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research-article

VIBRATION BASED CONDITION MONITORING OF WIND TURBINE GEARBOXES BASED ON CYCLOSTATIONARY ANALYSIS

[+] Author and Article Information
Alexandre Mauricio

Division PMA, Department of Mechanical Engineering, Faculty of Engineering Science, KU Leuven; Dynamics of Mechanical and Mechatronics Systems, Flanders Make, Celestijnenlaan 300, BOX 2420, 3001 Leuven, Belgium
alex.ricardomauricio@kuleuven.be

Junyu Qi

Division PMA, Department of Mechanical Engineering, Faculty of Engineering Science, KU Leuven; Dynamics of Mechanical and Mechatronics Systems, Flanders Make, Celestijnenlaan 300, BOX 2420, 3001 Leuven, Belgium
junyu.qi@kuleuven.be

Konstantinos Gryllias

Division PMA, Department of Mechanical Engineering, Faculty of Engineering Science, KU Leuven; Dynamics of Mechanical and Mechatronics Systems, Flanders Make, Celestijnenlaan 300, BOX 2420, 3001 Leuven, Belgium
konstantinos.gryllias@kuleuven.be

1Corresponding author.

ASME doi:10.1115/1.4041114 History: Received June 24, 2018; Revised July 17, 2018

Abstract

Wind industry experiences a tremendous growth during the last few decades. 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 reduce 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 towards the accurate and early bearing fault detection, a plethora of signal processing methods have been proposed including spectral analysis, synchronous averaging and enveloping. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary. Cyclic Spectral Correlation and Cyclic Spectral Coherence have been presented as powerful tools for condition monitoring of rolling element bearings. In this work a new diagnostic tool is introduced based on the integration of the Cyclic Spectral Coherence 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 wind turbine gearbox vibration condition monitoring benchmarking dataset.

Copyright (c) 2018 by ASME
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