Abstract
Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic-emission system towards lubricating conditions. In addition, the system has shown that a damaged surface can be detected at least ∼ 4 % (8 h, regarding the time to failure) earlier than by using the vibration-based system. Furthermore, significant deviations from the average acoustic-emission signal were detected up to ∼ 50 % (130 h) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.
Highlights
A large part of downtimes and repair costs of wind turbines (WTs) is caused by gearbox roller bearing failures (Sheng, 2015)
These results were confirmed in Mokhtari et al (2018), and it was found that the kurtosis values of the acoustic emission (AE) signals and the consideration of the mean frequencies can be used for a distinction between mixed-friction and full-lubrication regimes
Within the scope of this work, the analysis of the AE system sensitivities towards operating conditions causing roller bearing failures, the assessment of AE regarding the potential for premature detection of bearing damages and the application of AE to multi-bearing test rigs shall contribute to the state of the art
Summary
A large part of downtimes and repair costs of wind turbines (WTs) is caused by gearbox roller bearing failures (Sheng, 2015). Condition monitoring systems (CMSs) in wind turbines monitor vibration, temperatures and oil contamination (Tchakoua et al, 2014) These parameters are very well suited to detect any macroscopic damages of individual components such as gearbox bearings. The classical CMSs are, not applicable to the detection of damage pre-stages, like crack initiation or propagation, nor are they applicable to the identification of critical operating conditions, such as lowlubrication regimes or excessive sliding. For this purpose, the use of acoustic emission (AE) could be a suitable solution. The second part is based on unpublished research (statement regarding damage detection times)
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