Bing Li

Weakly textured closed pipelines—including energy and buried drainage conduits—require timely inspection of cracks, corrosion, joints and related defects. Weak illumination, sparse texture and repetitive cylindrical geometry degrade visual odometry and RGB-D SLAM, especially along the pipe axis, while image-level detectors rarely provide the axial distance, circumferential angle and pipe-frame 3D coordinates needed for maintenance. This paper presents the Structure-Constrained Pipe Joint Estimator (SC-PipeJE), an RGB-D framework that jointly estimates robot poses and 3D damage locations under weak texture. SC-PipeJE improves YOLOv8-seg for structural landmarks and damage masks, fits cylinders and centerlines from local point clouds, and optimizes a sliding window that couples RGB-D odometry with continuous geometric residuals, discrete landmarks and multi-frame damage factors so that damage observations also refine pose. On a primary RGB-D corpus of 5468 annotated frames and multi-structure sequences, the detector reaches mAP@0.5:0.95 of 0.7194 and Mask AP of 0.6980. Absolute trajectory error is 0.0343 m (67.6% lower than RTAB-Map under the same RGB-D protocol), and multi-frame damage localization yields 32.88 mm mean 3D error (46.6% lower than single-frame back-projection) at about 20 FPS. Quantitative pose and 3D damage results are reported on the laboratory RGB-D Corpus A; a complementary CCTV subset (Corpus B) is used only for qualitative appearance stress checks and is not mixed into the quantitative protocol. Ablation and robustness studies show that continuous geometry, discrete landmarks and multi-frame damage factors provide complementary observability when appearance cues are unreliable.

Shaft flexibility in a multi-support bearing-rotor system simultaneously alters the journal attitudes at multiple supports, thereby redistributing the hydrodynamic support conditions along the shaft. This study investigates this system-level transmission in a three-disk, four-support bearing-rotor system subjected to end loading. The eccentricity and inclination states of all four journals are determined simultaneously through a coupled equilibrium solution of a Timoshenko beam structural model and a finite-length Reynolds lubrication model, and the resulting load- and speed-dependent oil-film coefficients are introduced into a finite-element rotordynamic model. End loading produces a strongly nonuniform support response: at 3000 rpm, the stiffness and damping traces of the loaded-end support J ₄ increase by 4298.0 % and 293.6 %, respectively, and at 2884 rpm its direct-stiffness trace reaches approximately 47 times the initial value. Continuous modal-branch tracking shows, however, that the neighboring low-order FW and BW synchronous branches change by only −0.00037 % and +0.00039 %, whereas the higher-order H1 and H2 branches change by +0.0140 % and +0.1477 %, respectively. The contrast is explained by the markedly different generalized participation of the bearing locations in the corresponding modal branches. These results show that the global dynamic consequence of local oil-film strengthening is governed by hydrodynamic support redistribution and modal participation at the support locations rather than by the magnitude of the local bearing-stiffness change alone.

To obtain discrete element method simulation parameters for cohesive soil during the scrapping process of loaders, this paper calibrates parameters of cohesive soil with different moisture contents based on the Hertz-Mindlin with Johnson-Kendall-Roberts (JKR) Cohesion contact model in Experts in Discrete Element Modeling (EDEM). First, five cohesive soil samples with different moisture contents were prepared. By combining vibration sieving, the inclined plane method, and angle of repose experiments, the measured data ranges of particle size distribution, soil-steel friction coefficient, and angle of repose were obtained. Secondly, the JKR V2 adhesion model was constructed in EDEM. Significant parameters were screened using the Plackett-Burman test. Finally, the response surface analysis range was determined by integrating climbing experiments, and a quadratic regression model was established through Box-Behnken design to optimize parameter combinations. Furthermore, a Particle Swarm Optimization (PSO) algorithm was introduced for single-objective optimization of the angle of repose. The experimental results show that with the increase of moisture content, the angle of repose increases from 30.83° to 37.13°, and the significant parameters are JKR surface energy, soil-soil restitution coefficient, and static friction coefficient. The simulation error of the PSO algorithm is reduced from the maximum 3.38% in the response surface method to within 2.2%. This study provides a high-precision parameterization method for DEM modeling of cohesive soil, offering references for establishing DEM simulations of loaders scraping cohesive soil.

