Research topic: Early fault diagnosis of wind turbine drivetrains (2021-2024)
My main PhD research objective is:
It is expected to reach an intelligent and promising solution for successfully condition monitoring of drivetrain to address the issues of high failure rate and high operation/maintenance cost of wind turbines.
Current research interests:
- Wind turbine drivetrain condition monitoring
- Data-driven fault detection/diagnosis
- Machine/deep learning models
- Statistical signal processing
- Multi-body simulation
Main supervisor: Prof. Amir R. Nejad
Co-supervisor: Prof. Zhen Gao
Project:
This PhD is a part of the InteDiag-WTCP project, a collaborative project between China and Norway. The Chinese team is led by Hunan University, in collaboration with Central South University, Hunan University of Science and Technology, and two leading wind power companies XEMC WINDPOWER and GOLDWIND TECHNOLOGY. The Norwegian team is led by NTNU, in collaboration with two monitoring and digital service companies, EDR&MEDESO AS and SAFETEC NORDIC AS.
Background:
M.Sc., Mechanical Engineering, University of Tabriz, Iran (2019)
- Field: Vibration and Dynamic, Health Monitoring
- Thesis title: An intelligent method for combined fault diagnosis of rotary machinery based on Variational Mode Decomposition and deep learning
B.Sc., Mechanical Engineering, University of Tabriz, Iran (2015)
- Thesis title: Modeling and experimental evaluation of Pelamis wave energy converter
Courses:
