Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation EntropyFeng, Tao; Liu, Chun-sheng; Xu, Ao; Wang, Chao-hui; Wang, Feng-min; Liu, Xin; Su, Sen-tao
doi: 10.1088/1742-6596/2492/1/012010pmid: N/A
Aiming to extract efficiently the fault features of partial discharge in the process of fault diagnosis of power transformer, a method of combining Empirical Wavelet Transform (EWT) with Multiscale Permutation Entropy (MPE) is advanced to extract fault features of transformers partial discharge. Firstly, four different partial discharge pulse signals are analyzed by EWT method, and the fault signal is decomposed according to different frequency domain characteristics of the signal to obtain the intrinsic mode function (IMF) of the signal. Then, the calculated multi-scale permutation entropy of different IMFs to complete the fault feature extraction. Finally, the multi-scale entropy of the fault semaphore is used as the eigenvector of the Support Vector Machine (SVM) for glitch diagnosis, and the accurate systematization of the partial discharge semaphore of the transformer is realized. semaphore Compared with the Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD) feature extraction way, it shows that the raised EWT-MPE is more valid and accurate in diagnosing and analyzing transformer partial discharge faults, and the accuracy of fault classification 96.43%.
A data-driven approach to fault modeling and diagnosis of brake-by-wire systemMei, Mingming
doi: 10.1088/1742-6596/2492/1/012016pmid: N/A
E-Booster plays an important role in braking safety as the pressure source for vehicle hydraulic systems. According to the design feature of its series controller, the signals on key nodes, which can be directly measured, can be used as the data source for fault diagnosis. In this paper, a deep learning technique of residual 1-D CNN equipped with parallel structure is proposed for fault recognition and classification in a dynamic process. With the help of wavelet transform and probabilistic heat map, it is found that the phase current fault feature is distinct in the frequency domain. In contrast, the uniform demagnetization fault and pressure recession fault are more obvious in the time domain. Therefore, the parallel network structure with a wide and narrow convolutional kernel is used, which can handle multiple complex fault features simultaneously. Simulation models based on both data-driven and mathematical formulations are also established to better fit the actual nonlinear conditions. Finally, the proposed network structure can reach 92.1% classification accuracy compared with the commonly used lightweight 2-D CNN model. It can be concluded that 1-D CNN achieves similar classification results as 2-D CNN with less computational resource consumption.
Numerical simulation of drag reduction effect on the surface of bionic fish-scalesWu, Xueting; Wang, Yonghua; Xu, Jinkai; Yu, Huadong
doi: 10.1088/1742-6596/2492/1/012012pmid: N/A
With the development of marine transportation and underwater navigation technology, fluid drag reduction has become an international research hotspot as an important technical means to save energy and reduce environmental pollution. It has been found that the body surface structures of many organisms have unique drag-reduction properties. Therefore, it is feasible and important to replicate the morphological features of the body surface to the material surface. In this research, the structural features of biomimetic fish scales were summarized and extracted by observing the laminar arrangement features and morphology features of the surface scales of aquatic fish. Then, the arrangement features and morphology of fish-scales were abstracted into the oblique groove structure, and a three-dimensional model of the bionic fish-scale with a fan-shaped structure was constructed. The surface flow field of the bionic fish-scale was numerically simulated by COMSOL Multiphysics to revealing the mechanism of resistance reduction on the surface of the bionic fish-scale. The results indicate that the maximum drag reduction rate of the bionic fish scale surface is 8.40% compared with the smooth surface at a water speed of 0.6 m/s.
Peer Review Statementdoi: 10.1088/1742-6596/2492/1/011002pmid: N/A
All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.• Type of peer review: Single Anonymous• Conference submission management system: Morressier• Number of submissions received: 72• Number of submissions sent for review: 67• Number of submissions accepted: 39• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 54.2• Average number of reviews per paper: 2• Total number of reviewers involved: 20• Contact person for queries:Name: Xuexia YeEmail: [email protected]: AEIC Academic Exchange Information Centre
Prefacedoi: 10.1088/1742-6596/2492/1/011001pmid: N/A
The 6th International Conference on Intelligent Manufacturing and Automation (IMA 2023) took place in Guangzhou, China from January 13th to 15th, 2023 (virtual form). It is committed to bringing professionals and scholars to deliberate on the future of Intelligent Manufacturing and Automation, and bring new horizons and insights to the related researchers and practitioners.With the successful experience of the past 5 years, the 6th International Conference on Intelligent Manufacturing and Automation was an even greater success in 2023. Focusing on “Intelligent Manufacturing and Automation” and related research areas, IMA 2023 brought together leading personnel from all over the world to share views and experiences and exchange ideas, promoted the research and developmental activities in Intelligent Manufacturing and Automation, and facilitated future business or research contacts among all the participants.After months of well preparation and hard work, the proceedings of IMA 2023 covering a bunch of papers, having been checked through rigorous review and processes to meet the requirements of publication, are smoothly published. These papers feature but are not limited to the following topics: Biomedical Manufacturing, Rapid Manufacturing Technologies, Electric Automation, Mechanical Manufacturing and Automation, etc.We had about 80 participations both as speakers and participants, including researchers, teachers, students, industrial experts, and professionals. During the keynote speech part, each keynote speaker was given 30-40 minutes for keynote speech. Among them, Professor Zongmin Ma from North University of China, China performed a keynote speech: Research on the Development Progress and Application of NV Solid-State Atomic Magnetometer. He mainly discussed about the magnetic measurement principle, key measurement technology and integration technology of NV color center in quantum sensing, and put forward the technical development direction for the current technical problems. In addition, he also summarized the preliminary practical applications of NV magnetometers in the fields of geomagnetic detection, nondestructive testing, biomedicine and other fields in recent years. The insightful speeches of each keynote speakers triggered interesting discussions and a good number of informal talks between all the participants.We would like to thank all the participants in the 6th International Conference on Intelligent Manufacturing and Automation. Besides, our special acknowledgement goes to the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. May this venture be a spark that ignites many new inventions in the fields of Intelligent Manufacturing and Automation.The Committee of IMA 2023List of Committee Member is available in this pdf.
