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Intelligent Diagnosis of Motor Rolling Bearing Based on Wavelet Dropout Convolutional Neural Network with Asymptotically Trained Interference

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IEEE Sensors Journal 2025年第10期25卷 17892-17904页

作者： Li, Kun Wang, Haizhou Han, Ying Liu, Xinming Liaoning Technical University Faculty of Electrical and Control Engineering Liaoning Huludao125105 China

In recent years, in practical industrial applications, the task of accurate and robust compound fault diagnosis of motor rolling bearings under huge noise is still the focus of current research. To solve these problems, an end-to-end framework of wavelet Dropout convolutional neural network (ATIWD-CNN) with asymptotically trained interference was proposed. Firstly, an innovative six-layer discrete wavelet Dropout attention layer (DWDA-Layer) with an asymptotic deactivation rate was proposed, in which the time domain signal was mapped to the wavelet domain to simulate noise interference by the dropout operation, thereby improving the shortcomings of deep neural network models in signal analysis;the proposed local dual-scale frequency attention mechanism (LDSFAM) can focus on stronger fault-related information from the mixed frequency components. Secondly, a larger convolution kernel was used in the first learnable convolution block to suppress the interference of high-frequency noise and extract a wider range of time domain features, in which the smoother Gaussian Error Linear Unit (GELU) was used in all convolution blocks, and the Group Normalization (GN) technique and the setting of ultra-small batch size were used to make the training of the model more refined. Compared with some other advanced methods, the average diagnostic accuracy of ATIWD-CNN on two experimental datasets (with -10dB noise level) was increased by 7.16% and 2.42%, respectively. © 2001-2012 IEEE.

关键词： Time domain analysis

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Insulator Image Segmentation Method Based on IHPO-GH Optimized K-means Algorithm

Insulator Image Segmentation Method Based on IHPO-GH Optimiz...

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International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Liu, Yijiangze Peng, Jishen Song, Xinping Cheng, Boyu Faculty of Electrical & Control Engineering Liaoning Technical University Liaoning China

ISBN: (纸本)9789819618514

In order to improve the accuracy of insulator image segmentation and to resolve the problems of the traditional k-means algorithm that is sensitive to the cluster center, the number of clusters, and is prone to segmentation errors, an insulator image segmentation method based on improving the Hunter Pery Optimization-Gray Histogram (IHPO-GH) and optimizing the k-means algorithm was proposed. The gray histogram of the image is constructed, and the number of clustering centers of k-means algorithm is determined by obtaining the extreme points of the gray histogram;The IHPO algorithm based on the cosine decreasing method to balance the global and local search and the exponential decreasing method to improve the local solution accuracy is proposed to optimize the selection of the initial cluster center;through the above two methods, more accurate initial parameters can be obtained and the iterative process of k-means can be optimized. Experimental verification shows that the algorithm in this paper is significantly faster than the k-means algorithm in terms of iteration speed, and the solution accuracy is also greatly improved by calculating PNSR and MSE parameters. Finally, the visual effect analysis verifies the accuracy of the proposed algorithm image segmentation. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Image segmentation

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Predictive Time-Varying Sliding Mode control of Permanent Magnet Synchronous Motor with Improved Sand Cat Swarm Algorithm 19th

Predictive Time-Varying Sliding Mode Control of Permanent Ma...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Hou, Limin Mu, Guangzhao Geng, Chunyang Ban, Mingyun Faculty of Electrical and Control Engineering Liaoning Technical University Huludao China

ISBN: (纸本)9789819613908

Aiming at the buffeting problem of traditional integral SMC of PMSM, a predictive time-varying sliding mode control of permanent magnet synchronous motor with improved sand cat swarm algorithm was proposed. Firstly, an improved double power reaching law is proposed, which solves the contradiction between buffeting and rapidity effectively. The idea of prediction is introduced into the SMC, and the optimal time-varying sliding mode surface at the current time is selected online by predictive control, which improves the convergence of the global stage of sliding mode control. Then the extended state observer is used to observe the unknown interference of the system and to compensate it by feed forward, which improves the robustness of the system. Finally, in order to reduce the difficulty of manual parameter adjustment, an improved sand cat swarm algorithm is proposed to optimize each parameter of the controller off-line, which effectively improves the parameter accuracy. Simulation and experimental results show that the control strategy has strong robustness and excellent dynamic and steady state performance. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Predictive control systems

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Back EMF Harmonics and DC Components Rejection for Improvement in Sensorless FTC for Multiphase PMSM Based on SMO With Enhanced Cross-Coupling Second-Order Sequence Filter

