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Vibration separation method for permanent magnet guideway irregularity measurement

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 223卷

作者： Luo, Yi Yuan, Yuhang Zeng, Peiyang Liu, Jianru Deng, Bin Deng, Zigang Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Sichuan Peoples R China

High-temperature superconducting (HTS) pinning maglev has the potential to achieve high operational speed while having a low maintenance cost, attracting a lot of attention. The HTS pinning maglev system realizes the levitation of the vehicle through the flux pinning characteristics between the high-temperature superconducting bulk and the magnetic field of the permanent magnet guideway (PMG). Thus, the profile of the magnetic field of the PMG can significantly affect the levitation status of the vehicle, which makes the PMG irregularity become one of the main excitation sources of the system. The magnetic induction intensity on the surface of the PMG can be used to characterize the PMG irregularity, but the non-contact dynamic measurement may produce large errors by the vibration of the measurement system during the process. In order to evaluate the PMG irregularity, the vibration component needs to be separated from the overall signal. In this paper, through the synchronous measurement of the Hall sensor and accelerometer, the vibration separation method of the PMG irregularity measurement is simplified to the optimal filter estimation. The particle swarm optimization (PSO) algorithm is used to calculate the parameters of the optimal filter based on the correlation coefficient criterion. The effectiveness of the proposed method is verified by the test. The separated signal is more accurate in both time domain and frequency domain for the characterization of PMG irregularity.

关键词： High-temperature superconducting pinning maglev Permanent magnet guideway irregularity Vibration separation Correlation coefficient particle swarm optimization algorithm

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Research on Intelligent optimization of Wellbore Trajectory in Complex Formation

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1364-1364页

作者： Gu, Haipeng Yan, Tie Wu, Yang Stat Northeast Petr Univ Sch Math Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ NEPU Sanya Offshore Oil & Gas Res Inst Sanya 572000 Peoples R China

Borehole trajectory optimization is a key issue in oil and gas drilling engineering. The traditional wellbore trajectory design method faces great challenges in optimizing the trajectory length and complexity, and it is difficult to meet the actual engineering requirements. In this paper, the three-stage wellbore trajectory optimization problem is studied, and a multi-objective optimization model including two objective functions of trajectory length and trajectory complexity is constructed. In this paper, an improved multi-objective particle swarm optimization algorithm is proposed, which combines the clustering strategy to improve the diversity of solutions, and enhances the local search ability and global convergence performance of the algorithm through the elite learning strategy. In order to verify the performance of the algorithm, comparative experiments were carried out using classical multi-objective benchmark functions. The results showed that the improved algorithm is superior to the traditional method in terms of diversity and convergence of solutions. Finally, the proposed algorithm was applied to the actual three-stage wellbore trajectory optimization problem. In summary, the research results of this paper provide theoretical support and engineering practice methods for wellbore trajectory optimization, and serve as an important reference for further improving the efficiency and quality of wellbore trajectory design.

关键词： wellbore trajectory multi-objective optimization particle swarm optimization algorithm elite learning strategies

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Integrated Control Strategy of Bearingless Switched Reluctance Motor Based on Active Disturbance Rejection Control

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Wang, Xuan Wang, Xilian Huang, Zhuliang Wang, Baohua Yang, Ruikang Beijing Jiaotong Univ Coll Elect Engn Beijing 100044 Peoples R China

Traditional proportion integration differentiation (PID) controller is difficult to solve the contradiction between response speed and overshoot, and its anti-interference ability is limited. To solve the problem, an integrated control strategy of bearingless switched reluctance motor based on active disturbance rejection control (ADRC) is proposed. A first-order speed ADRC controller and a second-order displacement ADRC controller are designed for direct instantaneous torque control and direct instantaneous force control system, respectively. With the integral time-weighted absolute error (ITAE) as the optimization objective, the particle swarm optimization algorithm is used to tune the main parameters of the ADRC controllers, overcoming the drawbacks of adjusting parameters manually, such as complex operation and time consumption. Simulations and experiments are carried out. The results demonstrate that the control strategy has superior dynamic and static response performance in both speed and displacement control and has good application prospects.

