Belt Conveyor System Research Articles

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.

Constraint-following control design for nonlinear underactuated belt conveyors with uncertainty

There are many mismatched uncertainties in the nonlinear underactuated belt conveyor systems which leads to challenges for designing the controllers. This paper addresses this issue based on the concept of constraint-following by establishing the dynamic model and treating the desired trajectory as a constraint. First, the nominal control portion is studied in the absence of the deviation of initial condition and uncertainties. Second, the uncertainties are decomposed into matched and mismatched parts according to the match condition. Then, the robust constraint-following control portion that is solely based on matched uncertainty is designed. By utilizing the Lyapunov method, the proposed control with two portions is able to ensure the uniform boundedness and uniform ultimate boundedness of the underactuated systems with uncertainties. Simulations are additionally carried out for the purpose of verification.

Digital transformation technologies for conveyor belts predictive maintenance: a review

The availability of condition-monitoring data has increased due to internet of things (IoT) technologies, providing information on various parameters like vibration, temperature, current, and voltage. Cloud computing and big data facilitate the prevention of failures and estimation of remaining useful life through advanced mathematical models and artificial intelligence (AI) techniques. These enable prompt and suitable maintenance actions. This article conducts a systematic review of digital transformation technologies, including cloud computing, edge computing, AI, machine learning (ML), and TinyML, in predictive maintenance for conveyor belt systems. This article reviews how these digital transformation technologies improve predictive maintenance strategies for conveyor belts. The systematic review summarizes the results and challenges of various methodologies used in conveyor belt systems and suggests areas for further research. This paper aimed to serve as a useful resource for researchers, practitioners, and industry professionals seeking insights into current predictive maintenance technologies for conveyor belt systems. The takeaways of the review are expected to ignite discussion on efficient and proactive maintenance strategies and promote the development of innovative solutions for ensuring the reliability and longevity of conveyor belt systems in the digital era.

Tunneling Mechanisms of Quinones in Photosynthetic Reaction Center–Light Harvesting 1 Supercomplexes

In photosynthesis, light energy is absorbed and transferred to the reaction center, ultimately leading to the reduction of quinone molecules through the electron transfer chain. The oxidation and reduction of quinones generate an electrochemical potential difference used for adenosine triphosphate synthesis. The trafficking of quinone/quinol molecules between electron transport components has been a long‐standing question. Here, an atomic‐level investigation into the molecular mechanism of quinol dissociation in the photosynthetic reaction center–light‐harvesting complex 1 (RC–LH1) supercomplexes from Rhodopseudomonas palustris, using classical molecular dynamics (MD) simulations combined with self‐random acceleration MD‐MD simulations and umbrella sampling methods, is conducted. Results reveal a significant increase in the mobility of quinone molecules upon reduction within RC–LH1, which is accompanied by conformational modifications in the local protein environment. Quinol molecules have a tendency to escape from RC–LH1 in a tail‐first mode, exhibiting channel selectivity, with distinct preferred dissociation pathways in the closed and open LH1 rings. Furthermore, comparative analysis of free energy profiles indicates that alternations in the protein environment accelerate the dissociation of quinol molecules through the open LH1 ring. In particular, aromatic amino acids form π‐stacking interactions with the quinol headgroup, resembling the key components in a conveyor belt system. This study provides insights into the molecular mechanisms that govern quinone/quinol exchange in bacterial photosynthesis and lays the framework for tuning electron flow and energy conversion to improve metabolic performance.

Enhancing the efficiency of Brown 24 pigment production through continuous microwave heating in conveyor belt and rotary kiln systems: A design and optimization study

In energy intensive industries, the continuous production with microwave technology presents several challenges in achieving high energy efficiency and heating uniformity. In the ceramic pigments sector, the Brown 24 is the ideal candidate for this study due to its susceptibility to thermal runaways and its high temperature to reach total conversion, higher than 1000K. By another hand, most literature related to this field focus on static systems while ignoring the continuous ones which are required by the industry. A coupled model that integrates thermal, electromagnetic and chemical phenomena within an energy system was implemented in COMSOL Multiphysics. Additionally, a MATLAB controller was employed to dynamically adjust the cavity length through a moving plunger, which maximizes the electrical efficiency. The required power is also managed to guarantee a total chemical conversion of the material. The proposed optimization methodology reduces computational costs, and it is applicable to any continuous microwave system processing moving solid materials. In this work, two microwave configurations were optimized. The first one, based on a conveyor belt, achieved a global efficiency close to 70%. While the second one, based on a rotary kiln, achieved a global efficiency of 85% and a production rate of 4.66kg/h, significantly outperforming a previous study by factors of 1.57 and 2.06, respectively. These findings show the potential for substantial improvements in continuous microwave systems.