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年第2期13卷 2612-2621页

作者： Farahat, Abdallah Liu, Guohai Chen, Qian Al-Azhar University Faculty of Engineering Department of Electrical Engineering Qena83513 Egypt Jiangsu University School of Electrical and Information Engineering Jiangsu Key Laboratory of Drive and Intelligent Control for Electric Vehicle Zhenjiang212013 China

This article presents a new sensorless fault-tolerant control approach for five-phase permanent magnet synchronous motor (PMSM) using a sliding mode observer (SMO) due to its strong robustness and stability. Asymmetric operations under open-circuit faults (OCFs) cause high harmonics and dc components in back EMFs, making it challenging to estimate speed and position. Thus, in this article, a cross-coupling second-order sequence filter (CC-SOSF) works for extracting the symmetrical sequence components of back EMFs by eliminating the harmonic. In addition, a dc mitigation circuit is added to this filter to eliminate dc bias. To ensure frequency adaptation and avoid the issue of the estimated frequency feedback, an open-loop frequency estimator (OLFE) is adopted. Finally, a phase-locked loop calculates the estimated rotor position and speed. Simulations and experimental tests confirm the effective elimination of both harmonics and dc components, leading to accurate position and speed estimation in the presence of OCFs. © 2013 IEEE.

关键词： Harmonic analysis

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Equipment Defect Detection Algorithm Based on Improved Yolov8 for Power Operation and Inspection

Equipment Defect Detection Algorithm Based on Improved Yolov...

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International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Peng, Jishen Maenvelope, Longze Song, Liye Faculty of Electrical and Control Engineering Liaoning Technical University Huludao125000 China

ISBN: (纸本)9789819618552

In this paper, an EF-YOLOv8 model based on YOLOv8 is proposed to solve the problem of detecting small target defects in power equipment. This model significantly improves the ability to identify subtle defects through inno-vative architectural adjustments. The core contribution lies in the design of the C2f-EF module, which combines multi-scale features with an enhanced atten-tion mechanism to specifically improve the model’s perception to detect small targets. The model also integrates the improved spatial pyramid pooling structure SPPCSPC-G and weighted bidirectional feature pyramid network (BiFPN), which further promotes accurate integration and extraction of multi-scale features and improves small target detection performance. Experiments show that EF-YOLOv8 has achieved remarkable results in small target defect detection. The mAP index has increased by 2.7% and 4.7%. At the same time, the model efficiency has been greatly improved. Parameters and computation were reduced by 12.5% and 10.9%, respectively. The detection speed is increased to 62 frames/s. This study provides a new efficient and accurate solution for defect detection in the field of power operation inspection, which is of great significance for improving the level of safety operation and maintenance of power grids. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Inspection equipment

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Research on Stacking Electricity Load Forecasting Based on Particle Swarm Optimization Algorithm

Research on Stacking Electricity Load Forecasting Based on P...

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International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Ma, Jun Peng, Jishen Han, Haotong Liu, Yijiangze Faculty of Electrical and Control Engineering Liaoning Technical University Huludao125000 China

ISBN: (纸本)9789819618552

Power load forecasting plays a crucial role in decision making for future power infrastructure construction and development planning. In this paper, an integrated learning Stacking forecasting model is proposed which incorporates an improved PSO optimization algorithm. The traditional PSO algorithm suffers from the lack of randomness in the change of particle positions, which is easy to fall into the dilemma of local optimal solutions, so the MPSO algorithm is designed to optimize the population initialization and search optimization process. The accuracy of short-term power load forecasting can ensure the safe and efficient operation of electric power. Traditional machine learning methods have the disadvantages of high computational cost of hyper-parameter tuning and reliance on manual experience in power load forecasting, while the MPSO algorithm has a natural advantage in the field of hyper-parameter tuning, which significantly improves the performance of neural networks by exchanging information between individuals in order to achieve the global optimal search results. The experimental results indicated that comparing the prediction results of integrated learning Stacking, the Stacking model optimized with MPSO parameters is more accurate in the results of various evaluation indexes. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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A PCB bare board defects detection method based on dual-domain wavelet-YOLO 24

A PCB bare board defects detection method based on dual-doma...

