scholarly journals Comparison of Conventional Method of Fault Determination with Data-Driven Approach for Ball Bearings in a Wind Turbine Gearbox

2018 ◽  
Author(s):  
V. N. Balavignesh
Keyword(s):  
Wind Turbine ◽  
Conventional Method ◽  
Data Driven ◽  
Ball Bearings ◽  
Wind Turbine Gearbox ◽  
Data Driven Approach

Wind turbine health state monitoring based on a Bayesian data-driven approach

2018 ◽  
Vol 125 ◽  
pp. 172-181 ◽  
Author(s):  
Zhe Song ◽  
Zijun Zhang ◽  
Yu Jiang ◽  
Jin Zhu
Keyword(s):  
Wind Turbine ◽  
Data Driven ◽  
Health State ◽  
State Monitoring ◽  
Data Driven Approach

Modeling and Prediction of Gearbox Faults With Data-Mining Algorithms

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Anoop Verma ◽  
Zijun Zhang ◽  
Andrew Kusiak
Keyword(s):  
Wind Turbine ◽  
Fast Fourier Transformation ◽  
Crest Factor ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Data Mining Algorithms ◽  
Unseen Data ◽  
Data Driven Approach ◽  
Advance Time ◽  
Mining Algorithms

A data-driven approach for analyzing faults in wind turbine gearbox is developed and tested. More specifically, faults in a ring gear are predicted in advance. Time-domain statistical metrics, such as jerk, root mean square (RMS), crest factor (CF), and kurtosis, are investigated to identify faulty components of a wind turbine. The components identified are validated with the fast Fourier transformation (FFT) of vibration data. Fifty neural networks (NNs) with different parameter settings are trained to obtain the best performing model. Models based on original vibration data, and transformed jerk data are constructed. The jerk model based on multisensor data outperforms the other models and therefore is used for testing and validation of previously unseen data. Short-term predictions of up to 15 time intervals, each representing 0.1 s, are performed. The prediction accuracy varies from 91.68% to 94.78%.

Wind Turbine Gearbox Fault Detection Using Multiple Sensors With Features Level Data Fusion

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Y. Lu ◽  
J. Tang ◽  
H. Luo
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Principal Component ◽  
Data Driven ◽  
Wind Turbine Gearbox ◽  
Processing Technologies ◽  
Time Frequency ◽  
Multiple Sensors ◽  
Level Data ◽  
Harmonic Wavelet

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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