Journal of Energy and Power Technology (JEPT) is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. This periodical is dedicated to providing a unique, peer-reviewed, multi-disciplinary platform for researchers, scientists and engineers in academia, research institutions, government agencies and industry. The journal is also of interest to technology developers, planners, policy makers and technical, economic and policy advisers to present their research results and findings.
Journal of Energy and Power Technology focuses on all aspects of energy and power. It publishes original research and review articles and also publishes Survey, Comments, Perspectives, Reviews, News & Views, Tutorial and Discussion Papers from experts in these fields to promote intuitive understanding of the state-of-the-art and technology trends.
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Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 6.1 weeks after submission; acceptance to publication is undertaken in 8.9 days (median values for papers published in this journal in the second half of 2021, 1-2 days of FREE language polishing time is also included in this period).
Fault Diagnosis and Health Status Assessment of Wind Turbine
Submission Deadline: January 31, 2024 (Open) Submit Now
Xiaoan Yan, PhD, Associate Professor
Nanjing Forestry University, Nanjing, China
Research interests: fault diagnosis; signal processing; condition monitoring; wind turbine
Minping Jia, PhD, Professor
Southeast University, Nanjing, China
Research interests: machine monitoring and fault diagnosis; vibration measurement, analysis and control; dynamic signal analysis and processing
Zengtao Chen, PhD, Associate Professor
University of Alberta, Edmonton, Canada
Research interests: mechanics of materials; materials modelling; damage and fracture mechanics
About This Topic
Wind turbine is a complex mechatronic equipment, which requires an efficient monitoring and maintenance system to ensure its safety, reliability and economy in service. Due to the complex service conditions, harsh environment and difficult maintenance of wind turbines, the traditional fault diagnosis andhealth status assessment theory cannot meet the needs of wind power industry for near-zero fault operation of wind turbines. Wind turbine fault diagnosis and health status assessment strategy based on machine learning, making full use of the wind turbine SCADA data information, which can achieve accurate fault detection and low-cost and efficient preventive maintenance. The most representative machine learning algorithm is the deep learning, which is also a hot research direction in the field of machine condition monitoring and fault diagnosis.
The aim of this Special Issue is to collect the recent results on adopting deep learning theory or some other advanced models for wind turbine fault diagnosis, health status assessment, and remaining life prediction.
Potential topics to be covered：
Health status assessment;
Remaining life prediction.
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