A review of wind turbine bearing condition monitoring: State of the art and challenges

Wind turbine drivetrain bearing failures continue to lead to high costs resulting from turbine downtime and maintenance. As the standardised tool to best avoid downtime is online vibration condition monitoring, a lot of research into improving the signal analysis tools of the vibration measurements is currently being performed. However, failures in the main bearing and planetary gears are still going undetected in large numbers. The available field data is limited when it comes to the properties of the stored measurements. Generally, the measurement time and the covered frequency range of the stored measurements are limited compared to the data used in real-time monitoring. Therefore, it is not possible to either reproduce the monitoring or to evaluate new tools developed through research for signal analysis and diagnosis using the readily available field data. This study utilises 12 bearing failures from wind turbine condition monitoring systems to evaluate and make recommendations concerning the optimal properties in terms of measurement time and frequency range the stored measurements should have. The results show that the regularly stored vibration measurements that are available today are, throughout most of the drivetrain, not optimal for research-driven postfailure investigations. Therefore, the storage of longer measurements covering a wider frequency range needs to begin, while researchers need to demand this kind of data.

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Bearing Condition Monitoring and Fault Diagnosis of a Wind Turbine Using Parameter Free Detection

Lecture Notes in Electrical Engineering - Communication Systems and Information Technology ◽

10.1007/978-3-642-21762-3_37 ◽

2011 ◽

pp. 289-294 ◽

Cited By ~ 1

Author(s):

Shenggang Yang ◽

Xiaoli Li ◽

Ming Liang

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Condition Monitoring ◽

Bearing Condition Monitoring

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Wind turbine generator bearing Condition Monitoring with NEST method

2012 24th Chinese Control and Decision Conference (CCDC) ◽

10.1109/ccdc.2012.6244033 ◽

2012 ◽

Author(s):

Peng Guo

Keyword(s):

Wind Turbine ◽

Condition Monitoring ◽

Turbine Generator ◽

Wind Turbine Generator ◽

Bearing Condition Monitoring

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Condition Monitoring and Diagnostics of Wind Turbines: A Survey

Volume 8: 26th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2014-35352 ◽

2014 ◽

Cited By ~ 1

Author(s):

Paolo Pennacchi ◽

Pietro Borghesani ◽

Steven Chatterton ◽

Candas Gultekin

Keyword(s):

Wind Energy ◽

Wind Turbine ◽

Condition Monitoring ◽

Wind Turbines ◽

Failure Modes ◽

State Of The Art ◽

The State ◽

Diagnostic Methods ◽

System Architectures ◽

Maintenance Costs

Wind energy conversion is the fastest growing source of electricity generation in the world among the other renewable energy production technologies. Whereas investment costs have decreased over years, operational and maintenance costs of wind turbines are still high, thus attracting the focus of researchers and industrial operators. Classical maintenance techniques, i.e.: run-to-failure and scheduled-preventive maintenance, are still dominant in this sector; however, condition monitoring has gained space in the wind turbine market and new diagnostic methods and techniques are continuously being proposed. Condition monitoring techniques seem the most effective tools to minimize operational and maintenance costs and reduce downtimes by early detection of faults. This paper is aimed at reviewing the state of the art of condition monitoring for horizontal axis wind turbines. After a brief introduction presenting the current trends in the market of wind energy, the paper reviews the most common failure modes of wind turbines and the traditional approach to maintenance. The core of this study details the state of the art in the field of system architectures, sensors and signal processing techniques for the diagnostic of faults in wind turbine components. Finally, some general conclusions are drawn on the overall trends in the field of condition monitoring of wind turbines.

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