Guest Editors
Yuqiao Zheng, Lanzhou University of Technology, China. Email: zhengyuqiaolut@163.com
Jianhua Yang, China University of Mining and Technology, China. Email:jianhuayang@cumt.edu.cn
Zhaoju Zhu, Fuzhou University, China. Email: zhuzhaoju0216@163.com
Jinjie Zhu, Tokyo Institute of Technology, Japan. Email: zhu.j.ag@m.titech.ac.jp
Summary
Energy is an essential material basis for human survival and development, but faults and damages are inevitable during either fabrication or lifetime of energy equipment for energy production. In addition, the energy equipment usually presents complex dynamical behaviors. They influence the normal operation of energy equipment. Therefore, it is essential to study the dynamics and develop advanced techniques for ensuring the safe and reliable operation of energy equipment. Lots of academics have been committed to investigating and developing the dynamics, fault diagnosis, reliability, and quality control in the life cycle management of energy equipment. For this purpose, this special issue would collect the latest achievements in this field. Potential topics include but are not limited to:
Dynamics and fault diagnosis of equipment in traditional energy
Dynamics and fault diagnosis of intelligent equipment and robot in energy engineering
Dynamics and fault diagnosis of energy equipment under complex working conditions
Fault diagnosis of energy equipment under extreme working conditions
New sensing and measuring methods of energy equipment
Deep learning in the application of energy equipment
Wind turbine technologies (blade, tower, gearbox)
Wind turbine (design, modeling, performance optimization,data analysis)
Mechanical structure dynamics of energy equipment
Reliability of energy equipment
Quality control of energy equipment in the manufacturing process
Keywords
Energy equipment, Dynamics, Fault diagnosis, Reliability, Quality control
Published Papers
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Open Access
ARTICLE
Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment
Guanghua Zheng, Chaolin Luo, Mengen Shen, Wanzhong Lv, Wenbo Jiang, Weibo Yang
Energy Engineering, Vol.120, No.4, pp. 985-1000, 2023, DOI:10.32604/ee.2023.024372
（This article belongs to this Special Issue:
Dynamics and Fault Diagnosis for Energy Equipment)
Abstract To solve the problems of low precision of weak feature extraction, heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage (UHV) equipment key parts, an automatic feature extraction algorithm is proposed. Firstly, the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method. Then, binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation. Finally, the automatic identification of weld defect area is realized based on the sequential traversal of binary tree. Several characteristic analysis dimensions are established…
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