Belt Damage Research Articles

Predictive Modeling of Conveyor Belt Deterioration in Coal Mines Using AI Techniques

Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear.

HLG-YOLOv7: small object detection in conveyor belt damage based on leveraging hybrid local and global features

In the industrial production process, the detection of conveyor belt damage plays a crucial role in ensuring the stable operation of the transportation system. To tackle the issues of significant changes in damage size, missed detections, and poor detection ability of small-size objects in conveyor belt surface damage detection, an improved HLG-YOLOv7 (Hybrid Local and Global Features Network) conveyor belt surface defect detection algorithm is proposed. Firstly, Next-VIT is employed as the backbone network to fully extract local and global features of the damage, enhancing the model’s ability to extract features of different-sized damages. Additionally, to deeply utilize the extracted local and global features, the Explicit Visual Center (EVC) feature fusion module is introduced to obtain comprehensive and discriminative feature representations, further enhancing the detection capability of small objects. Lastly, a lightweight neck structure is designed using GSConv to reduce the complexity of the model. Experimental results demonstrate that the proposed method has better small object detection performance compared to existing methods. The improved algorithm achieves mAP and F1 scores of 96.24% and 97.15%, respectively, with an FPS of 28.2.

EMD-based noise reduction study of steel cored conveyor belt containing slag signal

This paper proposes a noise reduction technique that combines empirical mode decomposition (EMD) and wavelet thresholding to eliminate the noise interference caused by metal slag in the steel cord conveyor belt damage signal. Initially, the original signal undergoes decomposition by EMD into high- and low-frequency intrinsic mode function (IMF) components based on metal slag signal feature analysis. The wavelet threshold de-noising method is then applied to filter out the high-frequency noise while preserving the low-frequency IMF components. The IMF components containing slag noise are discarded, and the filtered signal and the low-frequency IMF components are used for signal reconstruction to obtain an optimal noise reduction effect. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

Monitoring of Rubber Belt Material Performance and Damage.

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.

Ensemble Deep Learning for Automated Damage Detection of Trailers at Intermodal Terminals

Efficient damage detection of trailers is essential for improving processes at inland intermodal terminals. This paper presents an automated damage detection (ADD) algorithm for trailers utilizing ensemble learning based on YOLOv8 and RetinaNet networks. The algorithm achieves 88.33% accuracy and an 81.08% F1-score on the real-life trailer damage dataset by leveraging the strengths of each object detection model. YOLOv8 is trained explicitly for detecting belt damage, while RetinaNet handles detecting other damage types and is used for cropping trailers from images. These one-stage detectors outperformed the two-stage Faster R-CNN in all tested tasks within this research. Furthermore, the algorithm incorporates slice-aided hyper inference, which significantly contributes to the efficient processing of high-resolution trailer images. Integrating the proposed ADD solution into terminal operating systems allows a substantial workload reduction at the ingate of intermodal terminals and supports, therefore, more sustainable transportation solutions.

Transverse Profiles of Belt Core Damage in the Analysis of the Correct Loading and Operation of Conveyors

This article presents an analysis of the transverse profile of belt damage in the context of the proper loading and operation of conveyors. The aim of this study was to identify and understand the characteristic features of damage and their placement that may occur in conveyor belts during operation and indicate abnormalities in any of the components of the conveyor system, especially during loading at chutes. A total of seven different conveyors were examined, and the obtained results allowed for a thorough comparison and investigation of the distribution of belt damage on their cross-sectional profiles. This article discusses factors that may contribute to the occurrence of unevenly distributed belt damage. The conclusions from the conducted research can be of significant importance for conveyor owners and operators as they enable an effective assessment of the conveyor belt’s condition, the correctness of the conveyors, and the chutes’ designs as well as the implementation of necessary design changes, correct actions, and repairs. Through proper belt monitoring and maintenance, the risk of failure can be minimized, extending the belts’ lifespan and ensuring the efficiency of the transportation process. The article presents practical approaches to the analysis of the cross-sectional profile of damage, serving as a valuable source of information for individuals interested in optimizing the transportation process and maintaining efficient and safe conveyor operation.

