Maintenance, Reliability and Condition Monitoring
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Design of a composite repetitive controller for grid-connected inverters with a notch filter
Full text linkTo address the resonance peak issue of LCL (Inductor capacitor inductor) grid-connected inverters at the resonant frequency and reduce system losses caused by passive damping, this paper proposes a novel plug-in composite repetitive controller based on an active damping strategy utilizing a notch filter, along with detailed parameter design for the controller. Simulation results demonstrate that the notch filter-based repetitive controller maintains high gain at the fundamental frequency while exhibiting rapid gain attenuation at higher frequencies. Since the harmonic content of the inverter system is predominantly concentrated in the low-frequency range, the controller achieves excellent harmonic suppression performance within the low-frequency region. The low gain at high frequencies enhances system stability. Compared with conventional repetitive controllers, the proposed controller adopts a low-loss notch filter damping method, preserves the superior harmonic suppression capability (the grid current harmonic is reduced by 1.37 %), and improves system stability
Dual-aggregation feature compilation network for urban traffic object detection and pedestrian pose estimation
Full text linkWith the increasing complexity of urban transportation systems, object detection and pedestrian pose estimation play a crucial role in intelligent traffic management and autonomous driving technologies. However, existing feature compilation networks are often designed for single tasks and perform poorly in small object detection and high occlusion pedestrian pose estimation tasks. To address the above issues, this paper proposes an efficient feature compilation network with Dual-aggregation, compatible with both object detection and pedestrian pose estimation. This network adopts a transfer learning-like training strategy in the feature extraction network, using a micro-complex convolution structure during training to bring the training results as close as possible to global optimization. During inference, a single simple convolution is used to inherit the training results, improving the model performance while ensuring model lightweight. The feature fusion employs a global-local dual aggregation structure, simultaneously considering multi-scale global and local features. Additionally, we use multiple public datasets to create a hybrid dataset under various scenarios to validate the robustness of the network. The experiments show that the proposed method outperforms existing mainstream methods in detection accuracy for urban object detection and pedestrian pose estimation tasks, especially demonstrating better robustness in complex urban traffic scenarios
Research on converter transformer state early warning system based on confidence ratio-EEMD and multi-cascade network
Full text linkAiming at the problems of poor prediction effect of non-stationary parameters and single warning rule of UHV converter transformer, this study proposes an intelligent warning method based on decomposition-multi-level cascade network and fuzzy set. Firstly, the integrated empirical modal decomposition technique is used to decompose the target parameter sequence into multiple sub-sequences, and the effective components are screened by the DPR-KLdiv confidence ratio, which is dynamically grouped and reconstructed to form a multilevel feature input; and the multilevel cascade network is constructed by combining multi-device parameters to make the time series prediction. The fuzzy function is further introduced to establish the parameter state mapping rules to expand the alarm triggering conditions. The experiments are validated by actual equipment data, and the local discharge signals of different defects are detected by ultra-high frequency method to enhance the generalization ability of the parameters. The results show that the average RMSE and MAE of this method are 23.21 and 18.47 respectively under the hours step prediction, and the accuracy of the warning is over 90 %, which effectively improves the accuracy of non-smooth parameter prediction and the flexibility of the warning decision
Study on stability of shaft surrounding rock under adjacent shafts mining disturbance in underground mine
