Fault detection in complex mechanical systems such as wind turbine gearboxes remains challenging, even with the recently significant advancement of sensing and signal processing technologies. As first-principle models of gearboxes capable of reflecting response details for health monitoring purpose are difficult to obtain, data-driven approaches are often adopted for fault detection, identification or classification. In this paper, we propose a data-driven framework that combines information from multiple sensors and fundamental physics of the gearbox. Time domain vibration and acoustic emission signals are collected from a gearbox dynamics testbed, where both healthy and faulty gears with different fault conditions are tested. To deal with the nonstationary nature of the wind turbine operation, a harmonic wavelet based method is utilized to extract the time-frequency features in the signals. This new framework features the employment of the tachometer readings and gear meshing relationships to develop a speed profile masking technique. The time-frequency wavelet features are highlighted by applying the mask we construct. Those highlighted features from multiple accelerometers and microphones are then fused together through a statistical weighting approach based on principal component analysis. Using the highlighted and fused features, we demonstrate that different gear faults can be effectively detected and identified.
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e-mail: jtang@engr.uconn.edu
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April 2012
Research Papers
Wind Turbine Gearbox Fault Detection Using Multiple Sensors With Features Level Data Fusion
Y. Lu,
Y. Lu
Graduate Research Assistant
Department of Mechanical Engineering,
University of Connecticut
, 191 Auditorium Road, Unit 3139, Storrs, CT 06269
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J. Tang,
J. Tang
Associate Professor
Department of Mechanical Engineering,
e-mail: jtang@engr.uconn.edu
University of Connecticut
, 191 Auditorium Road, Unit 3139, Storrs, CT 06269
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H. Luo
H. Luo
Global Technical Leader – Wind, Machinery Diagnostics Services, GE Energy Services
, 1 River Road, Schenectady, NY 12345
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Y. Lu
Graduate Research Assistant
Department of Mechanical Engineering,
University of Connecticut
, 191 Auditorium Road, Unit 3139, Storrs, CT 06269
J. Tang
Associate Professor
Department of Mechanical Engineering,
University of Connecticut
, 191 Auditorium Road, Unit 3139, Storrs, CT 06269e-mail: jtang@engr.uconn.edu
H. Luo
Global Technical Leader – Wind, Machinery Diagnostics Services, GE Energy Services
, 1 River Road, Schenectady, NY 12345J. Eng. Gas Turbines Power. Apr 2012, 134(4): 042501 (8 pages)
Published Online: January 25, 2012
Article history
Received:
May 5, 2011
Revised:
May 9, 2011
Online:
January 25, 2012
Published:
January 25, 2012
Citation
Lu, Y., Tang, J., and Luo, H. (January 25, 2012). "Wind Turbine Gearbox Fault Detection Using Multiple Sensors With Features Level Data Fusion." ASME. J. Eng. Gas Turbines Power. April 2012; 134(4): 042501. https://doi.org/10.1115/1.4004438
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