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.

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.

Prediction of wind turbine gear micropitting under variable load and speed conditions using ISO/TR 15144-1: 2010

2012 ◽  
Vol 227 (9) ◽  
pp. 1898-1914 ◽  
Author(s):  
Issa S Al-Tubi ◽  
Hui Long
Keyword(s):  
Film Thickness ◽  
Wind Turbine ◽  
Rotational Speed ◽  
Gear Tooth ◽  
Lubricant Film ◽  
Tooth Surface ◽  
Variable Load ◽  
Lubricant Film Thickness ◽  
Wind Turbine Gearbox ◽  
Tooth Flank

Wind turbine gearbox operates under a wide array of highly fluctuating and dynamic load conditions caused by the stochastic nature of wind and operational wind turbine controls. Micropitting damage is one of failure modes commonly observed in wind turbine gearboxes. This article investigates gear micropitting of high-speed stage gears of a wind turbine gearbox operating under nominal and varying load and speed conditions. Based on the ISO standard of gear micropitting (ISO/TR 15144-1:2010) and considering the operating load and speed conditions, a theoretical study is carried out to assess the risk of gear micropitting by determining the contact stress, sliding parameter, local contact temperature and lubricant film thickness along the line of action of gear tooth contact. The non-uniform distributions of temperature and lubricant film thickness over the tooth flank are observed due to the conditions of torque and rotational speed variations and sliding contact along the gear tooth flanks. The lubricant film thickness varies along the tooth flank and is at the lowest when the tip of the driving gear engages with the root of the driven gear. The lubricant film thickness increases with the increase of rotational speed and decreases as torque and sliding increase. It can be concluded that micropitting is most likely to initiate at the addendum of driving gear and the dedendum of driven gear. The lowest film thickness occurs when the torque is high and the rotational speed is at the lowest which may cause direct tooth surface contact. At the low-torque condition, the varying rotational speed condition may cause a considerable variation of lubricant film thickness thus interrupting the lubrication which may result in micropitting.

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.

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.

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