Wind industry experiences a tremendous growth during the last few decades. As of the end of 2016, the worldwide total installed electricity generation capacity from wind power amounted to 486,790 MW, presenting an increase of 12.5% compared to the previous year. Nowadays wind turbine manufacturers tend to adopt new business models proposing total health monitoring services and solutions, using regular inspections or even embedding sensors and health monitoring systems within each unit. Regularly planned or permanent monitoring ensures a continuous power generation and reduces maintenance costs, prompting specific actions when necessary. The core of wind turbine drivetrain is usually a complicated planetary gearbox. One of the main gearbox components which are commonly responsible for the machinery breakdowns are rolling element bearings. The failure signs of an early bearing damage are usually weak compared to other sources of excitation (e.g., gears). Focusing toward the accurate and early bearing fault detection, a plethora of signal processing methods have been proposed including spectral analysis, synchronous averaging and enveloping. Envelope analysis is based on the extraction of the envelope of the signal, after filtering around a frequency band excited by impacts due to the bearing faults. Kurtogram has been proposed and widely used as an automatic methodology for the selection of the filtering band, being on the other hand sensible in outliers. Recently, an emerging interest has been focused on modeling rotating machinery signals as cyclostationary, which is a particular class of nonstationary stochastic processes. Cyclic spectral correlation and cyclic spectral coherence (CSC) have been presented as powerful tools for condition monitoring of rolling element bearings, exploiting their cyclostationary behavior. In this work, a new diagnostic tool is introduced based on the integration of the cyclic spectral coherence (CSC) along a frequency band that contains the diagnostic information. A special procedure is proposed in order to automatically select the filtering band, maximizing the corresponding fault indicators. The effectiveness of the methodology is validated using the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking dataset which includes various faults with different levels of diagnostic complexity.
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March 2019
Research-Article
Vibration-Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis
Alexandre Mauricio,
Alexandre Mauricio
Division PMA,
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Search for other works by this author on:
Junyu Qi,
Junyu Qi
Division PMA,
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Search for other works by this author on:
Konstantinos Gryllias
Konstantinos Gryllias
Division PMA,
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
e-mail: konstantinos.gryllias@kuleuven.be
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
e-mail: konstantinos.gryllias@kuleuven.be
Search for other works by this author on:
Alexandre Mauricio
Division PMA,
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Junyu Qi
Division PMA,
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
Konstantinos Gryllias
Division PMA,
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
e-mail: konstantinos.gryllias@kuleuven.be
Department of Mechanical Engineering,
Faculty of Engineering Science,
KU Leuven,
Dynamics of Mechanical
and Mechatronic Systems,
Flanders Make,
Celestijnenlaan 300, BOX 2420,
Leuven 3001, Belgium
e-mail: konstantinos.gryllias@kuleuven.be
1Corresponding author.
Manuscript received June 24, 2018; final manuscript received July 17, 2018; published online November 1, 2018. Editor: Jerzy T. Sawicki.
J. Eng. Gas Turbines Power. Mar 2019, 141(3): 031026 (8 pages)
Published Online: November 1, 2018
Article history
Received:
June 24, 2018
Revised:
July 17, 2018
Citation
Mauricio, A., Qi, J., and Gryllias, K. (November 1, 2018). "Vibration-Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis." ASME. J. Eng. Gas Turbines Power. March 2019; 141(3): 031026. https://doi.org/10.1115/1.4041114
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