Skip to main content

Oil Level Prediction of Wind Power Gearbox Based on Current Analysis

Buy Article:

$105.00 plus tax (Refund Policy)

This paper proposes an oil/lubrication level recognition (LLR) approach for a gearbox of a wind power system. The approach can recognize the lubrication level of a gearbox by only using generator current without any additional measurement apparatus such as accelerometers for measuring vibration signals. First, in the study, the 11 lubrication levels from a full to completely empty were tailor-made at every 10% intervals. The generator current signals of the lubrication levels of the gearbox were measured by using the dynamometer test bed. Second, the frequency spectrums of the current signals were generated via Fast Fourier Transform (FFT) and several features were extracted from the FFT spectrums. Finally, the recognition accuracies obtained by using the k-nearest neighbor (KNN) and back propagation (BP) are discussed based on the optimal feature combination. The results indicate that the KNN-based LLR approach can efficiently recognize lubrication levels even under noise interference.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics


Document Type: Research Article

Publication date: 2012-05-01

More about this publication?
  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more