Optimal filter design using mountain gazelle optimizer driven by novel sparsity index and its application to fault diagnosis

The informative frequency band (IFB) plays a vital role in detecting defects in complex machinery through visible informative features. In the present work, a denoising filter has been designed to enhance the small non-stationarities present in the signal. Initially, the system impulse is computed to estimate the filter coefficients which are further optimized by the mountain gazelle optimization (MGO) based on the maximum value fitness function. The novel sparsity index based on kurtosis and negentropy (NE) is put forward as the fitness function. Then, optimized coefficients are convolved with the system impulse to design the denoising filter. The efficacy of the designed filter is verified through vibration and acoustic signals from the defective components of the belt conveyor system. The designed filter is better able to extract the impulsiveness from the signal, give improved values of kurtosis and signal-to-noise ratio (SNR), and reduce interferences from other machinery components and the environment simultaneously.

Compact Linear Flow Phantom Model for Retinal Blood-Flow Evaluation.

Impaired retinal blood flow is associated with ocular diseases such as glaucoma, macular degeneration, and diabetic retinopathy. Among several ocular imaging techniques developed to measure retinal blood flow both invasively and non-invasively, adaptive optics (AO)-enabled scanning laser ophthalmoscopy (AO-SLO) resolves individual red blood cells and provides a high resolution with which to measure flow across retinal microvasculature. However, cross-validation of flow measures remains a challenge owing to instrument and patient-specific variability in each imaging technique. Hence, there is a critical need for a well-controlled clinical flow phantom for standardization and to establish blood-flow measures as clinical biomarkers for early diagnosis. Here, we present the design and validation of a simple, compact, portable, linear flow phantom based on a direct current motor and a conveyor-belt system that provides linear velocity tuning within the retinal microvasculature range (0.5-7 mm/s). The model was evaluated using a sensitive AO-SLO line-scan technique, which showed a <6% standard deviation from the true velocity. Further, a clinical SLO instrument showed a linear correlation with the phantom's true velocity (r2 > 0.997). This model has great potential to calibrate, evaluate, and improve the accuracy of existing clinical imaging systems for retinal blood flow and aid in the diagnosis of ocular diseases with abnormal blood flow.

Feasibility of Condition Monitoring of Belt Splices in Belt Conveyor Systems Using IoT Devices*

Feasibility of Condition Monitoring of Belt Splices in Belt Conveyor Systems Using IoT Devices*

Optimal design of robust control for belt conveyor systems based on fuzzy dynamic model and Nash game

An optimized robust control method is proposed in this paper to deal with the disturbances of nonlinear factors such as uncertainty in belt conveyor and to realize the speed control of belt conveyor. By controlling the subsystems to track the same desired speed, belt breaks and overlaps caused by varying belt speeds from one section to another are limited. First, a fuzzy model is developed by applying fuzzy set theory to describe uncertainties of belt conveyor such as masses, stiffness, damping and frictions. Second, a robust control is designed for the nominal system and compensating for the uncertainty. With the robust control, the belt conveyor system is proved to be uniformly bounded and uniformly ultimately bounded by the Lyapunov approach. Third, the parameters of controller are optimized based on a non-cooperative game approach. The optimal solution is the Nash equilibrium. Optimal solution allows the controller system to perform better and decrease the cost of control. Finally, by comparing with LQR control and different parameters, the feasibility of robust control and the rationality of the optimal parameters are proven.

A generalized Udwadia–Kalaba control design for uncertain belt conveyor systems with inequality constraints

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.

Edge Machine Learning-Enabled Predictive Fault Detection System for Conveyor Belt Maintenance Optimization in Industrial Settings

Digital transformation is essential for industrial and manufacturing sectors, especially in conveyor belt systems. On the other hand, Innovative electronic components and software applications offer opportunities for advanced fault prediction, minimizing operational disruptions, reducing inefficiencies, and enhancing overall industrial efficiency. Efficient conveyor belt systems are crucial for industrial operations, requiring early fault detection to prevent costly downtime and ensure safety. The utilization of embedded machine learning via the Edge Impulse platform enhances data collection and spectral feature extraction using wavelet approximation. This article presents a novel methodology for predictive fault detection in conveyor belt systems employing edge machine learning. Leveraging the ESP32 microcontroller and accelerometer sensor for real-time vibration data capture, a four-layer Artificial Neural Network (ANN) model, trained on 336 feature samples, is deployed via Edge Impulse and TensorFlow scripts. TensorFlow Lite compresses the model for microcontroller integration. The ESP32 microcontroller, acting as an edge device, achieves real-time predictions with 97.5% accuracy and 0.20 minimal loss. This work promises significant strides in predictive maintenance, offering cost savings, operational reliability, and efficiency gains in industrial settings, revolutionizing maintenance strategies.

