Property requirements of vibration measurements in wind turbine drivetrain 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.

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Wind Turbine Main Bearing Condition Monitoring via Convolutional Autoencoder Neural Networks

10.1109/iceccme52200.2021.9590937 ◽

2021 ◽

Author(s):

Christian Tutiv'en ◽

Carlos Benalcazar-Parra ◽

Angel Encalada-D'avila Escuela ◽

Yolanda Vidal ◽

Bryan Puruncaias ◽

...

Keyword(s):

Neural Networks ◽

Wind Turbine ◽

Condition Monitoring ◽

Main Bearing ◽

Bearing Condition Monitoring ◽

Convolutional Autoencoder

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

Energies ◽

10.3390/en13133406 ◽

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.

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Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Energies ◽

10.3390/en13061474 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1474 ◽

Cited By ~ 4

Author(s):

Francesco Castellani ◽

Luigi Garibaldi ◽

Alessandro Paolo Daga ◽

Davide Astolfi ◽

Francesco Natili

Keyword(s):

Wind Turbine ◽

Condition Monitoring ◽

Wind Turbines ◽

Statistical Tests ◽

Novelty Detection ◽

General Idea ◽

Data Set ◽

Vibration Measurements ◽

Bearing Fault ◽

Operation Conditions

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

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Mine Hoist Bearing Condition Monitoring and Fault Diagnosis System Based on Labview

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.317-319.1232 ◽

2011 ◽

Vol 317-319 ◽

pp. 1232-1236

Author(s):

Li Rong Wan ◽

Guang Yu Zhou ◽

Cheng Long Wang ◽

Wen Ming Zhao

Keyword(s):

Fault Diagnosis ◽

Program Development ◽

Condition Monitoring ◽

Virtual Instrument ◽

Signal Analysis ◽

Rolling Bearing ◽

Bearing Failure ◽

Bearing Condition Monitoring ◽

Fault Diagnosis System ◽

Mine Hoist

By taking full advantage of the technologies of data acquisition, signal analysis and processing and fault diagnosis, this thesis carries out a research on the realization method of mine hoist bearing condition monitoring and fault diagnosis. Firstly, this thesis takes a technical analysis for rolling bearing. Secondly, based on determining the overall framework and using a virtual instrument software (Labview), it carries out a program development of the system. The developed system not only integrates the functions of traditional instruments, but also describes the bearing states and the types of bearing failure accurately according to the running status of the monitored bearings. It provides technical support for the mine hoist repair and maintenance and scientific protection for its safe running.

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