关键词： Control systems Rotors Torque Windings Force Motors Centralized control Switched reluctance motors Stator windings particle swarm optimization Active disturbance rejection control (ADRC) bearingless switched reluctance motor (BSRM) particle swarm optimization algorithm sharing suspension windings

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Design of impact ultrasonic penetrator and optimization of impact efficiency

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ADVANCES IN SPACE RESEARCH 2025年第8期75卷 6070-6088页

作者： Wang, Yinchao Yu, Ziming Yin, Zihao Zhang, Weiwei Zu, Lin Tao, Guanghong Yu, Suyang Shenyang Aerosp Univ Sch Mechatron Engn Shenyang 110136 Peoples R China Shenyang Aerosp Univ Key Lab Rapid Dev & Mfg Technol Aircraft Minist Educ Shenyang 110136 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China Deep Space Explorat Lab Hefei 230026 Peoples R China

This paper proposes, optimizes, and experimentally investigates a thread-clamped ultrasonic penetrator that uses a spring to induce forced vibration in the transducer, thereby enhancing drilling efficiency. The penetrator consists mainly of a transducer, a free mass, an elastic energy storage unit, a drilling tool, and a housing. The elastic energy storage unit is attached to the transducer's flange, permitting limited axial movement. When excited by a sinusoidal signal at a specific frequency, the transducer's front end generates high-frequency longitudinal vibrations that impact the free mass. Upon colliding with the drilling tool and the transducer subsequently, the elastic energy storage unit absorbs and utilizes this energy, optimizing the energy transfer process. This study designs the penetrator's structure, analyzes the motion curves of each component, and derives the kinetic energy curve of the drilling tool. A novel particle swarm optimization algorithm is employed to optimize the key parameters of the penetrator, verifying the optimization effect. The prototype was fabricated, and its vibration and output characteristics were tested. The results from rigorous testing clearly demonstrate a significant improvement in the penetrator's drilling efficiency after meticulous structural and parameter optimization. Both simulation and experimental results confirm the feasibility of the penetrator. (c) 2025 Published by Elsevier B.V. on behalf of COSPAR.

关键词： Piezoelectric actuator Kinetic optimization Energy amplification particle swarm optimization algorithm

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A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

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INFORMATION SCIENCES 2025年 686卷

作者： Karahasan, Ozlem Bas, Eren Egrioglu, Erol Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkiye

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

关键词： Simple recurrent artificial neural network Exponential smoothing Sine cosine optimization algorithm particle swarm optimization algorithm Stock exchange Forecasting

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Application of particle swarm optimisation algorithm incorporating frog-leaping algorithm in optimal scheduling for production management in manufacturing plant

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第3期19卷 1721-1735页

作者： Wu, Mingming Wuhu Univ Sch Automot Engn & Intelligent Mfg Wuhu 241000 Peoples R China

The goal of management optimization scheduling in manufacturing plants is to improve machining efficiency and reduce costs, which is one of the research hotspots in the current era. The study proposed a particle swarm optimization algorithm incorporating the frog-leaping algorithm, which combined the grouping mechanism with the global search to improve the search speed of the algorithm, and also incorporated the mutation and crossover ideas of the genetic algorithm. To enhance the machining efficiency while minimizing time and resource requirements, two buffering mechanisms were used for the machining process in this algorithm. The algorithm ultimately achieved the optimal solution of 1170 around the 10th generation, according to experiments, which reduced the maximum machining time by 22%, the average production cycle time by 23%, the machine utilization rate reached 69%, and the percentage of the optimal relative error was almost less than 4%. Additionally, the algorithm's average relative error fluctuation is less than that of the other algorithms, indicating that this algorithm is more stable. This result shows that the particle swarm optimization algorithm incorporating the frog hopping algorithm in this study has good practical value in optimizing the scheduling of production management in manufacturing workshops for improving the processing efficiency and reducing the cost, which is beneficial to the development of the manufacturing industry.