Deep-learning-based multistate monitoring method of belt conveyor turning section

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

Experimental Study of the Influence of the Interaction of a Conveyor Belt Support System on Belt Damage Using Video Analysis

This article describes a method of obtaining data from the examination of an impact process by capturing it with a TroubleShooter TSHRMM high-speed camera and evaluating the results of a video analysis of the impact of the load on a conveyor belt using MiDAS Player version 5.0.0.3. By using the conveyor belt deflection obtained from the video analysis and the visual damage to the conveyor belt from an impactor, damage classification models were created. This model was created for two groups of experiments that took place without the presence of a support system and with the presence of a support system, respectively. The experimental research included monitoring the deflection of the conveyor belt and the degree of damage to the conveyor belt from the selected parameters.

Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data

Rotating machines are frequently used in industrial applications. However, due to their severity, mechanical failures such as rotor imbalance, shaft imbalance, pulley imbalance, structural breakage, and bearing imbalance can lead to unplanned shutdowns. While vibration analysis-based condition monitoring techniques can detect and diagnose many early errors, certain mechanical faults have associated vibration characteristics that make it difficult to identify and distinguish these faults. To address this issue, this paper proposes a method based on data fusion for vibrational and electrical signatures to achieve new fused signatures for healthy and different faulty cases. The weighted decision fusion method generates the fused decision by weighting and combining the output of multiple sensors. Conventional vibration evaluation parameters diagnose unbalance, pulley misalignment, belt damage, and combined faults. However, these parameters have more dimensions and correlated features for some faulty cases, such as unbalance and misalignment. Therefore, the Principal Component Analysis (PCA) was applied to reduce the dimensionality of evaluating parameters and preserve almost all data variation. The PCA produces uncorrelated Principal Components (PCs) for each case. A backpropagation neural network (BPNN) was constructed to construct an integrated fault diagnosis framework. The first and second PC was inserted as input parameters in the training set of BPNN. It was observed that BPNN achieves 2.1762×10-10 Mean Squared Error (MSE) demonstrates superior data fusion solutions and PCA in the condition monitoring of rotating machines. Overall, this study proposes an effective method for diagnosing mechanical faults in rotating machines, which can improve reliability and reduce downtime in industrial applications.

Machine vision based damage detection for conveyor belt safety using Fusion knowledge distillation

A belt conveyor system is one of the essential equipment in coal mining. The damages to conveyor belts are hazardous because they would affect the stable operation of a belt conveyor system whilst impairing the coal mining efficiency. To address these problems, a novel conveyor belt damage detection method based on CenterNet is proposed in this paper. The fusion of feature-wise and response-wise knowledge distillation is proposed, which balances the performance and size of the proposed deep neural network. The Fused Channel-Spatial Attention is proposed to compress the latent feature maps efficiently, and the Kullback-Leibler divergence is introduced to minimize the distribution distance between student and teacher networks. Experimental results show that the proposed lightweight object detection model reaches 92.53% mAP and 65.8 FPS. The proposed belt damage detection system can detect conveyor belt damages efficiently and accurately, which indicates its high potential to deploy on end devices.

Calibration procedure for ultrasonic sensors for precise thickness measurement

The conveyor belt, the most expensive element of the conveyor, during many years of operation is subject to complex wear processes related to the processes of its friction against the debris and structural elements, fatigue processes related to its bending on drums and idler sets, and repeated changes in tension when starting the conveyor and passing through the driving drums. The rate of wear processes is proportional to the time and intensity of the belt operation (the number of cycles of the belt loop around the conveyor). Another character, sudden and random, is the belt damage that occurs during the fall of the spoil onto the belt. Smaller lumps accelerate the friction processes by damaging the surface of the covers (rubber cracks, small tears) and intense friction caused by the acceleration of the excavated material. Larger lumps with energy exceeding the limit values cause the belt to puncture through, tearing out parts of the cover or rim, and damage to the core. As the belt wears off, the energy limits decrease (due to the decreasing belt thickness), which significantly accelerates the rate of its degradation processes. Identifying the rate of change in belt thickness over the entire surface is therefore critical to estimating the remaining life of the belt. The implementation of non-invasive diagnostics of conveyor belts to assess the condition of its core and the degree of abrasion allows for a significant reduction in belt operating costs. It can contribute to the extension of its life by reducing uneven wear and damage to the core. The decision to replace the belt at the right moment allows it to be regenerated. Regular control of the condition of damage and abrasions of the belt can prevent the occurrence of major failures and plan the moments of preventive replacements, which are much cheaper than expensive emergency replacements that can additionally cause enormous production losses. Prediction of the replacement of belt sections and their length allows for proper management of the budget allocated to repair and replacement of belts. Thanks to the obtained detailed information on the degree of belt wear, it is possible to take ad hoc corrective actions to extend the life of the belts and to predict the remaining time of its failure-free operation in the adopted replacement strategy. In Poland, at the Wrocław University of Science and Technology, as a result of many years of research work financed by the National Center for Research and Development, diagnostic systems have been developed to assess the technical condition of conveyor belts. One of such devices is the ultrasonic system for continuous belt thickness measurement, which is being built under the LIDER X project, called BeltSonic. This article presents the method of calibrating this device and the obtained measurement accuracy. Accurately measuring the thickness of the strip over its entire surface is critical to forecasting its remaining life.

