Search Results (464)

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Object detection in complex industrial environments is prone to being affected by insufficient dynamic weighting of local and global features, as well as illumination variations and impurities. Moreover, existing models suffer from excessive model complexity, which directly impairs computational efficiency. To more accurately distinguish Chinese herbal materials with diverse morphologies, this paper proposes the MobileAttn module. Drawing on the idea of token representation in the Transformer architecture, this module extracts contextual information through global feature compression, fuses it with tokens to generate a spatial attention map, and realizes dynamic recalibration of convolutional features. This process enhances the feature weights of key semantic regions, suppresses redundant background information, and improves feature discriminability. To address illumination interference, brightness-aware weights are combined with dual-path (channel and spatial) attention for global control, dynamically reducing the impact of illumination; this component is named LightAttn. When Chinese herbal materials contain common industrial unknown impurities (e.g., small stones and weeds), an impurity detection auxiliary module, a post-processing step independent of the main detection network, is proposed. This module refines Non-Maximum Suppression (NMS) logic to distinguish target Chinese herbal materials from interfering impurities. Subsequently, it accurately locates and marks impurities on the conveyor belt, thereby achieving effective unknown impurity detection. Experimental results demonstrate that, compared with the original YOLOv11 on the Chinese herbal materials detection task, the optimized model achieves a 1.7% improvement in the overall mean Average Precision (mAP@0.5:0.95). On a per-class basis, gains are particularly pronounced for certain challenging high-aspect-ratio Chinese herbal materials. Prunella vulgaris and orange peel achieve respective AP improvements of 5.8% and 4.1%. Meanwhile, the model parameter count is reduced by 23.1% and the computational complexity by 20.3%. The F1-Score of the impurity detection results is 86.38%, verifying the effectiveness of the impurity detection auxiliary module. Full article

Real-time perception of coal–biomass blending during conveyor-belt transport remains challenging because of local aggregation, particle overlap, and illumination variation. In this study, a laboratory-scale conveyor-belt image dataset covering different coal mass fractions, illumination conditions, and particle sizes was constructed. Whole-image classification, cropped-ROI classification, direct regression, horizontal bounding box (HBB)-based detection, oriented bounding box (OBB)-based detection, and RT-DETR-L detection baselines were compared using YOLO-series and auxiliary models. Coal mass fraction was estimated using a frequency-weighted statistical strategy that converts frame-level predictions into continuous estimates. YOLOv8-cls achieved an average RMSE of 13.98 percentage points (pp), indicating the influence of background interference in whole-image classification. Among HBB models, YOLOv8m achieved the lowest mean RMSE of 6.10 pp but required higher computational cost. Compared with YOLOv8n, YOLOv8n-OBB reduced the average RMSE from 9.02 to 6.90 pp by providing a more compact material-region representation and reducing background redundancy. These results show that OBB representation improves the stability of lightweight models. The proposed method provides a feasible vision-based soft-sensing approach for online trend monitoring of coal–biomass blending under lightweight deployment. Full article

The article investigates damage in textile-reinforced rubber conveyor belts caused by impact loading. The study aims to evaluate how impact conditions and belt structural properties affect severe damage formation and to develop predictive models for identifying critical operating conditions. Damage assessment was performed using visual inspection and computed tomography (CT), with CT serving as a reference method due to its ability to detect internal defects in the load-bearing carcass. CT identified more severe damage cases than visual inspection, confirming its higher sensitivity. Experimental tests were carried out with impact heights between 0.8 and 2.6 m and impact weights from 50 to 100 kg. The results showed that impact energy is the dominant factor influencing damage formation, as higher impact heights and weights significantly increased the probability of severe damage. Belt structural characteristics also affected damage resistance, especially the thickness of the top cover, which reduced the risk of failure. To predict severe damage, Logistic Regression, Random Forest, and XGBoost models were applied, all achieving excellent performance (AUC > 0.95). Logistic Regression (AUC = 0.994) additionally enabled the estimation of damage probability and the identification of critical impact conditions. The proposed approach supports safer operating limits, risk assessment, and predictive maintenance in conveyor systems. Full article

