An interdisciplinary framework to predict premature roller element bearing failures in wind turbine gearboxes

2021 ◽  
Author(s):  
Gopalakrishnan Ravi ◽  
Pieter-Jan Daems ◽  
Ksenija Nikolic ◽  
Wim De Waele ◽  
Jan Helsen ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Bearing Failures ◽  
Interdisciplinary Framework ◽  
Element Bearing

Investigations of Bearing Failures Associated with White Etching Areas (WEAs) in Wind Turbine Gearboxes

2013 ◽  
Vol 56 (6) ◽  
pp. 1069-1076 ◽  
Author(s):  
Robert Errichello ◽  
Robert Budny ◽  
Rainer Eckert
Keyword(s):  
Wind Turbine ◽  
Bearing Failures

The Cause of Premature Wind Turbine Bearing Failures: Overloading or Underloading?

2018 ◽  
Vol 61 (5) ◽  
pp. 850-860 ◽  
Author(s):  
Nikolay T. Garabedian ◽  
Benjamin J. Gould ◽  
Gary L. Doll ◽  
David L. Burris
Keyword(s):  
Wind Turbine ◽  
Bearing Failures ◽  
Turbine Bearing

scholarly journals Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation

2021 ◽  
Vol 11 (8) ◽  
pp. 3588
Author(s):  
Daniel Strömbergsson ◽  
Pär Marklund ◽  
Kim Berglund
Keyword(s):  
Wind Turbine ◽  
Rotational Speed ◽  
Training Data ◽  
Vibration Monitoring ◽  
Vibration Measurement ◽  
Indicator Values ◽  
Wind Turbine Gearbox ◽  
Bearing Failures ◽  
Artificial Neural ◽  
Condition Indicator

The highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the failures are still being reported. Artificial neural networks (ANNs) has been shown to favourably be used as a classifier of bearing failures to increase the detection and diagnosis performance, which requires labelled data when training for all types of considered failures. However, less work has been done with an ANN used to create descriptive functions of the vibration and turbine operation data relationship and thereby negating inherent variance in the vibration data and increasing the detectability when a defect appears. Therefore, this study utilizes the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearbox failures. An ANN establishes a function between the rotational speed and condition indicator values with healthy training data collected before the failure occurred. Thereafter, whole datasets leading up to the changing of the gearboxes is used to predict the condition indicator values without the failure influence. The difference between the predicted and true values show an increased sensitivity of the detection in two cases of gearbox output shaft bearing failures as well as indicating a planet bearing failure which with the previous data had gone undetected.

Property requirements of vibration measurements in wind turbine drivetrain bearing condition monitoring

2021 ◽  
Vol 63 (11) ◽  
pp. 667-674
Author(s):  
D Strömbergsson ◽  
P Marklund ◽  
K Berglund ◽  
P-E Larsson
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Field Data ◽  
Measurement Time ◽  
Frequency Range ◽  
Main Bearing ◽  
Vibration Measurements ◽  
Bearing Failures ◽  
Bearing Condition Monitoring

Wind turbine drivetrain bearing failures continue to lead to high costs resulting from turbine downtime and maintenance. As the standardised tool to best avoid downtime is online vibration condition monitoring, a lot of research into improving the signal analysis tools of the vibration measurements is currently being performed. However, failures in the main bearing and planetary gears are still going undetected in large numbers. The available field data is limited when it comes to the properties of the stored measurements. Generally, the measurement time and the covered frequency range of the stored measurements are limited compared to the data used in real-time monitoring. Therefore, it is not possible to either reproduce the monitoring or to evaluate new tools developed through research for signal analysis and diagnosis using the readily available field data. This study utilises 12 bearing failures from wind turbine condition monitoring systems to evaluate and make recommendations concerning the optimal properties in terms of measurement time and frequency range the stored measurements should have. The results show that the regularly stored vibration measurements that are available today are, throughout most of the drivetrain, not optimal for research-driven postfailure investigations. Therefore, the storage of longer measurements covering a wider frequency range needs to begin, while researchers need to demand this kind of data.

scholarly journals Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 83
Author(s):  
Jürgen Herp ◽  
Niels L. Pedersen ◽  
Esmaeil S. Nadimi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Likelihood Function ◽  
Health Management ◽  
Control Strategies ◽  
Assessment Model ◽  
Prognostic Models ◽  
Early Stopping ◽  
Main Bearing ◽  
Bearing Failures

Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the RUL for wind turbine main bearing failures. The prediction of the RUL is estimated from a likelihood function based on concepts from prognostics and health management, and survival analysis. The RUL is estimated by training the model on run-to-failure wind turbines, extracting a parametrization of a probability density function. In order to ensure analytical moments, a Weibull distribution is assumed. Alongside the RUL model, the fault’s progression is abstracted as discrete states following the bearing stages from damage detection, through overtemperature warnings, to over overtemperature alarms and failure, and are integrated in a separate assessment model. Assuming a naïve O&M plan (wind turbines are run as close to failure as possible without regards for infrastructure or supply chain constrains), 67 non run-to-failure wind turbines are assessed with respect to their early stopping, revealing the potential RUL lost. These are turbines that have been stopped by the operator prior to their failure. On average it was found that wind turbines are stopped 13 days prior to their failure, accumulating 786 days of potentially lost operations across the 67 wind turbines.

scholarly journals Estimating the Remaining Power Generation of Wind Turbines—An Exploratory Study for Main Bearing Failures

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3406
Author(s):  
Benedikt Wiese ◽  
Niels L. Pedersen ◽  
Esmaeil S. Nadimi ◽  
Jürgen Herp
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Feature Space ◽  
Main Bearing ◽  
Proposed Model ◽  
Bearing Failures ◽  
Gated Recurrent Units ◽  
Useful Lifetime

Condition monitoring for wind turbines is tailored to predict failure and aid in making better operation and maintenance (O&M) decisions. Typically the condition monitoring approaches are concerned with predicting the remaining useful lifetime (RUL) of assets or a component. As the time-based measures can be rendered absolute when changing the operational set-point of a wind turbine, we propose an alternative in a power-based condition monitoring framework for wind turbines, i.e., the remaining power generation (RPG) before a main bearing failure. The proposed model utilizes historic wind turbine data, from both run-to-failure and non run-to-failure turbines. Comprised of a recurrent neural network with gated recurrent units, the model is constructed around a censored and uncensored data-based cost function. We infer a Weibull distribution over the RPG, which gives an operator a measure of how certain any given prediction is. As part of the model evaluation, we present the hyper-parameter selection, as well as modeling error in detail, including an analysis of the driving features. During the application on wind turbine main bearing failures, we achieve prediction in the magnitude of 1 to 2 GWh before the failure. When converting to RUL this corresponds to predicting the failure, on average, 81 days beforehand, which is comparable to the state-of-the-art’s 94 days predictive horizon in a similar feature space.

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.

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