A new paradigm for intelligent status detection of belt conveyors based on deep learning

All-round visual monitoring of conveyor belt running status is an important component of the belt conveyor operation and maintenance system. Intelligent analysis methods will help improve the detection in real-time and effectiveness and help make reasonable decisions. Aiming at the current situation that the existing technology cannot achieve integrated detection, this paper proposes a new conveyor belt running status monitoring paradigm based on deep learning for the first time. By grafting the segmentation network into the target detection network Yolov5, the integrated detection of various tasks, including fuzzy load measurement, deviation status detection, large foreign objects, and belt damage detection, etc., is realized in a real sense. Experiments show that the proposed method balances detection accuracy and speed well, with a detection accuracy of up to 97%, a segmentation accuracy of up to 100%, and a detection speed of up to 90 frames per second (FPS). The proposed integrated detection work is of great significance for improving the intelligent analysis capability of the operation and maintenance system, thereby ensuring the safe and efficient operation of the transportation system. Our source dataset is available at https://github.com/zhangzhangzhang1618/dataset_for_belt_conveyor/tree/master.

A centernet-based direct detection method for mining conveyer belt damage

A centernet-based direct detection method for mining conveyer belt damage

A Novel Denoising Approach Based on Improved Invertible Neural Networks for Real-Time Conveyor Belt Monitoring

Powerful belt conveyor is the crucial transportation equipment for coal mining. Keeping its stable operation is essential to ensure the coal mining efficiency. At present, damage detection systems based on machine vision are widely studied and deployed. Clear images are the foundation for successful damage detection. However, in the harsh environment of underground coal seam, it is difficult to capture high-quality images, which limits the performance of the machine vision-based techniques for the conveyor belt damage detection. To solve this issue, an image denoising and enhancement approach is proposed based on the invertible neural network (INN). First, a new res-block is introduced to improve the feature extraction ability of the proposed model. Then, to take advantage of the INN architecture, input images are split by the generative flow (GLOW) coupling block in the forward path and the noise among the high-frequency (HF) features is discarded in the inverse path. An improved trainable guided filter (TGF) is proposed to reconstruct the HF images. Besides, an improved multiple loss function is designed to keep detailed feature information and eliminate the effect of noise. The comparisons and experimental results demonstrate that the proposed denoising INN (DeINN) presents excellent image denoising ability and outperforms several existing popular methods. Finally, a real-world application demonstrates that the proposed DeINN satisfies the industrial requirements on belt damage detection.

ANALISIS PERAWATAN BELT MENGGUNAKAN METODE RELIABILITY CENTERED MAINTENANCE GUNA MENINGKATKAN PRODUKTIVITAS MESIN ATLANTIC ZEISER DI PT. WKI

M aintenance functions as monitoring, and maintaining company facilities , equipment , and work facilities by designing , managing , handling , and inspecting work to ensure functionality during uptime and minimize dowtime caused by damage or repairs. T his research is used to plan machine maintenance using the Reliability Centered Maintenance (RCM) method. The purpose of this research is to plan Preventive Maintenance activities in order to improve machine reliabilility and also maintain the smooth production process . From the fishbone diagram, it is known that the belt damage on the Atlantic Zeiser engine is due to four catagories, namely Material, Method, Man, and Enviroment. The belt is often used for the production process, but selection of the belt material with good quality according to spesifications . Based on the result of the calculation of the Realibility parameter, it is obtained from the Realibility parameter that the means time between failure data, Mean Time Between Failure (MTBF) of the lowest belt component is 125 hours and Realibility is R(t) the lowest 93,801%. From the Maintainability parameter, the data obtained are the longest average maintenance time (A) for the suction Fan, which is 5,750 hours and Mean Time Between Maintenance (MTBM) is 214,000 hours. From the Availibility parameter, the data obtained are Inharen availibility (Ai) and Achieved Availibility (Aa) namely belt is 97,737% and 96,423% . Keywords : Preventive Maintenance, Belt, Reliability Centered Maintenance (RCM), Fishbone