To address the poor detection performance in low-light underground coal mine belt conveyors caused by information entropy degradation and high background noise, as well as the difficulty in multi-scale target extraction due to uneven entropy distribution, this paper proposes an efficient foreign object detection model named SIRI-YOLO based on an improved YOLOv11n architecture. First, a Self-Calibrating Illumination Network (SCINet) is introduced to restore image information entropy and enhance low-light adaptability. Second, the C2PSA module is enhanced to C2PSA-IRMB by incorporating an Inverted Residual Mobile Block (IRMB), improving multi-scale feature utilization and reducing ineffective entropy increase. Third, an improved Reparameterized Generalized Feature Pyramid Network (RepGFPN) is adopted to strengthen the fusion of high-level semantics and low-level spatial features, reducing information entropy loss during feature pyramid transfer. Finally, the Inner-MPDIoU loss function is introduced to replace CIoU, achieving more accurate entropy minimization from a KL divergence perspective. Experimental results on a dataset containing large coal chunks and anchor rods show that SIRI-YOLO achieves 92.8% mAP@50, 59.4% mAP@50:95, 89.5% precision, and 87.2% recall, with only 2.92M parameters and 70.01 FPS, outperforming mainstream YOLO models. Furthermore, on the public ExDark low-light dataset, SIRI-YOLO improves mAP@50 by 4.2% over YOLOv11n, demonstrating strong generalization across different low-light and complex scenarios. The proposed method effectively handles uneven illumination, scale variation, and complex backgrounds, offering a practical solution for coal mine safety through system entropy reduction. Full article

An X-ray fluorescence online analyzer was applied to the analysis of samples of known composition and concentration containing rare earth elements (REEs) and heavy metals (HMs), which were specially prepared by the authors (working samples). Reference samples were used for Th and U. The statistical parameters (detection limit, accuracy, and sensitivity) of the measurements of the spectra were calculated and a thorough assessment of the results was carried out. For large-volume samples, detection limits of 20–100 ppm for REEs and 10–140 ppm for HMs were achieved within 600 s. For thin-layer samples and similar geometries, detection limits for light and medium REEs improved to 3–20 ppm. The methodological possibilities for quantitative analysis of the REEs and HMs were examined and a rather simple approach with an easy implementation was developed. The method was tested in automatic measurements using concentrations in the range of 1000–4000 ppm, as a simulation of real-life measurements, and to determine the stability of the analyzer and the consistency of the results obtained. The results show that the online XRF analyzer can be applied for reliable detection and quantification of REEs and HMs at the ppm level. With these results, we are closer to obtaining results under conditions representative of those on real-world mining conveyor belts. Full article

To improve the efficiency, accuracy, and operational stability of postharvest potato tuber sorting in the presence of mineral impurities, mainly soil clods and stones, an intelligent sorting system for potato tubers and mineral impurities was designed and developed. The system employed YOLOv10n as the baseline network and incorporated a PSA module together with a dynamic blur augmentation strategy to establish a task-adapted detection model, termed YOLOv10n-PB. Rather than treating detection accuracy alone as the optimization objective, the proposed system jointly considered detection performance, inference-latency stability, temporal–spatial coordination, and pneumatic rejection reliability. In addition, a programmable logic controller and pneumatic actuators were integrated to enable online target identification and dynamic removal. Comparative experiments involving lightweight YOLO models and $L_{25}\left(5^3\right)$ orthogonal tests were conducted to evaluate the effects of conveyor belt speed, material spacing, and classification threshold on sorting performance. The results showed that YOLOv10n-PB achieved a mAP@0.5 of 98.9% on the test set. Among the investigated factors, conveyor belt speed had the greatest effect on overall sorting accuracy, followed by material spacing and classification threshold. Range analysis identified the optimal parameter combination as a conveyor belt speed of 0.2 m/s, a material spacing of 9 cm, and a classification threshold of 0.4. Validation experiments under these conditions yielded an overall sorting accuracy of 98.3%, a combined mineral-impurity removal accuracy of 98.3%, and a potato tuber false rejection rate of 1.7%. These results demonstrate the feasibility of the proposed system for accurate and stable automatic sorting of potato tubers and mineral impurities under postharvest operating conditions. Full article