Airguard Ai: Revolutionizing Air Cargo Inspection through Pygame and YOLOv8 Simulation

The objective of this project is to create a simulation for air cargo inspection using artificial intelligence (AI). This will be achieved by combining Pygame for simulating conveyor belts and YOLOv8 for detecting hazardous items. The simulation replicates a conveyor belt system that transports air cargo for inspection. Pygame, a robust game development package, is used to build a graphically interactive environment where users may view and examine the cargo inspection process in a simulated context. The primary element of the project entails using YOLOv8, an advanced object detection model, to precisely locate dangerous things within the shipment. The real-time detection capabilities of YOLOv8 allow for quick and efficient examination of the cargo, ensuring the timely identification of any hazards. The simulation serves as a platform to assess and verify the effectiveness of the AI-driven cargo inspection system across different scenarios. Users have the ability to engage with the simulation by modifying factors like the speed of the conveyor belt, the types of cargo, and the criteria for inspection. This allows them to assess the effectiveness of the system in identifying dangerous objects. This project functions as an instructional tool to comprehend the incorporation of AI in cargo inspection, while also having practical implications for improving real-world air cargo security. Pygame and YOLOv8, when combined, offer a flexible and robust framework for simulating and evaluating AI-based inspection systems. This contributes to the progress of air cargo safety and security measures.

A New Denoising Method for Belt Conveyor Roller Fault Signals.

In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method's denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.

Design of Motor Condition Monitoring System for Belt Conveyor in Coal Mine

With the increase of the number of mines and the depth of mining coal in China, it is more and more important for the safety performance of underground equipment. On the basis of analyzing the coal mine safety production and transportation system, we design the motor condition monitoring system of coal mine belt conveyor, choose high-precision sensors to collect the signals of voltage, current, temperature, torque and speed of the coal mine belt conveyor motor, and use the CAN communication circuit to transfer the collected data to the computer, so as to realize the condition monitoring of the coal mine belt conveyor motor.

Exponential robust control for fuzzy belt conveyors model with adaptive tension force

AbstractThere exist multiple uncertainties in the belt conveyor system. To reduce the system errors and save the control energy, the uncertainties need to be described and compensated accurately. In this paper, the fuzzy set theory is applied to establish the fuzzy dynamical model. Then, an exponential control is designed and a noncooperative‐game based optimization approach is proposed to solve out the Nash equilibrium. The novel control with optimal parameters including the adaptive tension force are applied to render the system uniform boundedness and uniform ultimate boundedness. Finally, the simulations are performed for verification.

Design and Application Research on Modular Construction Waste Sorting, Recycling Equipment and Intelligent Recognition System

With the rapid advancement of urbanization and the continuous development of the construction industry, the generation of construction waste is increasing steadily, posing severe challenges to environmental protection and resource recycling. To address this issue, this paper delves deeply into the design and application of modular equipment for sorting and recycling construction waste, as well as intelligent recognition systems. This equipment system integrates advanced robotic arm technology, conveyor belt systems, electromagnetic suction rods, and cutting-edge visual recognition technology, with the aim of achieving intelligent sorting and efficient recycling of construction waste. This paper provides a detailed elaboration from multiple perspectives, including the system’s design philosophy, key technology applications, analysis of experimental results, and future development prospects. The objective is to offer innovative solutions for the resource utilization of construction waste.

Performance Analysis of Permanent Magnet Synchronous Motor for Mining Applications – A Review

The features of Permanent Magnet Synchronous Motor (PMSM) makes it suitable for mining applications such as copper mining, mining floating machine, mining scraper conveyor, mine integrated machine, mining vacuum circuit breaker, mineral extraction in mines, coal mine belt conveyor system, mining truck and many more. This study focuses on performance analysis of permanent magnet synchronous motor for mining applications and performance analysis is the confirmation and analysis of parameters, various losses and efficiency at various loads. A comparative analysis of wound rotor synchronous motor, induction motor and permanent magnet synchronous motor confirmed the suitability of PMSM for various mining applications. Advanced technology of artificial intelligence integrated with permanent magnet synchronous motor also enhances the effectiveness and precision of the system.