关键词： Job shop scheduling particle swarm optimization algorithm Frog-leaping algorithm Buffering technique Industrial revolution

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Experimental, theoretical and optimization studies on multimode vibration reduction of floating raft system based on NES cells

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OCEAN ENGINEERING 2024年第Part1期311卷

作者： Wang, Hong-Li Li, Meng Zeng, You-Cheng Ding, Hu Shanghai Univ Shanghai Frontier Sci Ctr Mechanoinformat Sch Mech & Engn Sci Shanghai Key Lab Mech Energy Engn Shanghai 200444 Peoples R China Shanghai Univ Shaoxing Inst Technol Shaoxing 312074 Peoples R China

Most of the studies on multimode vibration reduction of floating raft systems remain at the stage of theoretical analysis, while relevant experimental studies are rare. In this paper, an experimental setup for a scaled-down floating raft system is constructed. The vertical track nonlinear energy sink (TNES) cells are experimentally applied for the first time to suppress the multimode vibration of the floating raft system. To ensure the damping effect is not affected by additional mass, the TNES cells are always installed on the floating raft system. The TNES is able to achieve vibration suppression by allowing unrestricted movement of its vibrator. Two damping systems with different TNESs placement are proposed. Experimental modal analysis is performed on the floating raft structure to determine its response characteristics. Based on the results, different magnitudes of excitation force are applied to each order mode to effectively suppress the multimode vibration of the system. Meanwhile, the nonlinear system coupled with TNES cells is analyzed using the harmonic balance method (HBM) to obtain the approximate analytical solution. The system responses obtained from the analytical solution are compared with the experimental data results. The particle swarm optimization (PSO) algorithm is used to obtain the optimal parameters of the TNESs for vibration suppression. The results show that the multimodal vibration of the floating raft structure is effectively suppressed by the TNES cells in both experimental and theoretical analyses. The vibration suppression effect for all modes is significantly improved by the PSO algorithm. In conclusion, the research content of this paper provides a practical and feasible vibration suppression solution for the design of floating raft systems.

关键词： Vertical track nonlinear energy sink Floating raft system particle swarm optimization algorithm Vibration suppression

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A novel fractional multivariate grey prediction model for forecasting hydroelectricity consumption

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GREY SYSTEMS-THEORY AND APPLICATION 2024年第3期14卷 507-526页

作者： Li, Ye Ren, Hongtao Liu, Junjuan Henan Agr Univ Coll Informat & Management Sci Zhengzhou Peoples R China

PurposeThis study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting ***/methodology/approachThis paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The particle swarm optimization algorithm is then utilized to compute the model's hyperparameters. Subsequently, the proposed model is applied to forecast China's hydroelectricity consumption and is compared with other models for *** derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model's effectiveness in handling complex and nonlinear data, emphasizing its practical *** implicationsThis paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China's hydroelectricity resource development scheduling and ***/valueThe primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

关键词： Grey multivariate prediction model FMGM(1, N, alpha) model Hydroelectricity consumption particle swarm optimization algorithm

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Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO

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ENERGIES 2024年第21期17卷 5502页

作者： Qiu, Bo Zhang, Jian Yang, Yun Qin, Guangyuan Zhou, Zhongyi Ying, Cunrui Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China

Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov-Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the particle swarm optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels.

关键词： oil well production prediction GRU-KAN model particle swarm optimization algorithm MissForest

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Machine learning-based improved plastic stress distribution method for strength prediction of CFST members

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STRUCTURES 2025年 74卷

作者： Zhou, Zheng Yang, Lifu Zhou, Xuhong Shi, Caijun Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China

The plastic stress distribution (PSD) method is used as the primary method in typical design codes for calculating the strength of concrete-filled steel tubular (CFST) members. However, the PSD method can only be applied to members with limited ranges of material strengths and cross-sectional slenderness ratios, and the calculation methods are lacking or relatively complicated for members beyond the limitations. In this work, an improved PSD method is proposed for the strength prediction of CFST members with different cross-sectional slenderness ratios and material strengths, by using the concrete strength factor fi1 to consider the possible compressive strength increase or reduction of the concrete due to the confinement or local buckling from the steel tube and the steel strength factor fi2 to consider the potential compressive strength reduction of the steel tube due to local buckling. The direct inverse analysis using the improved particle swarm optimization algorithm is then conducted to identify the strength factors fi1 and fi2 based on the extensive experimental results. Machine learning algorithms are finally explored to train the rules between the strength factors (fi1 and fi2) and the member properties including the diameter or width and depth, wall thickness, and material strength, and the predicted results are in good agreement with the experimental results. These results further specify the accurate plastic stress distribution, and provide a strong justification for possible changes to the design codes to expand the use of the PSD method to all types of CFST members.

关键词： Concrete-filled steel tubes Plastic stress distribution method Direct inverse analysis particle swarm optimization algorithm Machine learning algorithm

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