Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods

2021 ◽  
Author(s):  
Felix C. Mehlan ◽  
Amir R. Nejad ◽  
Zhen Gao
Keyword(s):  
Wind Turbine ◽  
Fatigue Damage ◽  
Wind Turbine Gearbox ◽  
Local Loads ◽  
Vibration Data ◽  
Novel Approach ◽  
Torque Measurements ◽  
Continuous Estimation ◽  
Dynamic States

Abstract In this article a novel approach for the estimation of wind turbine gearbox loads with the purpose of online fatigue damage monitoring is presented. The proposed method employs a Digital Twin framework and aims at continuous estimation of the dynamic states based on CMS vibration data and generator torque measurements from SCADA data. With knowledge of the dynamic states local loads at gearbox bearings are easily determined and fatigue models are be applied to track the accumulation of fatigue damage. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered IMS and HSS bearings show moderate to high correlation (R = 0.50–0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15 % from measurements.

scholarly journals The Effect of Parallel-Axis Helix Gear Pair on Wind Turbine Gearbox Vibration Control

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zheng Li ◽  
Tianhe Zhang ◽  
Yang Chen ◽  
Lijuan Song
Keyword(s):  
Wind Turbine ◽  
Helix Angle ◽  
Drive Train ◽  
Gear Pair ◽  
Wind Turbine Gearbox ◽  
Multibody Dynamic ◽  
Gearbox Vibration ◽  
Basic Parameters ◽  
Parallel Axis

This article studies the effects of some basic parameters of a parallel-axis helix gear stage on wind turbine gearbox vibration in a case study: a multibody dynamic model is constructed to simulate the drive train of a faulted multistage wind turbine gearbox with serious vibrations. The significant vibration behaviour of the drive train for typical excitations is calculated, and the results according to specified geometric parameters of the gears are analysed in detail to investigate effective solutions for vibration reduction. The results indicate that the helix angle and numbers of teeth of a gear pair are the most significant factors for solving the problem. The effectiveness of the proposed solutions and relevant mechanisms are discussed and validated by a prototype vibration test.

scholarly journals Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2339 ◽  
Author(s):  
Aijun Yin ◽  
Yinghua Yan ◽  
Zhiyu Zhang ◽  
Chuan Li ◽  
René-Vinicio Sánchez
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Short Term Memory ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Lstm Network ◽  
Wavelet Energy Entropy ◽  
Improve Cost

The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.

scholarly journals Significance of Effective Lubrication in Mitigating System Failures — A Wind Turbine Gearbox Case Study

2014 ◽  
Vol 38 (4) ◽  
pp. 441-449 ◽  
Author(s):  
Yashwant Sinha ◽  
John A Steel ◽  
Jesse A Andrawus ◽  
Karen Gibson
Keyword(s):  
Wind Turbine ◽  
Wind Turbine Gearbox ◽  
System Failures

Modeling and Prediction of Gearbox Faults With Data-Mining Algorithms

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Anoop Verma ◽  
Zijun Zhang ◽  
Andrew Kusiak
Keyword(s):  
Wind Turbine ◽  
Fast Fourier Transformation ◽  
Crest Factor ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Data Mining Algorithms ◽  
Unseen Data ◽  
Data Driven Approach ◽  
Advance Time ◽  
Mining Algorithms

A data-driven approach for analyzing faults in wind turbine gearbox is developed and tested. More specifically, faults in a ring gear are predicted in advance. Time-domain statistical metrics, such as jerk, root mean square (RMS), crest factor (CF), and kurtosis, are investigated to identify faulty components of a wind turbine. The components identified are validated with the fast Fourier transformation (FFT) of vibration data. Fifty neural networks (NNs) with different parameter settings are trained to obtain the best performing model. Models based on original vibration data, and transformed jerk data are constructed. The jerk model based on multisensor data outperforms the other models and therefore is used for testing and validation of previously unseen data. Short-term predictions of up to 15 time intervals, each representing 0.1 s, are performed. The prediction accuracy varies from 91.68% to 94.78%.

Knocking Sounds in the Wind Turbine Gearbox During Slowing Down—Case Study

2017 ◽  
pp. 235-244 ◽  
Author(s):  
Tomasz Barszcz ◽  
Rafał Gawarkiewicz ◽  
Adam Jabłoński ◽  
Michał Sękal ◽  
Michał Wasilczuk
Keyword(s):  
Wind Turbine ◽  
Wind Turbine Gearbox ◽  
Slowing Down

Wind Turbine Gearbox Planetary Stage Gear Damage Detection Using Vibration Data

2014 ◽  
Author(s):  
Jesse Hanna ◽  
Huageng Luo
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Variable Speed ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Planet Gear ◽  
Condition Monitoring System ◽  
The Many ◽  
Many Sources

Effective vibration based condition monitoring applied to the planetary stage of a wind turbine gearbox has been historically difficult. Numerous complications associated with the low speed and variable speed nature of a wind turbine gearbox as well as the many sources of vibration signal modulation and poor vibration transmission paths within the planetary stage itself have presented complex challenges around effectively monitoring the health of planetary stage components. The focus of this paper is the vibration behavior of planetary stage gear related damage and how this behavior can be accurately identified using vibration data. The theory behind this behavior and a case history showing the successful detection of planet gear damage and ring gear damage is presented. The damage detailed in this case is clearly identifiable in the data provided by the ADAPT.Wind condition monitoring system. Although this type of damage requires a gearbox replacement, prompt detection is important to avoid the risk of splitting the gearbox casing and damaging additional wind turbine components.

scholarly journals Sensor selection for cost-effective virtual torque measurements on a wind turbine gearbox

2021 ◽  
Author(s):  
Cedric Cappelle ◽  
Michiel Cattebeke ◽  
Jelle Bosmans ◽  
Matteo Kirchner ◽  
Jan Croes ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Cost Effective ◽  
Sensor Selection ◽  
Wind Turbine Gearbox ◽  
Torque Measurements ◽  
Selection For
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