Operational failures in continuous material-handling systems are usually evaluated through failure counts; however, failure frequency alone may underestimate the true operational burden when downtime severity is unevenly distributed across devices and fault mechanisms. This study develops an integrated statistical framework for analysing operational failures and downtime in a continuous material-handling and technological transport process. The empirical dataset consists of 6605 anonymised failure events recorded between 2017 and 2025, covering 108 monthly observations, three technological device categories, and 42 classified fault types. The methodology combines frequency–severity analysis, inferential testing, time-series forecasting, and cluster-based identification of monthly operating regimes. The results show a strong disproportionality between failure frequency and downtime burden. Conveyor belts accounted for 51.40% of all failures but generated 83.22% of total downtime, confirming their dominant role in system-level operational losses. Several fault types, including Belt Slip, Off-Track Belt, Tear, Motor Failure, and Transfer Chute, also exhibited high downtime severity despite lower occurrence frequency. Inferential testing confirmed statistically significant and operationally meaningful differences in downtime severity across machine categories, whereas the calendar month was not a significant determinant of monthly failure counts or total downtime. Among the candidate forecasting models, Seasonal and Trend decomposition using Loess combined with exponential smoothing (STL-ETS) achieved the best holdout performance for both failure counts and total downtime. Cluster analysis further identified six interpretable monthly operating regimes differing in failure intensity, downtime burden, equipment involvement, fault-type composition, and temporal growth dynamics. The study contributes to downtime-oriented maintenance analytics by demonstrating that operational risk should be assessed through combined frequency–severity and regime-based perspectives rather than through aggregate failure counts alone. Full article

Floating belt conveyor routes consisting of serially arranged belt conveyors, the end parts of which are mechanically attached to floating bodies, are designed for the continuous transport of extracted granular materials from water. This paper deals with the analytical determination of the position of the centre of gravity of the buoyancy force, the coordinates of which change depending on the longitudinal deflection of the floating body from the equilibrium state, which acts as a supporting element of individual conveyor belts. Analysis of the individual phases of deflection of the floating body, consisting of a pair of floats with a circular cross-section, shows that the complete submergence of one of the floats occurs at a higher value of the angle of inclination in the case when the floats are initially submerged under the surface to exactly half their diameter. On the realized experimental device, the buoyancy force was detected using strain gauges during the deflection of the floating body from the equilibrium position for three defined levels of immersion. The floating body of the experimental device consists of a pair of floats with a circular cross-section with a diameter of 80 mm. The output is a structured methodological procedure for determining the position of the centre of gravity of the displacement (centre of buoyancy) of a floating body when it deviates from the equilibrium position and a methodology for calculating the stability arm, which is a key parameter for assessing the buoyancy and stability of the body. On the basis of the laboratory measurements, the magnitude of the buoyancy force can be quantified as a function of the immersion depth of the floating body. It was found that the buoyancy force remains constant when the body deflects only when the immersion corresponds to half the diameter of a float with a circular cross-section. If the depth of the immersion is less than the radius of the float, the buoyancy force increases during deflection; however, if the immersion is greater than the radius of the float, the buoyancy force decreases. Full article