Yolox-BTFPN: An anchor-free conveyor belt damage detector with a biased feature extraction network

Improving the reliability and generalization of the conveyor belt damage detection method is crucial and challenging for its industrial application. In this study, a novel vision-based detector named Yolox-BTFPN is proposed. First, a biased texture feature pyramid network (BTFPN), which focuses on the texture feature of the conveyor belt is designed to more accurately capture the damage characteristic. Then the anchor-free decoupled detection heads are utilized to eliminate the hyperparameters design of anchors. Finally, the simplified optimal transport assignment (SimOTA) strategy is employed to solve the problem of samples imbalance and assignment of ambiguous feature points. The experimental results indicate that the proposed method has better reliability and faster convergence performance compared to the state-of-the-art methods. It was found that the mAPs of Yolox-BTFPN are 98.45% and 94.37% on our two test datasets, and that the Epoch, Params and FLOPs are 41, 7.9 M and 12.37 G, respectively.

Analysis of Belt Conveyor Damage in Coal Feeder Units 1-2 Using the Fault Tree Analysis (FTA) Method at PT. PLN (Persero) UPK Nagan Raya 2x110 MW


 
 
 
 This research focuses on conveyor belts on coal feeder units 1-2 located in PT. PLN PLTU Nagan Raya. Belt conveyor is a tool used to move coal from coal feeder to boiler or kiln, belts from conveyor belts are made of various types of materials such as plastic, rubber, leather, metal or other materials depending on the type and nature of the material transported. The purpose of this study is to find out the root cause of the problem of conveyor belt damage in coal feeder units 1-2. The method used is the FTA (Fault Tree Analysis) method and provides a proposed improvement with the 5W + 1H method (what, why, who, when, where and how). Data collection methods are carried out in two ways, namely library studies and elevation directly into the field through interviews with the company's management. The results showed that the better the quality of coal used, the lower the number of damage that occurs on the conveyor belt.
 
 
 

Damage Detection Method of Mining Conveyor Belt Based on Deep Learning

The belt conveyor is one of the key equipment in coal mining. Timely identification of belt damage is necessary to ensure coal mine safety production and reduce economic losses. In order to meet the needs of practical application, a new method of belt damage identification is proposed by fusing the temporal and spatial features of the image and directly using the on-site monitoring video image. Based on the deep learning network structure, an end-to-end belt damage detection model is designed. An improved attention mechanism is designed and introduced into the image spatial feature extraction model to solve the problem of complex background in belt surveillance video. In order to reduce the influence of reflection and shadow on recognition accuracy, a method of extracting temporal features of continuous multi frame video images using Temporal Convolutional Networks (TCN) is designed. The experimental results show that the accuracy of the method for belt damage recognition by fusing the temporal and spatial features of the image is more than 20% higher than that of the method for extracting the spatial features of the image alone, which shows that our proposed method is suitable for belt damage detection directly using the on-site belt monitoring video and is practical.

Conveyor belt damage detection method based on improved YOLOv4

Conveyor belt damage detection method based on improved YOLOv4

Rotary Machines Fault Diagnosis based on Principal Component Analysis

Rotating machines are commonly used in industrial applications. Mechanical faults such as rotor unbalance, shaft misalignment, pulley misalignment, structural looseness, and bearing faults leading to unplanned shutdown based on the severity of these faults. The condition monitoring technique based on vibration analysis has the potential to detect and diagnose a great number of early stage faults. However, some mechanical faults have correlated vibration features leading to ambiguous diagnosis to identify and distinguish these faults. In this paper, a proposed method based on the Principal Component Analysis (PCA) is presented to produce uncorrelated Principal Components (PCs) to identify the healthy and different faulty cases. A test rig was prepared to simulate a group of mechanical faults such as rotor unbalance, pulley misalignment, belt damage, combined unbalance with pulley misalignment, and combined unbalance with belt damage. The conventional vibration measurements were collected for each case and their features were extracted and used to produce the equivalent PCs. It was found that the produced uncorrelated PCs have the superior to distinguish the majority of simulated faults which have correlated vibration features as presented in the rest of paper.