Wind Turbine Blade Bearing Fault Detection and Diagnosis Using Vibration and Acoustic Emission Signal Analysis

  • Zepeng Liu

Student thesis: Phd


Over the last few decades, wind energy, as one of the renewable sources, has attracted extensive attention all over the world; and wind turbines are critical devices to convert the kinetic wind energy into electrical energy. Blade bearings, as joint components of wind turbines, play an important role in pitching blades at the desired angles for optimized electric energy output. Blade bearing failure can cause the turbine to lose control or even breakdown which cause curtailment in energy productivity. The assemble and repair costs of blade bearings are high. As a result, condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings are vitally important to increase the wind turbine production and reduce operation and maintenance (O&M) costs. However, CMFD of wind turbine blade bearings is still at an initial stage due to the following challenges: (1) The rotation speeds of blade bearings are very slow (less than 5 r/min), so the collected fault signals are weak and masked by heavy noise disturbances. (2) Different from constantly rotating bearings, such as main bearings, generator bearings and gearbox bearings, the blade bearing rotation angle is very limited (within 100 degrees) resulting a limited number of fault signals. (3) The disturbances from the blade flapping and dynamic wind loads can cause fluctuating rotation speeds which affect the diagnostic accuracy. To overcome the aforementioned challenges, we firstly developed an industrial-scale and slow-speed wind turbine blade bearing test-rig, and utilized vibration and acoustic emission (AE) analysis for the fault detection. The test blade bearing is a naturally damaged wind turbine blade bearing which was in operation on a wind farm for over 15 years; therefore, its vibration and AE signals are more in line with field situations. Then, some novel methods including time-domain analysis, frequency domain analysis and AI methods, are developed to gradually and systematically solve the aforementioned challenges. The proposed diagnostic methods in this thesis are validated by different experiments and case studies. The comparisons with respect to some popular diagnostic methods are explained in detail, which highlights the superiority of our developed methods.
Date of Award1 Aug 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJoaquin Carrasco Gomez (Supervisor) & Long Zhang (Supervisor)


  • signal processing
  • blade bearing
  • fault diagnosis
  • wind energy
  • data-driven analysis

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