Full text linkThe stability of mine shafts is crucial for safe production in underground mining. To elucidate the impact of adjacent shaft mining disturbance on shaft structural stability in underground mines, this study takes a Manganese Mine in Guizhou, China as a case study. A refined three-dimensional model at engineering scale was established by using the Rhino-FLAC3D coupled modeling method. This model can numerically simulate the mining of ore bodies at different stages of mining. The displacement, stress distribution, and plastic zone in both strata and shaft surrounding rock were systematically analyzed to reveal the response laws of shaft surrounding rock under mining disturbance. The results showed that during the first and second mining phases, no measurable deformation occurred in the surrounding rock of the main shaft, auxiliary shaft, or ventilation shaft. During the third mining phase, the maximum displacement observed in these shafts’ surrounding rock reached 0.048 m, which remains within the stability threshold of rock masses according to evaluation criteria. Regression analysis was conducted on the monitoring displacement of three mining stages, and power function fitting curves were obtained. Plastic zones (20-30 m range) developed along the periphery of goaf areas, maintaining a safe distance of 45-55 m from adjacent shafts. A stress gradient formed around goaf areas, with tension stresses up to 1.33 MPa exceeding the ultimate tension strength of roof strata. There was potential tension failure in the roof strata of the goaf. Although mining disturbance effects on main and auxiliary shafts intensified with depth progression, no substantial structural impacts were observed. This confirms that all shaft structures can maintain stability during operational phases. The findings provide theoretical guidance for shaft stability control in deep mining operations
Kinematic and force analysis of a scissor lift mechanism
Full text linkA scissor lift design was developed with a load capacity of 100 kg and a lifting height of 1 m. The platform lifting mechanism is actuated by a traction electric motor via rollers moving along the guides. A calculation model of the scissor lift was created, resulting in a statically indeterminate system. Support reactions and an actuating force were determined depending on the platform lifting height. The analytical results showed that the actuating force increases nonlinearly during platform lifting, ranging from 1.674 kN to 6.45 kN, while the actuator rod stroke is 441 mm. Similarly, the simulation conducted using SolidWorks Motion yielded the actuating force in the range of 1.62 kN-6.5 kN and the rod stroke of 443.5 mm. The study established the patterns of variation of the main kinematic and force parameters of the scissor lift, which exhibit nonlinear characteristics. A piecewise linear variation of the actuating force was synthesized to ensure a trapezoidal motion profile of the platform. This type of motion profile was selected to provide comfortable and safe movement for people, particularly those with disabilities. The strength of the main structural elements of the scissor lift, namely levers, traction crossbar, guides and rollers, was ensured
Fuzzy dynamic self-tuning based linear active disturbance rejection control for PMSM speed control
Full text linkIn this paper, a novel control approach, namely fuzzy dynamic self-tuning-based linear active disturbance rejection control (FDS-LADRC), is proposed for the speed loop system of permanent magnet synchronous motors (PMSMs). Specifically, a control framework based on the linear active disturbance rejection control (LADRC) is presented. Fuzzy dynamic self-regulators are developed to enable simultaneous adaptive adjustments of both the controller and observer parameters. Additionally, the stability analysis is provided. A series of numerical simulations demonstrates that FDS-LADRC achieves superior adaptivity, transient performance, disturbance rejection capability, and anti-noise ability under various operating conditions. For instance, during no-load startup, compared with the traditional LADRC, nonlinear active disturbance rejection control (ADRC), a variant of FDS-LADRC named IT2FDS which utilizes interval type-2 fuzzy systems as fuzzy dynamic self-regulators, a state-of-the-art fractional-order ADRC with fuzzy self-tuning (FSFOADRC), and sliding mode control (SMC), FDS-LADRC reduces overshoot by 10.82 %, 13.55 %, 7.36 %, 5.53 %, and 3.94 %, respectively, and shortens settling time by 0.0132 s, 0.0076 s, 0.0139 s, 0.0009 s, and 0.0156 s, respectively. Finally, corresponding real-world experiments are conducted to validate the effectiveness and superiority of FDS-LADRC
Experimental method for determining the vibrodynamic state of embankments on high-speed railways
Full text linkThe article presents modern methods for reinforcing the embankment in the zone of the interface between the coastal bridge piers and the earth bed of the high-speed railway section. It has been established that as a result of driving reinforced concrete piles into the railway embankment, the natural vibrations of the earthwork decrease by up to 15 %. A frequency equal to the frequency of vibrations arising from the speed of high-speed railways with the help of vibrators on models of the earth bed for determining the amplitude-frequency characteristics of various design points has been created and the values of this frequency have been processed by fixing them with the help of seismometric sensors SM-3 in all design points. A significant decrease of shear at the main site after driving of reinforced concrete piles and approaching of this value to microseismic value based on the values of sensors located at the main site and at a distance of 1.5 m from the foundation is determined. It has been established that by driving reinforced concrete piles into the railway embankment, the vertical settlement of the earthwork decreases by 33 % and 50 % depending on the soil type. Also, the methodology of experimentation for the study of vibrations of the earth bed piled from different soils on high-speed railroads is given
