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Research Papers

# Vibration-Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis

[+] Author and Article Information
Alexandre Mauricio, 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

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

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 141(3), 031026 (Nov 01, 2018) (8 pages) Paper No: GTP-18-1329; 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. 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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## References

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## Figures

Fig. 1

Flowchart of EESFO

Fig. 2

Representation of the wind turbine components and its measured gearbox [12]

Fig. 3

Planetary gearbox representation of bearing location and gear stages [12]

Fig. 4

Cyclic spectral coherence map of sensor AN7 zoom around the BPFI for: (a) damaged case and (b) healthy case

Fig. 5

EESFO criterion for the selection of band of integration of CSC for HSS downwind bearing BPFI (345.3 Hz) optimization for sensor AN7 for: (a) healthy and (b) damaged

Fig. 6

EESFO for the BPFI detection of the HSS downwind bearing damage corresponding to sensor AN7

Fig. 7

Zoom of AN7 sensor data EESFO around the BPFI

Fig. 8

EESFO criterion for the selection of band of integration of CSC for IMS downwind bearing BPFO (105 Hz) optimization for sensor AN6 for: (a) healthy and (b) damaged

Fig. 9

EESFO for the BPFO detection of the IMS downwind bearing damage corresponding to sensor AN6 zoomed around: (a) the BPFO and (b) third BPFO

Fig. 10

Zoom of the EESFO around the: (a) BPFI of the IMS upwind bearing and (b) third harmonic of the BPFI of the IMS upwind bearing

Fig. 11

Envelope spectrum with kurtogram for AN5

Fig. 12

EESFO for the BPFO detection of the PLC upwind bearing damage corresponding to sensor AN5

## Errata

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