Vibration Fault Detection in Wind Turbines Based on Normal Behaviour Models without Feature Engineering

نویسندگان

چکیده

Most wind turbines are remotely monitored 24/7 to allow for early detection of operation problems and developing damage. We present a new fault approach vibration-monitored drivetrains that does not require any feature engineering. Our method relies on simple model architecture enable straightforward implementation in practice. propose apply convolutional autoencoders identifying extracting the most relevant features from broad continuous range spectrum an automated manner, saving time effort. focus [0, 1000] Hz demonstration purposes. A spectral normal vibration response is learnt component past measurements. demonstrate trained can successfully distinguish damaged healthy components detect generator bearing gearbox parts their responses. Using measurements commercial test rig, we show vibration-based turbine be performed without usual upfront definition features. Another advantage presented instead monitoring individual frequencies harmonics. Future research should investigate proposed more comprehensive datasets types.

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ژورنال

عنوان ژورنال: Energies

سال: 2023

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en16041760