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8th International Conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Zhong, Xu Li, Kun Faculty of Electrical and Control Engineering Liaoning Technical University Liaoning Huludao China

ISBN: (纸本)9798400710094

In this paper, a defect detection model DDW-YOLO based on YOLOv5 is proposed to improve the accuracy and efficiency of PCB bare board defect detection. In PCB bare boards, the ratio of defect area to background area differs significantly, resulting in poor performance of existing detection methods in identifying small target defects. To solve this problem, this paper proposes the wavelet signature fusion module and the dual-domain wavelet attention network, which can enhance the model’s attention to high-frequency information and improve the detection ability of small defects. The experimental results show that DDW-YOLO is superior to the current mainstream model in both detection accuracy and calculation efficiency, and provides an effective solution for PCB bare board defect detection. © 2024 Copyright held by the owner/author(s).

关键词： Defect detection

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Three-Vector Model Predictive control Strategy for NPC Three-Level Inverter

Three-Vector Model Predictive Control Strategy for NPC Three...

引用

International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Li, Xiaowei Lin, Dong Faculty of Electrical and Control Engineering Liaoning Technical University Huludao125100 China

ISBN: (纸本)9789819618675

This article proposes a three vector model predictive control strategy based for the Neutral Point Clamped (NPC) three-level inverter to reduce current ripple. The proposed three-vector model predictive current control synthesizes a new voltage vector using three vectors from the vicinity of the small sector, effectively utilizing three voltage vectors per control cycle. This approach improves the quality of the output current and reduces current ripple. To balance the neutral-point voltage, a switching control method is employed, which selects a new synthesized voltage vector that minimizes the impact on the neutral-point potential fluctuations. Simulation results show that the total harmonic distortion (THD) value of the three-vector model predictive control is approximately 1.4% lower than that of the single-vector control, significantly enhancing the current quality. Additionally, the switching control method effectively stabilizes the neutral-point capacitor voltage. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Vectors

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ICOA-Based Self-Resistant Speed control of Permanent Magnet Synchronous Motors 19th

ICOA-Based Self-Resistant Speed Control of Permanent Magnet ...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Yan, Xin Zhang, Wang Tu, Naiwei Hou, Limin Faculty of Electrical and Control Engineering Liaoning Technical University Liaoning125105 China

ISBN: (纸本)9789819613823

Aiming at the problem of large overshoot of the traditional second-order self-imposed disturbance control in the speed control system of permanent magnet synchronous motor, an improved second-order self-imposed disturbance control method is proposed. Firstly, considering that the dilated state observer is slow in estimating the perturbation, a new fixed-time dilated state observer is designed. Secondly, in the state error feedback law, the hyperbolic sine function is used to design a new nonlinear function that is continuously derivable at the origin and segmentation points, thus solving the problem of the traditional nonlinear function that generates abrupt changes under certain conditions, and finally, an improved crayfish optimisation algorithm is proposed to rectify its parameters. Through Matlab/Simulink simulation and experimental verification on a semi-physical platform, the proposed control method not only effectively solves the overshooting problem, but also further improves the robustness and immunity of the system compared with the traditional self-immunity control. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Path Planning for Multi-agent Systems Using Deep Q-Networks Reinforcement Learning 25th

Path Planning for Multi-agent Systems Using Deep Q-Networks...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Alispahić, Ibrahim Tahirović, Adnan Faculty of Electrical Engineering Department of Automatic Control and Electronics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9783031720611

This work presents a comprehensive study of the application of multi-agent reinforcement learning (MARL) based on deep Q-networks (DQN), aiming to enhance the cooperation and coordination of multiple agents in complex environments. The core problem addressed is the multi-agent traveling salesman problem, i.e. the effective collaboration and target-reaching by multiple agents. This challenge is inherently present in a variety of applications, including inspection and maintenance tasks based on autonomous aerial robotic systems. The research delves into the intricacies of multi-agent systems, emphasizing the dynamic interaction between agents and the environment. Although the proposed algorithm is developed and trained in a planar space, its principles and methodologies are easily extendable to any operational environment. The proposed approach is capable of training on a number of different scenarios, using various numbers of agents and targets, regardless of their initial positions, making it highly adaptable to scenarios like aerial inspections, where the number of agents, the number of inspection sites and their locations are unknown a priori. Furthermore, the work outlines how the enhanced DQN algorithm can be tailored for scenarios with numerous autonomous agents and targets, ensuring efficient path planning and target acquisition in constrained and unstructured spaces. This work also provides a solid foundation for future research in integrating advanced reinforcement learning techniques into the design and operational strategies for solving tasks, which can be described by the multi-agent traveling salesman problem, including inspection and maintenance with an autonomous multi-agent system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Traveling salesman problem

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