Dr. Shawn Sheng is a senior research engineer at NREL. He has B.S. and M.S. degrees both in electrical engineering and a Ph.D. in mechanical engineering. Shawn is currently leading wind turbine condition monitoring, gearbox reliability database, and wind plant operation and maintenance research at NREL. Shawn has a broad range of experience: mechanical and electrical system modeling and analysis; data sensing and sensor placement; signal processing; machine learning and artificial intelligence; machine defect classification and level evaluation; machine life prognosis; multi scale modeling; traditional and intelligent control. He is a member of the American Society of Mechanical Engineers, Institute of Electrical and Electronics Engineers, and Society of Tribologists and Lubrication Engineers. His work has been published in various journals, conference proceedings, book chapters, and technical reports.

Research Interests

Wind plant operation and maintenance

Wind turbine drivetrain and other rotating machine condition monitoring

Machine learning and artificial intelligence

Education

Ph.D., Mechanical Engineering, University of Massachusetts Amherst

M.S., Electrical Engineering, Institute of Electrical Engineering, Chinese Academy of Sciences

B.S., Electrical Engineering, Northeast Petroleum University (former Daqing Petroleum Institute)

Professional Experience

Editorial Board Member, Wind Energy Journal (2019–Present)

Technical Editor, Tribology and Lubrication Technology Magazine of Society of Tribologists and Lubrication Engineers (2018–2019)

Co-Guest Editor, Renewable Energy Journal Special Issue on Real-Time Monitoring, Prognosis, and Resilient Control for Wind Turbine Systems (2018)

Guest Editor, Wind Energy Journal Special Issue on Condition Monitoring (2014)

Featured Work

A Methodology for Reliability Assessment and Prognosis of Bearing Axial Cracking in Wind Turbine Gearboxes, Renewable and Sustainable Energy Reviews (2020)

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions, Journal of Engineering for Gas Turbines and Power (2020)

An Anomaly Detection Framework for Dynamic Systems Using a Bayesian Hierarchical Framework, Applied Energy (2019)

Recommended Key Performance Indicators for Operational Management of Wind Turbines, Journal of Physics (2019)

A Prognostics and Health Management Framework for Wind, ASME Turbo Expo (2019)

Awards and Honors

Society of Tribologists and Lubrication Engineers Wilber Deutsch Memorial Award (2017)