Accurate contour extraction of aggregate particles from conveyor-belt depth maps is essential for downstream particle counting and size measurement, yet industrial depth data often contains weak discontinuities, missing values, and speckle-like noise. We propose a task-specific geometry-aware contour extraction framework that combines a compact SegFormer encoder with depth-derived priors, a lightweight local branch, edge-prior gated fusion, and full-resolution residual refinement. The input representation consists of normalized depth, Sobel gradient magnitude, and the absolute Laplacian response. On AGG_FULLDATA, the method achieves Optimal Dataset Scale (ODS), Optimal Image Scale (OIS), and Average Precision (AP) values of 0.9607/0.9716/0.9683 under the primary tolerance-based protocol ($tol=1$), while retaining an ODS of 0.6476 under strict pixel-exact matching. On External130, a test-only split collected under altered operating conditions using the same sensor, it reaches 0.9580/0.9734/0.9683 without retraining and consistently outperforms the MiT-only baseline. A rigid-object repeatability study based on 30 raw PLY scans shows a mean boundary deviation of 0.335 px, a within-1 px correspondence rate of 97.1%, and a coefficient of variation (CV) of equivalent diameter below 1%, supporting the practical meaning of $tol=1$. The full pipeline runs at 48.9 frames per second (FPS) with 3.71 M parameters on an NVIDIA GeForce RTX 4060 GPU. Broader robustness to separately controlled operating factors, environmental disturbances, and cross-device settings still requires validation. Full article

This study develops a stochastic degradation-based reliability framework for mechanical systems subject to interacting operational stresses and imperfect maintenance. The degradation dynamics are formulated in cumulative damage space and modeled using a geometric Itô diffusion process, in which the drift term incorporates a multiplicative degradation kernel representing the combined influence of load, speed, misalignment, and environmental exposure. Imperfect maintenance is represented through a continuous attenuation functional embedded within the drift structure, allowing maintenance actions to reduce degradation growth without restoring the system to an as-good-as-new condition. Using a logarithmic transformation, the multiplicative stochastic differential equation is converted into an additive diffusion process, enabling analytical treatment via Itô’s lemma. A closed-form reliability expression is then obtained through first-passage analysis, yielding a lognormal survival function governed directly by the degradation dynamics. Numerical evaluation demonstrates physically consistent wear-out behavior and confirms the stability of the derived reliability formulation. The model further enables reliability-based maintenance optimization through preventive replacement analysis. Sensitivity results indicate that system reliability is strongly influenced by the degradation growth parameter governing the stochastic drift. The proposed framework provides a mathematically tractable connection between stochastic degradation modeling, reliability theory, and maintenance optimization. Beyond its application to conveyor belt systems, the formulation offers a general analytical structure for reliability assessment of degrading engineering systems governed by multiplicative stochastic dynamics. Full article

Linear assets (LAs), such as conveyor systems, road networks, pipelines, and power transmission lines, are typically maintained through localized, segment-based interventions. While such approaches effectively address spatially heterogeneous degradation, they often neglect the system-level consequences of repeated local actions. In particular, improvements in segment condition may be accompanied by increased structural complexity, leading to reduced reliability and higher lifecycle costs. This paper proposes a unified engineering framework that integrates segment-level condition assessment with system-level structural effects. The framework is based on a dual representation of asset condition, distinguishing between material state (MS) and structural state (SS), which correspond to material aging (MA) and structural aging (SA), respectively. A key contribution is the introduction of the fragmentation penalty (FP), capturing the negative impact of increasing segmentation and interface density on system performance. The framework incorporates multi-threshold decision logic, enabling differentiation between operational, refurbishment, and replacement regimes, and interprets maintenance actions as transformations affecting both condition and structure. A formal model is developed to represent the asset as a dynamic system of segments and interfaces. It provides a basis for future empirical calibration and structure-aware optimization. Although the model is developed using conveyor belt loops as a reference case, its broader relevance is discussed for other classes of linear assets with repeated local intervention and evolving structural heterogeneity. A simple worked example is included to demonstrate the operational meaning of the proposed fragmentation-aware perspective. The results show that maintenance decisions may change when structural side effects are considered together with local condition improvement, and they provide a basis for future empirical calibration and structure-aware optimization of maintenance strategies. Full article

