Conveyor Belt Research Articles

Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage

This paper is devoted to the real-time monitoring of close transportation devices, namely, belt conveyors. It presents a novel measurement system based on the linear strain gauges placed on the tail pulley surface. These gauges enable the monitoring and continuous collection and processing of data related to the process. An initial assessment of the machine learning application to the load identification was made. Among the tested algorithms that utilized machine learning, some exhibited a classification accuracy as high as 100% when identifying the load placed on the moving belt. Similarly, identification of the preset damage was possible using machine learning algorithms, demonstrating the feasibility of the system for fault diagnosis and predictive maintenance.

PLC based laser scanning system for conveyor belt surface monitoring

PLC based laser scanning system for conveyor belt surface monitoring

A Synoptic‐Dynamic View of the Millennium Drought (2001–2009) in Southeastern Australia

AbstractAustralia has had several severe droughts in its recent history. Most studies have linked these droughts to large‐scale modes of variability, whereas few studies have investigated droughts from the perspective of weather systems. The current study examines a wide range of weather systems focusing especially on heavy rainfall events, which are important to meteorological drought. Two distinct phases (development and recovery) are identified for the Millennium Drought based on the cumulative standardized precipitation index. Differences in precipitation from heavy rainfall events between the two drought phases are most pronounced in autumn and summer. The pronounced reduction in precipitation from autumn heavy rainfall events during the development phase is due to fewer, less intense, faster‐moving warm conveyor belts. In contrast, increased precipitation from autumn heavy rainfall events in the recovery phase is explained by an interaction between warm conveyor belts and upper‐level anticyclonic potential vorticity with a persistent anticyclonic circulation over the Tasman Sea acting to slow the eastward propagation of rainfall‐producing weather systems. In summer, however, the difference in precipitation from heavy rainfall events in the two drought phases is due to changed moisture content within warm conveyor belts. In the recovery phase, the higher moisture content is associated with greater moisture transport over the Tasman Sea between Australia and New Zealand.

Simulation and Experimental Study on Indentation Rolling Resistance of a Belt Conveyor

In this paper, in order to reduce the indentation rolling resistance of a belt conveyor and improve its operation efficiency, the indentation rolling resistance of a belt conveyor is studied. Firstly, the theoretical calculation of the indentation rolling resistance caused by the contact between a conveyor belt and an idler is studied. Additionally, the expression of the indentation rolling resistance including the multi-element Maxwell relaxation modulus is simplified and deduced using the assumption of small deformation, and the steps of iterative calculation of the indentation rolling resistance through setting the initial values are designed. Then, the multi-component Maxwell relaxation modulus function of the conveyor belt tested in the previous work of the authors is substituted into COMSOL 5.6 finite element software, and the stress distribution caused by the contact between the conveyor belt and the idler under different working conditions is analyzed. Also, a series of measures to reduce the indentation rolling resistance are found through the laws presented by the simulation results. Finally, the experimental study of the indentation rolling resistance is carried out using the indentation rolling resistance testing device from our research group. By comparing the experimental results for the indentation rolling resistance under different working conditions with the theoretical calculation results for the indentation rolling resistance, it was found that the relative error was less than 13%, which proved the feasibility of the indentation rolling resistance and relaxation modulus testing method, as well as the theoretical calculation method for indentation rolling resistance.

Study of conveyor belt deviation detection based on improved YOLOv8 algorithm

Conveyor belt deviation is a commmon and severe type of fault in belt conveyor systems, often resulting in significant economic losses and potential environment pollution. Traditional detection methods have obvious limitations in fault localization precision and analysis accuracy, unable to meet the demands of efficient and real-time fault detection in complex industrial scenarios. To address these issues, this paper proposes an improved detection algorithm based on YOLOv8, aiming to achieve efficient and accurate detection during the operation of the belt. Firstly, an Enhanced Squeeze-and-Excitation (ESE) module is incorporated into C2f to boost feature extraction for rollers and belts. Secondly, the construction of the BiFPN_DoubleAttention module in the neck network enhances bidirectional feature fusion and attention mechanism, thereby improving multi-scale object localization accuracy under complex environments. Then, a Multi-Head Self-Attention (MHSA) mechanism is introduced in the head network, better capturing positional features of small roller targets and belt areas in various environments, thus enhancing detection performance. Finally, extensive experiments are conducted on a self-built dataset, achieving an accuracy of 98.1%, mAP0.5 of 99.0%, and a detection speed of 46 frames per second (FPS). This method effectively handles variations and disturbances in the conveyor belt transportation environment, meeting real-time diagnostic needs for belt misalignment in the industry.

Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning

In recent years, the application of machine learning techniques in condition monitoring has significantly advanced the precision and efficiency of fault detection processes. In particular, detecting bearing faults in conveyor belt drums is critical in the mining industry for maintaining operational reliability and productivity. This paper presents a case study using vibration signals and diagnostic reports provided by the company Dynamox. After meticulous data cleaning, preprocessing, and feature extraction employing advanced signal processing techniques and statistical features, several machine learning models were trained, optimized and evaluated, with the best models providing very promising results.

Textile Fabric Defect Detection Using Enhanced Deep Convolutional Neural Network with Safe Human–Robot Collaborative Interaction

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP).

Optical detection of single and multiple seeding using an innovative shape-recognition algorithm

The ability to accurately and continuously monitor seeding quality allows farmers to improve their seeding practices, maintain appropriate seed spacing, and maximize crop productivity. The precise detection and counting of individual seeds, especially those that pass through and overlap in pairs or multiples, is a major challenge in developing seed monitoring systems. In this research, a novel shape-based algorithm is presented that utilizes seven pairs of 3-mm infrared LEDs to efficiently recognize and count both single seeds and overlapping seeds. The outputs of the receivers were converted to binary signals by comparing them to samples taken when no seed flow was present. Each infrared receiver acted as a pixel, and to recognize and count multiple seeds, their outputs were temporarily stored in a matrix with seven columns. When the seeds passed the sensor, their outputs were analyzed together using an innovative and systematic clustering algorithm. This enables shape-based processing and recognition of passing seeds regardless of type and size, effectively reducing counting errors. The developed system was subjected to testing using seven different seed types on a conveyor belt, while one seed type underwent testing on a precision corn seeder in the laboratory. The results indicated a high level of agreement between the sensor data and the actual seed rates. The average counting accuracy for popcorn, hybrid corn, chickpea, pinto beans, coated tomato, mung beans, and soybean seeds on the conveyor belt platform was 0.99, 0.99, 0.98, 0.96, 0.8, 0.99, and 0.99, respectively. The results showed that the accuracy of the algorithm increases with increasing sphericity and size of the seeds relative to the distance between the adjacent LEDs. Moreover, the counting accuracy exceeded 97% for all rates on the precision corn seeder platform, with an average accuracy of 0.986.

Разработка устройства для обеззараживания ИК-излучением почвосмеси в тонком слое на конвейере

Pest and disease control in the cultivation of greenery is an integral part of obtaining a high-quality and high yield. Heat treatment is one of the best ways to combat plant diseases and pests and allows you to quickly create the necessary sterile conditions without the use of chemicals. A device for disinfection of soil mixtures in a thin layer by IR radiation on a conveyor is being developed for heat treatment. The prepared soil mixture from the feed hopper enters the conveyor belt in a thin layer. An IR emitter with adjustable power is installed above the conveyor belt. The movement of the conveyor belt should ensure heating in the soil mixture layer up to 100 ± 5 ° C. This is controlled by a temperature sensor, which sends a signal via a programmable relay to the conveyor motor and regulates its movement. The disinfection process allows to achieve high energy efficiency due to software regulation, taking into account optimal modes and control of the processing temperature. Keywords: DISINFECTION, IR RADIATION, HEATING, SURFACE, SOIL MIXTURE, DISEASES, PESTS, CONVEYOR

Self‐reinforced natural rubber composites: Preparation and properties study

AbstractIn the contemporary modern mining sector, rubber conveyor belts are extensively and frequently utilized. Exceptional tear resistance and high elongation of the top cover rubber of the belt are imperative. In this investigation, a portion of natural rubber (NR) underwent partial vulcanization in advance. Subsequently, this pre‐vulcanized NR (PNR) was mixed with the NR matrix via mechanical blending. Due to the increased viscosity resulting from pre‐vulcanization, the PNR may disperse in the NR matrix, maintaining a specific microstructure without complete dissolution. During subsequent vulcanization, the PNR could co‐vulcanize with the NR matrix, forming a well‐bonded interface and ultimately yielding self‐reinforced NR (SNR) composites. The influences of the mass ratio of PNR to the NR matrix, sulfur content in PNR and pre‐vulcanization time on the mechanical properties of the SNR composites were thoroughly investigated. The results indicated that adding PNR effectively improved the tear strength of NR. When the PNR replaced 20 wt% of NR, sulfur content in PNR was 2.4 phr and pre‐vulcanization time was 2 min, the tear strength, stress at 300% elongation and wear resistance of SNR composites increased by 34.1%, 16.6%, and 10.2% compared to NR control sample, while elongation at break remained at 898%. These findings suggest that it is possible to enhance the tear strength of rubber without compromising elongation at break.

