [ABSTRACT FROM AUTHOR]

Together with the research and development of belt conveyors, diagnostic methods for their reliable operation have been developed. Our research is focused on the development of a diagnostic system and method of sensing the magnetic markers implemented directly into the conveyor belt, as in this way, it is possible to determine the velocity of the conveyor belt, which can be a source of information about the potential malfunction of the drive unit, control system, or of the belt itself. The magnetic markers can be also used to mark the particular segments of the conveyor belt, which can significantly enhance maintenance procedures thanks to the identification of damaged segments and the possibility to stop the conveyor belt in the maintenance area. However, problems can occur with the implementation of the magnetic markers. To prevent the magnetic markers from falling out, FEM (finite element method) simulations and analyses of commercially available magnetic markers with different dimensions were performed to analyze the nominal and friction force and related average contact pressure acting on the magnetic markers implemented into holes drilled into the conveyor belt in the curved state when passing through the drum. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of complex mechanical transmission structure and low efficiency of the existing belt conveyor driven by asynchronous motor, this paper focuses on the investigation of a new type of low-speed high-torque-density direct-drive permanent magnet synchronous motor (LHDPMSM) for driving the belt conveyor. The proposed LHDPMSM utilizes external rotor structure to achieve the goal of fully direct drive of the belt conveyor. First, the topology and advantages of the straight-shape external-rotor LHDPMSM is introduced. Then the selection rules of the pole-slot combination are studied. Furthermore, an improved subdomain model is established based on the idea of segmentation for fast calculation of the no-load magnetic field of the proposed straight-shape external-rotor LHDPMSM. Moreover, the operating performance of the proposed LHDPMSM with load fluctuations was verified by FEA. In addition, an efficient cooling system based on $Z$ -shape water channels and hollow shaft was designed, and a LPTN model was established for temperature fast calculation. Since the cooling system has a negative impact on the shaft mechanical strength, the mechanical strength of the shaft and other fragile components of the LHDPMSM was analyzed to ensure the safe operation of the motor. Finally, a prototype is manufactured and tested to validate the design concept introduced in this paper.

A method for detecting coal flow on a belt conveyor based on the two-dimensional laser is proposed in this paper to tackle the complications of poor accuracy and real-time detection of coal flow on a belt conveyor. This method collects the point cloud data of the conveyor belt surface and the lateral profile of the coal flow using a two-position laser probe and transmits the data with the industrial Internet to assure the real-time and reliability of the data transmission. The upper computer software of the detection system is designed using LabVIEW software. The improved wavelet threshold algorithm designed by MATLAB is employed for data preprocessing to enhance measurement accuracy. The results demonstrate that the method can accurately detect the coal flow of the belt conveyor, and the detection accuracy reaches more than 95%, presenting the advantages of real-time performance and high accuracy. It has broad application prospects in coal, mines, ports, electric power, and other fields.

The management of in-mine ore transportation systems is a crucial challenge due to the constant development of underground mines. The transport network's stability and efficiency directly affect enterprise profits and growth. Managing key areas such as transportation asset maintenance, object control, and ore extraction planning is essential to ensuring high transportation standards. For larger, multi-site enterprises, transportation supervision is divided among many managers. To address short-term production planning and management support during unplanned events, a decision support system is proposed in this article to meet the expectations of both lower and higher-level managers. The proposed concept is based on the simulation deployed from real-time operational data from mine, in which an agent is placed and taught through reinforcement learning. The simulation as well as agent possible actions have a block structure, where each means of ore transport is a separate add-on. In addition, it is assumed that the solution will be further improved by the implementation of various algorithms found in the literature (e.g. early fault detection algorithms). When created and implemented, such a system will bring improvements in multiple levels of operation, such as the safety of employees, overall machine health, cost reduction, energy efficiency, etc. As a result of this work, a conception of an intelligent decision-support system is presented, along with the specification of areas of its influence and a description of tasks and algorithms proposed to be incorporated within the system itself. [ABSTRACT FROM AUTHOR]

In this research work, different parameters have been studied which affect the energy consumption of belt conveyors. The effect of conveying distance and vertical lift on energy consumption was studied in detail by collecting energy consumption and other associated data of belt conveyors from different cement plants and analyzed with the help of different statistical analysis. The highest specific energy consumption was found to be 0.0011 kWh/Ton‐m in the smallest belt conveyor of 15 m and the lowest specific energy consumption was found to be 0.0007 kWh/Ton‐m in the longest belt conveyor of 100 m. Specific energy consumption is reduced up to 36% per ton‐meter of material conveying in the longest belt conveyor compared with the smallest belt conveyor. On the contrary, the specific energy consumption for different vertical lifts is found almost the same. Hence, it can be suggested that for horizontal conveying, the series of belt conveyors should be replaced with a single long conveyor to achieve maximum conveying efficiency and to lower specific energy consumption. The above study will be helpful for heavy industries such as cement plants, steel plants, and so forth to reduce their energy consumption on belt conveyors and achieve the environmental sustainability goals. At the last part of the study an equation is derived for estimating energy consumption in belt conveyors by applying multiple regression analysis. The proposed equation will be helpful for estimation of energy consumption of belt conveyors according to which the decision for conveyor length can be taken. [ABSTRACT FROM AUTHOR]

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.

