scholarly journals An Integrated Approach Using Condition Monitoring and Modeling to Investigate Wind Turbine Gearbox Design

2015 ◽  
Author(s):  
Shuangwen Sheng ◽  
Yi Guo
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
High Speed ◽  
Integrated Approach ◽  
Intermediate Stage ◽  
Wind Turbine Gearbox ◽  
First Order ◽  
Bearing Loads ◽  
Design Options ◽  
Utility Scale

Vibration-based condition monitoring (CM) of geared utility-scale turbine drivetrains has been used by the wind industry to help improve operation and maintenance (O&M) practices, increase turbine availability, and reduce O&M cost. This study is a new endeavor that integrates the vibration-based CM technique with wind turbine gearbox modeling to investigate various gearbox design options. A team of researchers performed vibration-based CM measurements on a damaged wind turbine gearbox with a classic configuration, (i.e., one planetary stage and two parallel stages). We observed that the acceleration amplitudes around the first-order sidebands of the intermediate stage gear set meshing frequency were much lower than that measured at the high-speed gear set, and similar difference was also observed in a healthy gearbox. One factor for a reduction at the intermediate stage gear set is hypothesized to be the soft sun-spline configuration in the test gearbox. To evaluate this hypothesis, a multibody dynamic model of the healthy test gearbox was first developed and validated. Relative percent difference of the first-order sidebands — of the high-speed and intermediate stage gear-meshing frequencies — in the soft and the rigid sun spline configurations were compared. The results verified that the soft sun-spline configuration can reduce the sidebands of the intermediate stage gear set and also the locating bearing loads. The study demonstrates that combining vibration-based CM with appropriate modeling can provide insights for evaluating different wind turbine gearbox design options.

scholarly journals Calculating loads and life-time reduction of wind turbine gearbox and generator bearings due to shaft misalignment

2020 ◽  
pp. 0309524X2091402 ◽  
Author(s):  
Damian P Rommel ◽  
Dario Di Maio ◽  
Tiedo Tinga
Keyword(s):  
Wind Turbine ◽  
Analytical Method ◽  
High Speed ◽  
Life Time ◽  
Universal Joint ◽  
Wind Turbine Gearbox ◽  
Bearing Failures ◽  
Shaft Misalignment ◽  
Metal Disk ◽  
Bearing Loads

During the last two decades, wind turbine industries have faced high failure rates, downtimes and costly repairs. Gearbox and generator have contributed to this, especially, because their high speed shaft bearings have often failed. In this article, an analytical method is proposed to calculate the reaction loads of flexible connecting couplings installed between wind turbine gearbox and generator. Raction loads are determined from joint kinematics and metal disk pack deformations as well as axial and angular shaft misalignment. The calculations are executed for both flexible connecting couplings and a universal joint shaft and applied to the gearbox high speed shaft. The performance of flexible connecting couplings and universal joint shaft is compared with respect to the bearing loads and life-time of the gearbox high speed shaft. It is shown that the early, unplanned bearing failures of gearbox and generator high speed shaft can often be attributed to the flexible connecting couplings installed between gearbox and generator.

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.

scholarly journals Investigation of Roller Sliding in Wind Turbine Gearbox High-Speed-Shaft Bearings

2019 ◽  
Author(s):  
David Vaes ◽  
Yi Guo ◽  
Pietro Tesini ◽  
Jonathan A Keller
Keyword(s):  
Wind Turbine ◽  
High Speed ◽  
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.

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