Fleet-wide analytics on field data targeting condition and lifetime aspects of wind turbine drivetrains
Daems, P.-J.; Peeters, C.; Matthys, J.; Verstraeten, T.; Helsen, J. (2023). Fleet-wide analytics on field data targeting condition and lifetime aspects of wind turbine drivetrains. Forschung im Ingenieurwesen-Engineering Research 87(1): 285-295. https://dx.doi.org/10.1007/s10010-023-00643-0
In: Forschung im Ingenieurwesen-Engineering Research. SPRINGER HEIDELBERG: Heidelberg. ISSN 0015-7899; e-ISSN 1434-0860, meer
Drivetrain failures result in the largest downtime per failure among the different turbine components. To minimize O&M costs, it is therefore essential to be able to anticipate failure events sufficiently in advance such that scheduled maintenance can take place. Moreover, a root cause for the failure should be identified, allowing to incorporate this knowledge in future design iterations, thereby increasing the reliability of the machine. In offshore wind energy, high-frequency SCADA (1 Hz) and vibration data (>20 kHz) are becoming increasingly available to monitor machine performance and health. This paper presents a twofold approach for monitoring drivetrain health and load history based on these two data sources. First, SCADA data is used to extract different design load cases (DLCs), described in IEC 61400‑3. Second, vibration data is used for advanced signal analysis to detect potential incipient bearing or gear defects in the drivetrain. It is shown that the efficacy of this vibration analysis is further enhanced by combining it with operating condition information from the SCADA data.
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