Wind turbine blades fault diagnosis based on vibration dataset analysis

Ahmed Ali Farhan Ogaili; Alaa Abdulhady Jaber; Mohsin Noori Hamzah

doi:10.1016/j.dib.2023.109414

Wind turbine blades fault diagnosis based on vibration dataset analysis

Data Brief. 2023 Jul 16:49:109414. doi: 10.1016/j.dib.2023.109414. eCollection 2023 Aug.

Authors

Ahmed Ali Farhan Ogaili^{1

2}, Alaa Abdulhady Jaber¹, Mohsin Noori Hamzah¹

Affiliations

¹ Mechanical Engineering Department, University of Technology, Baghdad, Iraq.
² Mechanical Engineering Department, University of Mustanisiryah, Baghdad, Iraq.

Abstract

Globally, wind turbines play a significant role in generating sustainable and clean energy. Ensuring optimal performance and reliability is crucial to minimize failures and reduce operating and maintenance costs. However, due to their conventional design, identifying faults in wind turbines is challenging. This dataset provides vibration data for faulty wind turbine blades, which covers common vibration excitation mechanisms associated with various faults and operating conditions, including wind speed. The introduced faults in the wind turbine blades include surface erosion, cracked blade, mass imbalance, and twist blade fault. This data article serves as a valuable resource for validating condition monitoring methods in industrial wind turbine applications and facilitates a better understanding of vibration signal characteristics associated with different faults.

Keywords: Condition monitoring; Fault diagnosis; Vibration signal analysis; Wind turbine blade.