This study demonstrates how structured algorithmic optimization can enhance intelligent visual measurement systems in mining engineering. Real-time visual measurement of mining conveyor belts is critical for operational safety, yet achieving high-precision anomaly detection under complex environmental conditions remains a significant challenge. Conventional approaches often struggle to balance detection accuracy with computational efficiency due to inefficient feature representation and optimization strategies. To address this, this study proposes FDSE-DETR, a lightweight end-to-end framework designed for real-time anomaly evaluation. The framework eliminates Non-Maximum Suppression (NMS) to streamline inference. Specifically, this study introduces a deformation-aware sampling mechanism to enhance feature representation of irregular hazards, alongside a cost-effective multi-scale aggregation strategy to preserve fine cues within strict device budgets. Furthermore, a reformulated loss objective is developed to rebalance hard samples under severe class imbalance, improving the detection confidence. Experimental results on mining conveyor belt foreign object datasets show a 4.5% improvement in mean average precision (mAP), a 3.9% improvement in overall recall and a 22.5% reduction in computational cost, achieving 120.7 FPS. This study aims to address the problems of insufficient accuracy and low efficiency in real-time material flow measurements on mining conveyor belts under high-dust and low-illumination conditions. Full article

Steel-cord conveyor belts are critical equipment in mining operations, and surface damage can easily lead to safety incidents. Therefore, achieving efficient and accurate damage detection is of significant importance. This study investigates a conveyor belt damage identification method based on the YOLOv7 object detection framework. First, a dedicated dataset of conveyor belt damage is constructed, annotated, and augmented using geometric and pixel-level transformations to support model training. To meet real-time detection requirements, the following two lightweight models are proposed on the basis of YOLOv7: GSConv-YOLOv7, which reconstructs the backbone network using GSConv to reduce parameter size and computational cost, and MobileViTv3-YOLOv7, which replaces the original backbone with MobileViTv3. Experimental results show that GSConv-YOLOv7 achieves an mAP of 84.6%, while reducing parameters and computation by 17.2% and 16.2%, respectively, and improving detection speed by 16FPSs. To further enhance accuracy, the MPDIoU loss function is adopted in place of CIoU, improving convergence and bounding box regression performance. Building upon this, an LSKNet attention mechanism is integrated, and most convolutional layers are replaced with RFAConv, resulting in the proposed YOLOv7-GPLF model. This model achieves an mAP of 88.1%, with parameter size and computational cost of 33.7 M and 94.5 G, respectively, and an inference speed of 54 FPSs. The model thus delivers significantly improved detection performance while remaining lightweight, enabling fast and accurate identification of conveyor belt damage. Full article

Blockchain is a distributed database technology that enables immutable, verifiable data recording, properties that are useful for failure analysis processes requiring high data integrity and traceability. In conveyor belt failure analysis, there is a growing need for reliable management of experimentally obtained data, especially for long-term monitoring of operating and failure states. The presented article focuses on customizing the blockchain architecture to support recording and validating experimental data used in the failure analysis of rubber-textile conveyor belts in pipe conveyors. The proposed methodology integrates a private blockchain system as a layer for storing and validating raw measured data obtained during experiments. The system meets technical accuracy requirements and is defined as a private blockchain with a permissioned system, which uses the Proof of Authority consensus algorithm and is characterized by centrally managed administration. The prototype of the “LogBlock” application demonstrates the storage and validation of data in the form of plain text and compressed (.zip) files, providing robust protection against unauthorized data modifications, auditability, and resistance to unauthorized interference, while being adapted to the specific requirements of the analyzed technical system. Experimental results indicate the feasibility of the proposed blockchain system in storing, validating, and managing raw measurement data, processed data, metadata, and related source files throughout the failure analysis process. The achieved results confirm the system’s ability to identify unauthorized data modifications and ensure their traceability after entering the system. The implemented solution confirms the suitability of using blockchain as a support tool for technically oriented failure analysis applications of conveyor systems. Full article