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PROFILE:

 

Fangzhou Cheng

Fangzhou Cheng is a Data Scientist in the Analytics for Condition Evaluation of Systems (ACES) area within the System Sciences Lab. His research is focused on the data-driven methods for condition-based maintenance of electrical and mechanical systems.

Prior to joining PARC, Fangzhou was a Research Assistant in Power and Energy Systems Lab at University of Nebraska-Lincoln, where he worked on model-based/data-driven methods for fault diagnostics and prognostics of wind turbine drivetrains using electrical signals. During his internship with General Electric Golbal Research Center, he was involved in developing a condition monitoring system for indirect-drive wind energy conversion systems. 

Dr. Cheng received his Ph.D. in Electrical Engineering from the University of Nebraska-Lincoln, and B.Eng. degree in Electrical Engineering from Zhejiang University in China. He has published over 15 peer-reviewed articles and papers in leading journals and conferences. He also presented and served as a reviewer for leading journals and conferences, such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Industry Applications and IEEE Engergy Conversion Congress and Exposition.

In his free time, Fangzhou enjoys playing basketball and ping pong.

 

 

 

other publications

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2018

Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis

Fangzhou Cheng, Liyan Qu, and Wei Qiao, IEEE Transactions on Sustainable Energy

Rotor Current-Based Fault Diagnosis for DFIG Wind Turbine Drivetrain Gearboxes Using Frequency Analysis and a Deep Classifier

Fangzhou Cheng, Jun Wang, Liyan Qu, and Wei Qiao, IEEE Transactions on Industry Applications

Multiscale Filtering Reconstruction for Wind Turbine Gearbox Fault Diagnosis Under Varying-Speed and Noisy Conditions

Jun Wang, Fangzhou Cheng, Liyan Qu, and Wei Qiao, IEEE Transactions on Industrial Electronics

2017

Current-based fault detection and identification for wind turbine drivetrain gearboxes

Fangzhou Cheng, Yayu Peng, Liyan Qu, and Wei Qiao, IEEE Transactions on Industry Applications

A comparative study on Vibration-and current-based approaches for drivetrain gearbox fault diagnosis

Xiaohang Jin, Fangzhou Cheng, Yayu Peng, Wei Qiao, and Liyan Qu, IEEE Industry Applications Magazine, vol. 23, no. 2, pp. 1-9, 2017.

Fault Prognosis of Drivetrain Gearbox Based on a Recurrent Neural Network

Yayu Peng, Fangzhou Cheng, Wei Qiao, and Liyan Qu 2017 IEEE International Conference on Electro Information Technology (EIT)

2016

Fault diagnosis of wind turbine gearbox using DFIG stator current analysis

Fangzhou Cheng, Chun Wei, Liyan Qu, and Wei Qiao, Energy Conversion Congress and Exposition (ECCE)

Quantitative evaluation of wind turbine faults under variable operational conditions

Xiaohang Jin, Wei Qiao, Yayu Peng, Fangzhou Cheng, and Liyan Qu, IEEE Transactions on Industry Applications

2015

A case-based data-driven prediction framework for machine fault prognostics

Fangzhou Cheng, Liyan Qu, and Wei Qiao, Energy Conversion Congress and Exposition (ECCE)

 

 

 

 

competencies