Research on dynamic characteristics of the turnover system of rail belt conveyor

The rail belt conveyor is a new bulk material conveying system that combines the advantages of traditional belt conveyors and railway systems. However, the turnover system, which is a key part of the carriage’s direction-changing mechanism, experiences severe vibration and impact problems that affect its performance and reliability. In this study, a dynamic model of the carriage-rail coupling in the turnover system is established, and the vibration characteristics of the rail are analyzed. The influence of carriage speed, wheelbase, and wheel diameter on the vibration acceleration of the rail is investigated. The results show that higher carriage speeds intensify the vibration response of the rail when the carriage leaves the turnover wheel. The wheel diameter has a significant effect on the vibration acceleration, especially at high speeds. Additionally, the wheelbase affects the vibration acceleration, with smaller wheelbases resulting in more prominent effects. The established dynamic model is validated using measured data, providing insights into the dynamic behavior of the turnover system in rail belt conveyors.

Design and Simulation of Robot Gripper and Conveyor System for Workstations

Abstract: Intelligent manufacturing is going to boom in the upcoming years. The characteristic features of Industry 4.0 are fully automated production facilities where all processes are controlled in real-time and take into account the changing external conditions. The core of the Industry 4.0 is made up of digitalization and robotics, in particular, the use of collaborative robots and MES. MES (Manufacturing Execution System) is a specialized system designed to solve the problems of synchronization, coordination, analysis, and optimization of production. It involves the use of conveyor belts for rapid transportation of products. Industries where conveyor belt systems are excelling include automotive, computer, good, packaging, print finishing, bottling and canning, chemical, pharmaceutical, aerospace and food processing The safety conditions in fundamental industrial jobs are dangerous and harmful combined with ineffective manual work as a result of rising need for mass manufacturing. In industries, automation is the use of technology to jobs that were previously handled by humans. It is a potent instrument that can aid companies in enhancing quality, production, and efficiency. A typical type of mechanical handling equipment that transports things from one place to another is a conveyor system. These systems consist of conveyor belts that are designed to work seamlessly with robotic arms and other automated equipment. Increased automation and mass production rate reduces the lead time and increases the profit of a firm. The deployed robotic arms are immensely capable of handling various tasks with increased flexibility. Using a robotic arm with an end effector as a gripper to hold/pick and place the material from the conveyor belt improves the product flow process. The project comes with the NodeMCU firmware and an integrated Wi-Fi chip, the ESP8266 12E. To connect with the controller via a mobile phone, a web page would be constructed using HTML and coded using the Arduino IDE's C language. At either end of the conveyor belt, 2 robotic arms with 2 degrees of freedom (D.O.F.) would be placed. Using one arm to place the product on the belt and a second to pick it up at the other end. Also calculating the analytical data of the system that is involved in reallife industrial processes along with the simulation of entire system for the UR10 industrial robot, using the RoboDK simulation software. Through the usage of this system, productivity is boosted with improved safety of the working environment, become more effective, be more flexible, and have higher levels of job satisfaction

A visual detection method for conveyor belt misalignment based on the improved YOLACT network

Conveyor belt misalignment is an operational and maintenance issue for many belt conveyor systems and can result in significant downtime and equipment failure. This article presents a visual detection method for belt misalignment detection using real-time instance segmentation. An improved version of the You Only Look At CoeffcienTs (YOLACT) network is discussed, where the feature backbone of the original YOLACT network is optimized. The improved YOLACT network is used to experimentally detect the belt misalignment with different combinations of bulk solid and operating conditions. The results show that the detection precision of the improved YOLACT network achieves 86.05% mean Average Precision (mAP), in which the greatest improvement on the mAP is the FPN modification with an increase of 1.75% compared to the YOLACT network with the ResNeXt-50. In terms of the detection speed, the improved YOLACT network (15.19 Frames Per Second [FPS]) outperforms the Mask R-CNN (3.42 FPS) and the MS R-CNN (3.41 FPS). Moreover, the averaged detection error from the tests is 0.74 mm for a belt loaded with different bulk solids. It can be concluded that the application of the improved YOLACT network is effective for the real-time detection of conveyor belt misalignment.