How Does Tropical Cyclone‐Induced Remote Moisture Transport Affect Precipitation Over East Asia

AbstractAnalyzing tropical cyclone‐induced remote moisture transport clusters (TRCs) and their effects on precipitation is crucial for understanding precipitation formation and enhancing forecast precision. Prior research, primarily case‐based, did not fully grasp the nature of TRCs. Utilizing an objective TRC identification method, we categorized 65 TRC tracks in East Asia into five types and examined their traits and precipitation links. The findings indicate that the moisture transport height of TRCs varies due to multiple factors, with higher transport linked to the warm conveyor belt and lower transport attributed to Taiwan’s mountain range influencing low‐level moisture. The precipitation peak altitudes of TRCs at higher latitudes are greater, and their precipitation intensity is positively correlated with terrain type, especially coastlines. This study underscores the diversity in TRC characteristics and related precipitation, suggesting that future research should consider the directionality of TRC tracks.

Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15–18 minutes conventionally to 5–7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Research on Air Cushion Flow Field and Bearing Characteristics of Single Row Hole Air Cushion Belt Conveyor

In order to obtain information regarding changes in the air cushion flow field and load-bearing characteristics of a single row hole air cushion belt conveyor, a structural model of the air cushion of the belt conveyor was established, with the single row hole air cushion belt conveyor as the research object. Firstly, according to the theory of fluid lubrication, a mathematical model of the air cushion was established. Then, the effects of air cushion thickness, pore velocity, and belt velocity on the pressure and bearing characteristics of the air cushion flow field were studied using FLUENT software. In addition, the equivalent stress, displacement, and pressure curves between the air cushion flow field and the conveyor belt and the material were analyzed using the two-way fluid–solid coupling method. Finally, the experimental platform of a single row hole air cushion belt conveyor was built, and the flow field and bearing characteristics of the belt were verified through experiments. The results show that reducing the thickness of the air cushion and increasing the pore flow rate can improve the pressure and bearing characteristics of the air cushion, while speed has little effect at lower belt speeds.

Sinusoidal PWM Generation for 3 Phase Inverter and RPM Measurement using Hercules TMS570LC43xx Launchpad

This project focuses on implementing a 3 phase Sinusoidal PWM generation using the Hercules TMS570LC43xx Launchpad Development Kit (Launchpad). The primary objective is to generate synchronized Sinusoidal nature PWM signals using the onboard High-End Timer (HET) and Enhanced Pulse Width Modulation (ePWM) module, which can be given to the Inverter for conversion of Direct current (DC) power into Alternating current (AC) power. This conversion is essential in various applications where AC power is required but the power source provides DC power. It is used in Solar photovoltaic (PV) systems and other renewable energy installations. These systems generate power suitable for powering household appliances or feeding into the electrical grid. Itis alsoused in electric vehicles (EVs) to drive the electric motor with variable speed. For verification of the wave nature, we have used an external lowpass filter (LPF) to transform the dynamic PWM signals into sinusoidal waveforms, ensuring compatibility with various applications like Inverters which can be further used in equipment and machinery such as Brushless DC motors, pumps and compressors. With the addon functionality to control the signal’s frequency which will be given to the Inverter to control the speed of the motor. Additionally, the project incorporates RPM measurement of the motor using an optical encoder setup interfaced with the Enhanced Quadrature Encoder Pulse (eQEP) module on the Launchpad. This feature enables accurate measurement of rotational speeds, position and Revolution per minute (RPM), enhancing the functionality of the system in real world applications like the speed of conveyor belts and other automated transport systems. Through successful implementation, this project demonstrates wide control for the Inverter, achieving reliable synchronized 3 phase signals with its variable speed having 120-degree phase shift signals alongside precise RPM measurement. The project highlights the Launchpad’s capabilities in handling complex signal processing tasks essential for modern power electronics applications. Looking forward, this project establishes a foundation for future enhancements and innovations in power electronics.