Belt Conveyor merupakan alat angkut yang digunakan untuk memindahkan material dalam suatu proses di industri modern. Agar tidak terjadi kesalahan desain saat akan dilakukan penambahan atau modifikasi terhadap belt conveyor, harus diketahui secara pasti kecepatannya. Untuk mengetahui kecepatan rotasi pulley pada belt conveyor diperlukan suatu alat ukur. Salah satu metode yang digunakan adalah dengan menghitung kecepatan satu putaran (rotasi) per satuan waktu (menit). Salah satu jenis mikrokontroler yang dapat digunakan untuk menghitungnya adalah mikrokontroler AT89S51. Dan salah satu cara untuk mendeteksi adanya objek berbahan logam yang sedang berputar tanpa perlu melakukan kontak langsung /sentuhan terhadap objek tersebut adalah dengan menggunakan sensor inductive proximity. Kecepatan putaran dapat ditampilkan melalui Liquid Crystal Display (LCD). Agar dapat bekerja sesuai dangan fungsi yang diinginkan, mikrokontroler harus diprogram. Bahasa pemrograman yang dapat digunakan di antaranya adalah BASIC Compiler 8051

Belt conveyors are widely used in mining industry in open-pit and underground mining for moving bulkload in horizontal and inclined directions to the sites of processing. In order to create the best conditions of frictional contact between a belt and a drum, various methods of drive drum lining are used. The main lining material is rubber of different grades, providing proper coefficient of friction of a belt with a drum (within the range of 0.6–0.62). A drive drum lining material must have high wear resistance, thermal resistance, mechanical strength, ability not to accumulate electric charges on the surface and not to generate dangerous concentrations of toxic components (for example, chlorine gases, carbon monoxide) when heated. The use of ceramic lining opens up great opportunities for increasing lining durability and useful life of high capacity heavy-duty conveyors. The paper presents the results of the study of stress-strain state of belt conveyor drive drum ceramic lining plates. We used Solid Work Simulation environment in the study on the basis of the accepted analytical model of plate-belt contact for drive drum with diameter D = 1250 mm, belt width L = 1000 mm, and the belt entering branch tension value Se = 25400 daN with regard to the value, direction, and nature of the acting loads. On the basis of stress-strain analysis of alumina ceramics lining plates, the favorable geometrical parameters of the plate cleats (projections) and the required properties of lining material ensuring the proper load-carrying capacity at the contact with the belt rubber facing were found. It was established that a plate cleat diameter for heavy duty conditions should be not less than 4.5 mm and its end round R should be within the limits of 0.5–0.6 mm, and, in the base, 0.3–0.4 mm at a cleat height of 1.0–1.4 mm in order to prevent stress concentration in hazardous sections. It was also established that alumina ceramics bending strength must be no less than 350 MPa for effective functioning of rubber-ceramic lining. Simulation of a plate stress-strain state on exposure to alternating loads made it possible to identify characteristic areas with maximum stress concentration, which were foci of crack nucleation. Thus, it became possible to predict lining useful life.

The paper shows the results of researching the rollers used in belt conveyors working in a hard coal mine. Rollers should be characterized by low rotation resistance, which results in low power consumption of the propulsion unit. The tests conducted aimed to assess the technical condition of the new C-type rollers after several years of operation in a hard coal mine. The research was carried out from the new approach to the standard issues of roll testing and verification in a hard coal mine. Laboratory tests were combined with testing the entire belt conveyor in terms of power demand. The research was carried out for 3 years and it was monitored how the dynamic resistance of the working rollers changed and how this translates into the power demand consumed by the belt conveyor in a hard coal mine. The aim of the research was a new approach to the research of rollers used in conveyors operating in a mine.

In the material transfer area, the belt is exposed to considerable damage, the energy of falling material is lost, and there is significant dust and noise. One of the most common causes of failure is transfer chute blockage, when the flow of material in the free fall or loading zone is disturbed by oversized rock parts or other objects, e.g., rock bolts. The failure of a single transfer point may cause the entire transport route to be excluded from work and associated with costly breakdowns. For this reason, those places require continuous monitoring and special surveillance measures. The number of methods for monitoring this type of blockage is limited. The article presents the research results on the possibility of visual monitoring of the transfer operating status on an object in an underground copper ore mine. A standard industrial RGB camera was used to obtain the video material from the transfer point area, and the recorded frames were processed by a detection algorithm based on a neural network. The CNN autoencoder was taught to reconstruct the image of regular transfer operating conditions. A data set with the recorded transfer blockage state was used for validation. [ABSTRACT FROM AUTHOR]

Belt mistracking is one of the most dangerous and costly failures of belt conveyors. This phenomenon can lead to complete damage to the conveyor belt, increased demand for electricity from the drive, accelerated wear of route elements, and poses a fire hazard. The article presents the most important causes and ways to detect belt mistracking. Based on the tests carried out at the laboratory test rig, the mechanical and operating parameters of the electric drive of conveyor operation burdened with the problem of lateral running off for the tested belt were compared. The mistracking causes a sudden, differentiated increase in the resistance to movement of the machine, which directly affects the recorded values of forces in the drive system and tensioning mechanism and increases the electrical energy consumption of the drive motor. Belt run monioring based on these parameters is not precise, because there are many other factors on the conveyor route, potentially responsible for the increase in recorded currents and forces. The paper presents an analysis of the possibility of using three, non-contact methods directly monitoring the lateral run-off of the belt, based on vibration measurements of conveyor pulley bearing, RGB images, thermal imaging and measuring the compressive force on the tensioning device. [ABSTRACT FROM AUTHOR]