Vibration Measurements of Wind Turbine Gearbox

2016 ◽  
pp. 77-90
Author(s):  
Qingkai Han ◽  
Jing Wei ◽  
Qingpeng Han ◽  
Hao Zhang
Keyword(s):  
Wind Turbine ◽  
Wind Turbine Gearbox ◽  
Vibration Measurements

scholarly journals Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3092 ◽  
Author(s):  
Elasha ◽  
Shanbr ◽  
Li ◽  
Mba
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
High Speed ◽  
Large Scale ◽  
Remaining Useful Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accurate Estimation ◽  
Wind Turbine Gearbox ◽  
Vibration Measurements

Deployment of large-scale wind turbines requires sophisticated operation and maintenance strategies to ensure the devices are safe, profitable and cost-effective. Prognostics aims to predict the remaining useful life (RUL) of physical systems based on condition measurements. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes to combine two supervised machine learning techniques, namely, regression model and multilayer artificial neural network model, to predict the RUL of an operational wind turbine gearbox using vibration measurements. Root Mean Square (RMS), Kurtosis (KU) and Energy Index (EI) were analysed to define the bearing failure stages. The proposed methodology was evaluated through a case study involving vibration measurements of a high-speed shaft bearing used in a wind turbine gearbox.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

Service Loading Analysis of Wind Turbine Gearbox Rolling Bearings Based on X-Ray Diffraction Residual Stress Measurements

2013 ◽  
Vol 768-769 ◽  
pp. 723-732 ◽  
Author(s):  
Jürgen Gegner ◽  
Wolfgang Nierlich
Keyword(s):  
Residual Stress ◽  
Wind Turbine ◽  
Rolling Contact Fatigue ◽  
Contact Fatigue ◽  
Rolling Contact ◽  
X Ray Diffraction ◽  
Rolling Bearings ◽  
Wind Turbine Gearbox ◽  
X Ray ◽  
Stress Measurements

Rolling bearings in wind turbine gearboxes occasionally fail prematurely by so-called white etching cracks. The appearance of the damage indicates brittle spontaneous tensile stress induced surface cracking followed by corrosion fatigue driven crack growth. An X-ray diffraction based residual stress analysis reveals vibrations in service as the root cause. The occurrence of high local friction coefficients in the rolling contact is described by a tribological model. Depth profiles of the equivalent shear and normal stresses are compared with residual stress patterns and a relevant fracture strength, respectively. White etching crack failures are reproduced on a rolling contact fatigue test rig under increased mixed friction. Causative vibration loading is evident from residual stress measurements. Cold working compressive residual stresses are an effective countermeasure.

scholarly journals Using transfer path analysis to assess the influence of bearings on structural vibrations of a wind turbine gearbox

Wind Energy ◽  
2014 ◽  
Vol 18 (5) ◽  
pp. 797-810 ◽  
Author(s):  
F. Vanhollebeke ◽  
J. Peeters ◽  
D. Vandepitte ◽  
W. Desmet
Keyword(s):  
Path Analysis ◽  
Wind Turbine ◽  
Structural Vibrations ◽  
Wind Turbine Gearbox ◽  
Transfer Path ◽  
Transfer Path Analysis

Optimal Design of Wind Turbine Gearbox using Helix Modification

2013 ◽  
Vol 6 (4) ◽  
pp. 147-151 ◽  
Author(s):  
Young-Jun Park ◽  
Geun-Ho Lee ◽  
Jin-Seop Song ◽  
Yong-Yun Nam ◽  
Ju-Seok Nam
Keyword(s):  
Optimal Design ◽  
Wind Turbine ◽  
Wind Turbine Gearbox
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