scholarly journals Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Energies ◽  
2011 ◽  
Vol 4 (11) ◽  
pp. 2077-2093 ◽  
Author(s):  
Peng Guo ◽  
Nan Bai
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Moving Window ◽  
Wind Turbine Gearbox

scholarly journals Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Field Trials ◽  
Health Condition ◽  
Normal Operation ◽  
Wind Turbine Gearbox ◽  
Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

Comparison of Blind Diagnostic Indicators for Condition Monitoring of Wind Turbine Gearbox Bearings

2021 ◽  
Author(s):  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Konstantinos Gryllias
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Fossil Fuels ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Permutation Entropy ◽  
Wind Turbine Gearbox ◽  
Data Set ◽  
Wind Turbine Gearbox Bearings

Abstract Under the pressure of climate change, renewable energy gradually replaces fossil fuels and plays nowadays a significant role in energy production. The O&M costs of wind turbines may easily reach up to 25% of the total leverised cost per kWh produced over the lifetime of the turbine for a new unit. Manufacturers and operators try to reduce O&M by developing new turbine designs and by adopting condition monitoring approaches. One of the most critical assembly of wind turbines is the gearbox. Gearboxes are designed to last till the end of asset's lifetime, according to the IEC 61400-4 standards but a recent study indicated that gearboxes might have to be replaced as early as 6.5 years. A plethora of sensor types and signal processing methodologies have been proposed in order to accurately detect and diagnose the presence of a fault but often the gearbox is equipped with a limited number of sensors and a simple global diagnostic indicator is demanded, being capable to detect globally various faults of different components. The scope of this paper is the application and comparison of a number of blind global diagnostic indicators which are based on Entropy, on Negentropy, on Sparsity and on Statistics. The performance of the indicators is evaluated on a wind turbine data set with two different bearing faults. Among the different diagnostic indicators Permutation entropy, Approximate entropy, Samples entropy, Fuzzy entropy, Conditional entropy and Wiener entropy achieve the best results detecting blindly the two failure events.

scholarly journals Thermal modelling of a small wind turbine gearbox for condition monitoring

2019 ◽  
Vol 2019 (18) ◽  
pp. 5335-5339
Author(s):  
Becky Corley ◽  
James Carroll ◽  
Alasdair McDonald
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Thermal Modelling ◽  
Wind Turbine Gearbox ◽  
Small Wind Turbine

scholarly journals Condition Monitoring of Wind Turbine Gearbox Bearing Based on Deep Learning Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 57078-57087 ◽  
Author(s):  
Jian Fu ◽  
Jingchun Chu ◽  
Peng Guo ◽  
Zhenyu Chen
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Model ◽  
Wind Turbine Gearbox ◽  
Deep Learning Model

Synchronous Analysis in Wind Turbine Gearbox Condition Monitoring

2012 ◽  
Author(s):  
Huageng Luo ◽  
Mingqi Luo ◽  
Shaobo Zhang
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Signal To Noise Ratio ◽  
Sampling Technique ◽  
Signal To Noise ◽  
High Count ◽  
Wind Turbine Gearbox ◽  
Synchronous Sampling ◽  
Accuracy And Precision ◽  
Bearing Damage

This paper summarizes the application of “synchronous sampling” technique in the digital domain to enhance the signal-to-noise ratio in detection of wind turbine gear and bearing damage signatures. In this technique, only a once-per-revolution Key Phaser or a low-count Encoder signal is required. High-count synchronous sampling is achieved by resampling in the digital domain. Implementation and demonstration of the aforementioned techniques are illustrated in two current wind turbine field operation examples. The improvement in the accuracy and precision of damage feature extractions is demonstrated.

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