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A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

Journal

MEASUREMENT
Volume 149, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.107002

Keywords

Wind energy; Wind turbines; Large-scale wind turbine bearings; Bearing failure modes; Condition monitoring and fault diagnosis

Funding

  1. Engineering and Physical Sciences Research Council [EP/S017224/1]
  2. EPSRC [EP/S017224/1] Funding Source: UKRI

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Large-scale wind turbine bearings including main bearings, gearbox bearings, generator bearings, blade bearings and yaw bearings, are critical components for wind turbines to convert kinetic wind energy into electrical energy. Unlike general-purpose industrial bearings, the loads and rotation speeds of wind turbine bearings change considerably because of dynamic wind flows. In the case of some extreme operating conditions, large-scale wind turbine bearings suffer excessive loads and they can be potentially damaged. Therefore, it is essential to develop reliable and cost-effective condition monitoring and fault diagnosis methods to assess the damage level and failure modes so that a proper maintenance plan can be designed. This paper aims at systematically and comprehensively summarizing current large-scale wind turbine bearing failure modes and condition monitoring and fault diagnosis achievements. Firstly, the representative failure modes of large-scale wind turbine bearings are reviewed in detail which can help to understand the causes and effects of the tribological issues of these bearings. Then, condition monitoring and fault diagnosis methods of large-scale wind turbine bearings are presented; within which failure modes, experimental scale and signal processing approaches are summarized. Finally, a number of popular condition monitoring and fault diagnosis approaches that can be potentially used for wind turbine bearings are reviewed, followed by a brief summary of future research directions for wind turbine bearing fault diagnosis. (C) 2019 Elsevier Ltd. All rights reserved.

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