To address the significant numerical simulation errors that occur when a loader shovels crushed stone soil, due to the lack of generality and practicality in the calibration of discrete element parameters under varying moisture contents, this paper aims to improve the accuracy of these parameters by conducting calibration experiments on crushed stone soil. Under actual working conditions with moisture content ranging from 0% to 20%, six types of crushed stone soil with different moisture levels were prepared. A moisture content-angle of repose model was established using the cylinder lifting method, achieving a correlation coefficient of 0.9944. The Hertz-Mindlin with JKR contact model was selected in EDEM. The test determined the range of relevant contact parameters and, through Plackett–Burman screening, identified four significant discrete element parameters for crushed stone soil: soil rolling friction coefficient, soil collision recovery coefficient, soil-crushed stone JKR surface energy, and soil JKR surface energy. An angle of repose-discrete element model was developed using the steepest ascent method and Box-Behnken design, with a coefficient of determination (R ² ) of 0.9958, indicating high model significance (P < 0.01). By integrating the two models, a moisture content-discrete element model was constructed. The model was validated through a combination of angle of repose tests and uniaxial compression tests, with results showing a relative error of 4.9% or less, confirming its reliability. The calibrated moisture content-discrete element model can be applied to the numerical simulation of crushed stone soil with varying moisture contents, providing a theoretical foundation for resistance reduction and efficiency improvement during loader shoveling operations under crushed stone soil conditions.

This study introduces an enhanced ORB-SLAM3 algorithm to address the limitations of traditional visual SLAM systems in feature extraction and localization accuracy within the challenging terrains of open-pit mining environments. It also tackles the issue of sparse point cloud maps for mobile robot navigation. By combining point-line features with a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU), the algorithm improves the feature matching’s reliability, particularly in low-texture areas. The method integrates dense point cloud mapping and an octree structure, optimizing both navigation and path planning while reducing storage demands and improving query efficiency. The experimental results using the TUM dataset and conducting tests in a simulated open-pit mining environment show that the proposed algorithm reduces the absolute trajectory error by 44.33% and the relative trajectory error by 14.34% compared to the ORB-SLAM3. The algorithm generates high-precision dense point cloud maps and uses an octree structure for efficient 3D spatial representation. In simulated open-pit mining scenarios, the dense mapping outperforms at reconstructing complex terrains, especially in low-texture gravel and uneven surfaces. These results highlight the robustness and practical applicability of the algorithm in dynamic and challenging environments, such as open-pit mining.

When pouring concrete overhead, a pump truck boom’s vibration has a big effect on how accurately the concrete is poured. This is especially true during fixed-point pouring, where the boom’s vibration is likely to cause the pouring position to deviate, which lowers the quality of the construction. It is difficult to forecast the dynamic reaction of the pump truck boom in a construction setting because of the constantly shifting external factors (wind speed, pipeline stress during pumping, etc.), which makes it difficult to guarantee casting accuracy. This study suggests a non-random vibration analysis technique for pump truck booms based on the interval process theory in order to address this issue. A dynamic excitation analysis method based on rigid–discrete coupling is proposed, taking into account the response influence of the material characteristics in the transportation process. The pumping process of concrete materials in the conveying pipeline is simulated using discrete element simulation technology to determine the system’s stress conditions under pumping conditions. The dynamic response characteristics of the pump truck boom under operating conditions are revealed by using non-random vibration analysis with the mathematical model that has been created based on the particular specifications of the pump truck boom. This study employs the Newmark-β technique for numerical computation to solve the dynamic equations and characterize the displacement response envelope under uncertain system parameter settings. The experimental findings demonstrate that the suggested approach may accurately capture the upper and lower bounds of the boom dynamic response, offering a trustworthy way to assess the dynamic behavior while pumping. The technique can reliably predict the dynamic displacement boundary and control the casting position deviation within a predefined range by accurately predicting the dynamic displacement range of the pump truck’s boom end and efficiently constructing the displacement envelope under uncertain dynamic excitation. For numerical computation, use the Newmark-β algorithm. This outcome confirms the substantial enhancement of the proposed method regarding pouring precision in construction settings, offering a novel solution and technical guidance for vibration control in engineering projects.

The road environment of open-pit mines is complex and unstructured. Unmanned construction machinery driving faces huge challenges. Improving the accuracy and speed of obstacle detection during driving is of great significance to ensuring the safety of mine automation construction and improving the efficiency of overall unmanned operations. In view of the fact that the current obstacle detection algorithm struggles to strike a balance between high precision and real-time performance, and there are problems such as difficulty in model deployment or unsuitability for practical applications, a lightweight open-pit mine driving obstacle detection algorithm based on improved YOLOv8s is proposed, which is committed to improving the driving safety of unmanned engineering machinery in open-pit mines. In order to enhance the ability of the backbone to capture features, the idea of the guidance module (CGBlock) of contextual information is introduced to construct a new CGC2f module; the efficient squeeze excitation (ESE) attention mechanism is embedded in the feature fusion layer to make the model pay more attention to the channels containing important feature information; in order to enhance the model’s learning ability for obstacles of different sizes in the open-pit mine, a more suitable dynamic head network (DyHead) is used at the output end; in order to further improve real-time performance, the layer-based adaptive amplitude pruning (LAMP) score algorithm is used to prune redundant weight parameters. To verify the effectiveness of the algorithm in this paper, an experimental verification is carried out on the constructed open-pit mine driving obstacle dataset. The results show that compared with YOLOv8s, the mAP50 of this algorithm reaches 95.3%, the detection speed is increased by 40.2%, the model parameters are reduced by 71.2%, and the calculation amount is reduced by 73.7%. It meets the requirements of real-time and high-precision obstacle detection in open-pit mine driving and provides technical support for smart mine driving.