Small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN)
Full text linkIn actual industrial environments, equipment failures often occur sporadically during operation, resulting in insufficient labeled data for training. To address the issues of difficult feature extraction and poor generalization caused by insufficient data in small-sample fault diagnosis, a small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN) is proposed. Firstly, dual convolution kernels are employed to extract signal features, with the large kernel capturing low-frequency components and the small kernel extracting additional features to enhance the network's expressiveness. Secondly, the channel attention mechanism adaptively adjusts the feature responses of each channel, enabling the network to focus on the most informative and relevant features while suppressing unimportant ones. Finally, the Temporal Convolutional Network (TCN) is utilized to capture dependency features within long time series, further improving the model's ability to process sequential data. Experimental results demonstrate that the DCK-CAM-TCN model significantly outperforms traditional Convolutional Neural Networks (CNNs) and other comparison models in small-sample scenarios. The results indicate the significant advantages of the DCK-CAM-TCN model in small-sample fault diagnosis
Finite element analysis and vibration simulation of electromagnetic imaging sensor housing based on ANSYS
Full text linkMining sensors work in harsh environments and are subject to complex vibrations. Its internal structure is prone to strength failure or fatigue damage. This paper focuses on the structural design of the front discharge and receiver housing inside the electromagnetic imaging sensor for coal-rock demarcation detection. Static analysis, modal analysis, and random vibration simulation were performed using ANSYS Workbench software to verify its reliability and strength in mining. In the static analysis, the thickness of the designed housing is 2 mm. The maximum equivalent elastic strain after applying a pressure of 0.5 MPa to the housing is 0.133 %, much less than the criterion of material fracture strain. This proves that it has excellent strength properties and will not experience strength failure. Modal analysis shows that the first-order intrinsic frequency of the housing is 3298.7 Hz. It is much higher than the vibration frequency in the actual working environment, which can effectively avoid resonance and improve the reliability of the structure. Random vibration simulation results show that the housing's maximum equivalent force and displacement are within the safe range, and the impact on the structural performance is negligible. These results provide a theoretical basis for the optimal design of the sensor housing and its application in complex vibration environments
Multi-mode frequency response prediction of milling robot based on feature transferring with small sample sets
Full text linkIndustrial robots are increasingly used in machining due to their cost-effectiveness and larger work envelopes. However, their relatively low structural stiffness makes them vulnerable to machining chatter, which negatively impacts both process stability and surface quality. Accurate prediction of the multi-mode frequency response function (FRF) of robotic milling systems is crucial to ensure process stability. Traditional FRF prediction approaches, however, often require extensive experimental procedures, are complex, and are time-consuming. To address these challenges, this study proposes an innovative feature-transfer-based method for multi-mode FRF prediction in milling robots, requiring only a minimal set of impact tests. The method organizes measured FRFs into second-order complex tensors, facilitating the transfer of features between different postures. Multi-mode parameters of the tool-tip FRF under the source posture are extracted using the least-squares complex exponential (LSCE) method and assembled into a label vector. A complex-kernel extreme learning machine with augmented inputs (CKELM-AI) is then trained to predict the tool-tip FRF under the target posture. Additionally, a virtual sample generation strategy based on CKELM-AI and feature augmentation, including statistical, frequency, and time-frequency features, is applied to enhance prediction accuracy. Experimental validation on a milling robot demonstrates that the proposed method significantly improves both prediction efficiency and accuracy, establishing a new, more efficient approach for predicting multi-mode FRFs without the need for extensive testing