Simulation of Electric Vehicle Regenerative Braking Control Strategy Based on Brake Intention RecognitionJia, Qiyang; Tang, Peng
doi: 10.1088/1742-6596/2492/1/012018pmid: N/A
The development of regenerative braking technology for a pure electric vehicle is the key technology to solving the current limited range of the electric vehicle. Accurately identifying the driver’s braking intent is fundamental to controlling the braking system of an electric vehicle.A brake intent recognition controller is designed in this paper, which is based on a lot of driving data and fuzzy principles. In addition, two typical regenerative brake control strategies are compared. The simulation results of Matlab/Simulink and AMESim show that the SOC of the power battery increases obviously at different braking intensities of 30 km/h. Besides, energy recovery is up to 20.5%, and battery energy increases by 6.5 wh.
Handling robot non-fixed high-velocity handling methodZhang, Zhihao; Guo, Fuyu; Tang, Qian; Chen, Jiawei
doi: 10.1088/1742-6596/2492/1/012025pmid: N/A
Handling robots are widely used today, and non-fixed handling is more effective than fixed handling in some specific scenarios. The non-fixed handling of robots can increase the load and reduce the precise positioning process of grabbing the object, but the handling velocity is relatively slow for the stability of the object. In addition, it needs to consider the friction and reaction force constraints on the object. This paper proposes a non-fixed high-velocity handling method for the handling robot. First, it deduces the friction and reaction force constraints that maintain the dynamic balance of the object during the handling process into robot dynamic constraints. Then, the time-optimal trajectory planning algorithm based on reachability analysis is used to solve the robot’s time-optimal trajectory that satisfies the dynamic constraints of the object in the handling process. This leads to a markedly improved production efficiency, as the robot concludes the handling process in the shortest possible time. Finally, the experimental handling of the object by the ABB IRB1200 robot verifies this method.
An attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnPChen, Weizhuo; Zhang, Lijie; Luo, Fangrui
doi: 10.1088/1742-6596/2492/1/012022pmid: N/A
Aiming at the attitude estimation of the telescopic arm of the boarding bridge in the process of docking with the offshore wind turbine, an attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP is proposed in this paper. YOLOv5 algorithm is used to detect four marks in the offshore wind turbine logo image, and then the EPnP algorithm is used to solve attitude angles of the telescopic arm relative to the landing port of the offshore wind turbine according to the 2D pixel coordinates of the center point of each mark. The experimental results show that the attitude estimation error of the proposed algorithm is less than 0.4° and the data update frequency of the algorithm implemented in NVIDIA Jetson AGX Xavier is 25Hz, which meets the real-time and accuracy requirements for the attitude detection of the boarding bridge telescopic arm.
Variable Gate Resistance Drive Circuit Based on di/dt Feedback for IGBTYan, Wenyi; Liu, Tingzhang; Lv, Liangliang; Fan, Ruijie
doi: 10.1088/1742-6596/2492/1/012014pmid: N/A
To overcome the rigid control of the classical drive method which used fixed resistance to drive IGBT, a variable gate resistance drive method was proposed. This method detected the change rate of collector current through the parasitic inductor of the IGBT module, and the closed-loop feedback was used to control this rate of change. All the expressions between voltage overshoot , overcurrent and di/dt were derived. Based on these expressions, the di/dt feedback control was designed. The feedback circuit selected gate resistances with different resistance values to drive IGBT in various stages of the turn-on and off process. According to the simulation analysis, both switching time and loss were reduced ultimately. The current peak and overvoltage were controlled effectively, and the switching performance was improved.
Optimal Design of Planetary Reducer of Electric Actuator base on NSGA-IILong, Hu; Zeng, Xiangming
doi: 10.1088/1742-6596/2492/1/012008pmid: N/A
To solve the problem of equipment installation space shortage in industrial automation sites, a set of hybrid transmission schemes was designed with a two-stage 2K-H(NGW) planetary reducer as the main transmission mechanism combined with a fixed shaft gear train and worm gear and worm mechanism. At the same time, to obtain the design parameters of the electric actuator more quickly and accurately, with the minimum volume and maximum efficiency of the main transmission mechanism as the design objectives, the multi-objective function mathematical model is established under the conditions of meeting the gear matching, displacement coefficient, strength, and overlap degree, and the non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the mathematical model. The research results show that the volume of the main transmission mechanism is reduced by 13.6%, the transmission ratio is increased by 1.54%, but the transmission efficiency is reduced by 3.32% when the strength and life of the gear are equal.