Yuanchen Huo

Taiyuan University of Technology

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Wensheng Lv

Tong Chuntian

Harbin Institute of Technology

Xueqin Wen

Taiyuan University of Technology

Shuai Xu

China Aerospace Science and Technology Corporation

Bing Pang

China Medical University

Dingyi He

The University of Texas at Dallas

T. Wang

Institute of Physics

X. Yang

Southwest Medical University

Le Yang

Gansu Agricultural University

Yiming Lin

Huadong Hospital

Cooperative Institutions

Chinese Academy of Medical Sciences & Peking Union Medical College

Zhengzhou University

China University of Mining and Technology

Institute of Physics

Sichuan University

Zhejiang University

Harbin Institute of Technology

Taiyuan University of Technology

China Aerospace Science and Technology Corporation

University of Southern California

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Research on fine recognition method of electric power equipment safety hazards based on visual processing

Yuanchen Huo Bing Pang X. Yang Le Yang Yiming Lin

With the further research of deep learning, power companies have gradually eliminated the prevention and control by manual inspection, and have adopted deep learning to identify the safety hazards of power equipment, and to detect the defects of power equipment more accurately and quickly. Firstly, this paper introduces the significant drawbacks of traditional power equipment identification methods in the field of power equipment identification, and then illuminates the advantages of deep learning in the field of power equipment, which leads to the YOLOv7 algorithm used in this study, and then provides a detailed introduction of the theory and concepts of the algorithm. Second, this paper constructs a dataset applicable to the identification of safety hazards in electrical equipment, and uses this dataset to train and test the YOLOv7 algorithm. Finally, by analyzing the experimental results, it is concluded that the algorithm has a high accuracy character and robustness in the method of identifying safety hazards of electrical equipment. This study provides a practical and feasible technical route and theoretical foundation for solving the problem of safety hidden hazard detection of electrical equipment using YOLOv7 algorithm.

10.1117/12.3058334

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An overview of the reliability of motor drive systems

Jian Yang Zeng Deyin Shuai Xu Tong Chuntian Wang Huimin

The reliable operation of motor drive systems has been paid more and more attention, which is regarded as the key factor for various applications. In this paper, under the concept of the reliability prediction metrics and models from component-level to system-level, quantitative reliability assessment of motor drive system can be introduced from four aspects: subsystems of power converter, subsystems of motor body structure, subsystems of controller and subsystems of sensor. Several methods have been presented in the four aspects to enhance the reliability of systems. Moreover, reliability research status and contributions in these years are overviewed and concluded, including redundant designing, motor structure optimization and fault-tolerant control algorithm. Also, the limitations of the current research have been analyzed and the future research is recommended.

Component (thermodynamics)

Motor drive

10.1109/eiconrus.2017.7910695

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Citations (1)

BP Neural Network-Based Determination of Soil-Codonopsis under Different Sources Key Factors for Enrichment and Transport of Heavy Metal Elements in the System

Water Air & Soil Pollution (2024)

Xueqin Wen Yuanchen Huo Min Chen Wensheng Lv

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10.1007/s11270-024-07398-6

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