RSFA-Net: A High-Performance Lightweight Network for Ore Segmentation and Proportion Detection in Conveyor Belt Images

RSFA-Net: A High-Performance Lightweight Network for Ore Segmentation and Proportion Detection in Conveyor Belt Images

A line scanning monitoring method for conveyor belt deviation using point cloud

Abstract A precise conveyor belt deviation monitoring method using line array point cloud data is proposed and demonstrated, which can ensure the healthy running of the conveyor system. The point cloud data characterizing the surface of the conveyor belt is collected in a line scanning way. Then, using a unique soft extraction method that weighted fusing three key features (cross-sectional variation, belt’s horizontal width, and previous frame) to process this data, the edge information of the conveyor belt can be accurately and robustly identified in real-time. Furthermore, the point cloud processing mode enables a belt-segmented deviation analysis method based on a standard sequence query. This can accurately determine the offset value and deviation trend of the conveyor belt, thereby achieving early warning of deviation faults. Experimental results show that the belt edge identification precision can reach 0.3 mm, and an early warning can be provided at least 57 m before the occurrence of a belt deviation fault. This belt deviation monitoring method can be widely applied in various working environments, especially in harsh conditions like mines and ports. It also has potential applications in automated production lines within Industry 4.0.

ANÁLISE DE UM PROTÓTIPO DE UMA ESTEIRA TRANSPORTADORA COM AUTOMAÇÃO INDUSTRIAL PARA SEPARAÇÃO DE PEÇAS METÁLICAS E NÃO METÁLICAS UTILIZANDO ARDUINO UNO

Introduction: The use of Arduino Uno and the LS PLC allows for an innovative approach to process automation, offering a practical and effective solution for industrial applications. This automated system is designed to increase productivity and accuracy in material separation, minimizing manual intervention and associated errors. Objective: to analyze a prototype of a conveyor belt with industrial automation for separating metal and non-metal parts using Arduino. Methodology: This study is a qualitative case study of the development of a prototype conveyor belt with industrial automation using the arduino system. Results: The data showed that the NPN inductive and ultrasonic sensors had high detection accuracy rates, with 95% and 97% success, respectively. Communication between the Arduino and the PLC was efficient, allowing the system to respond quickly to sensor readings and trigger the actuators synchronously. The implementation of safety devices, such as pushbuttons and circuit breakers, ensured system protection, while the visual monitoring provided by the 16×2 LCD display facilitated real-time monitoring of operations. Conclusion: The automation of the conveyor belt proved to be a viable and effective solution for sorting materials in an industrial context. The integration of the components and the efficient communication between them were fundamental to the success of the project. This project serves as a solid foundation for future innovations in industrial automation, contributing to the modernization and efficiency of production processes.

ANÁLISE DE UM PROTÓTIPO DE UMA ESTEIRA TRANSPORTADORA COM AUTOMAÇÃO INDUSTRIAL PARA SEPARAÇÃO DE PEÇAS METÁLICAS E NÃO METÁLICAS UTILIZANDO ARDUINO UNO

Introduction: The use of Arduino Uno and the LS PLC allows for an innovative approach to process automation, offering a practical and effective solution for industrial applications. This automated system is designed to increase productivity and accuracy in material separation, minimizing manual intervention and associated errors. Objective: to analyze a prototype of a conveyor belt with industrial automation for separating metal and non-metal parts using Arduino. Methodology: This study is a qualitative case study of the development of a prototype conveyor belt with industrial automation using the arduino system. Results: The data showed that the NPN inductive and ultrasonic sensors had high detection accuracy rates, with 95% and 97% success, respectively. Communication between the Arduino and the PLC was efficient, allowing the system to respond quickly to sensor readings and trigger the actuators synchronously. The implementation of safety devices, such as pushbuttons and circuit breakers, ensured system protection, while the visual monitoring provided by the 16×2 LCD display facilitated real-time monitoring of operations. Conclusion: The automation of the conveyor belt proved to be a viable and effective solution for sorting materials in an industrial context. The integration of the components and the efficient communication between them were fundamental to the success of the project. This project serves as a solid foundation for future innovations in industrial automation, contributing to the modernization and efficiency of production processes.

Longitudinal vibrations and their suppression of a tethered satellite system using friction self-excitation mechanism

This paper proposes a friction self-excitation mechanism to regulate the longitudinal vibrations of the tether during the state-keeping phase of a two-body tethered satellite system. A simplified dynamic model is established, consisting of two mass blocks, a constant-length tether, and a conveyor belt. The Stribeck friction model is utilized to describe the effects of friction on the stability of longitudinal vibration. Numerical simulations reveal the instability phenomenon induced by Hopf bifurcation and indicate the potential self-excited vibration under different parameters. Additionally, the paper explores the design approach for the tether by introducing the concept of segmented tethering and its dynamic behavior under different design scenarios. Varying the stiffness of the tether can change the vibration modes of the system. Finally, a ground experiment is carried out to evaluate the dynamic behaviors of the system and to demonstrate the effectiveness of the proposed vibration suppression mechanism.