Gravel is the most frequently used material in infrastructure construction. However, the irregular shape of the gravel pile makes it challenging for the loader to predict a stable shoveling position, which can easily result in partial collapse or a complete landslide, thereby posing a serious threat to the equipment. In view of the imperfect method of determining the shoveling position of the pile by the current unmanned loader and the high hardware requirements for the deployment of the identification model, this paper first establishes a mathematical model of the loader, and preliminarily determines the influence of the concave and convex edges of the gravel pile on the shoveling position selection through discrete element joint simulation; secondly, the influence of the pile with different edge curvatures on the loader operation process is analyzed in the simulation software, and the radar map is used to further identify the superior position features; finally, Ghost Net is used as the backbone network, the RFB module is introduced into the Backbone, and the CBAM attention mechanism is integrated into the C3 module to identify the lightweight YOLOv5s shoveling position. Discrete element analysis and a lightweight network model were used in the above study to find the safest and most effective shoveling positions. During the test that mimicked how the loader would actually shovel, the number of parameters in the improved model was cut down to 32.5% of the original, the number of calculations was cut down to about 55.2% of the original, and the average accuracy of finding the shoveling position of the gravel pile reached 98%.

For the visual measurement of moving arm holes in complex working conditions, a histogram equalization algorithm can be used to improve image contrast. To lessen the problems of image brightness shift, image over-enhancement, and gray-level merging that occur with the traditional histogram equalization algorithm, a dual histogram equalization algorithm based on adaptive image correction (AICHE) is proposed. To prevent luminance shifts from occurring during image equalization, the AICHE algorithm protects the average luminance of the input image by improving upon the Otsu algorithm, enabling it to split the histogram. Then, the AICHE algorithm uses the local grayscale correction algorithm to correct the grayscale to prevent the image over-enhancement and gray-level merging problems that arise with the traditional algorithm. It is experimentally verified that the AICHE algorithm can significantly improve the histogram segmentation effect and enhance the contrast and detail information while protecting the average brightness of the input image, and thus the image quality is significantly increased.

Bolted joints are widely used in aeroengine rotor systems to connect multiple components into an integrated structure and provide sufficient stiffness. The mechanical properties of a bolted joint have a significant effect on rotor dynamics. For modern aeroengine designs, the blade-tip clearance is gradually reduced to improve efficiency, which may lead to rubbing damage and affect safe operation. The mechanical properties of a bolted joint change significantly during the blade–casing rubbing process and influence the dynamic properties of the rotor system. Based on the finite element (FE) modeling method, a 15-node bolted joint rotor system model is established in this paper, in which the bolted joint is represented by a 2-node joint element, and the blade–casing rubbing force is considered. The Newmark method is used to solve the motion equations. The dynamic model is validated by comparing the frequency response characteristics for different numbers of blades with the results provided in other published studies. Based on the established model, the effects of the rotational speed, number of blades, and rubbing stiffness on the dynamic responses, normal rubbing forces, and bending stiffness of the bolted joint are evaluated by numerical simulation. The results show that the response amplitude and bending stiffness of the bolted joint change significantly under blade–casing rubbing faults, and the mean value of the vibration response deviates significantly from 0 as the number of blades increases. Meanwhile, the amplitude of the frequency component f_VC and the maximum value of the normal rubbing force also increase as the number of blades increases. The main contribution of this paper is the establishment of a new model for a bolted joint rotor system, considering the time-varying bending stiffness of the bolted joint and the blade–casing rub fault, comparing the simulation results to obtain some general results bridging the current research gap. Meanwhile, the numerical results in this paper can provide a cognitive basis for the blade–casing rubbing fault mechanism of a bolted joint rotor system under the influence of speed, number of blades, and rubbing stiffness. The nonlinear dynamic characteristics observed in the present paper can be applied to the blade–casing rubbing fault diagnosis of turbomachines.