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

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.

[1]  Jun Wu,et al.  Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network , 2020, Applied Sciences.

[2]  Ashley Crowther,et al.  Sources of time‐varying contact stress and misalignments in wind turbine planetary sets , 2011 .

[3]  M. H. Mathias,et al.  Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron , 2015 .

[4]  Mustafa Demetgul,et al.  Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network , 2014 .

[5]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[6]  Daniel Strömbergsson,et al.  Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms , 2020 .

[7]  Yi Wang,et al.  Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network , 2013, J. Intell. Manuf..

[8]  Qinghua Zhang,et al.  Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests , 2017, IEEE Sensors Journal.

[9]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[10]  K. I. Ramachandran,et al.  A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box , 2008, Expert Syst. Appl..

[11]  Nadège Bouchonneau,et al.  A review of wind turbine bearing condition monitoring: State of the art and challenges , 2016 .

[12]  Zepeng Liu,et al.  A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings , 2020 .

[13]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

[14]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[15]  Diego Cabrera,et al.  Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition , 2015 .

[16]  Idriss El-Thalji,et al.  A summary of fault modelling and predictive health monitoring of rolling element bearings , 2015 .

[17]  Jan Lundberg,et al.  Detection and identification of windmill bearing faults using a one-class support vector machine (SVM) , 2019, Measurement.

[18]  Wenxian Yang,et al.  Wind turbine condition monitoring by the approach of SCADA data analysis , 2013 .

[19]  Sung-Hoon Ahn,et al.  Condition monitoring and fault detection of wind turbines and related algorithms: A review , 2009 .

[20]  Simon J. Watson,et al.  Using SCADA data for wind turbine condition monitoring – a review , 2017 .

[21]  Cristina Castejón,et al.  Automated diagnosis of rolling bearings using MRA and neural networks , 2010 .

[22]  Jay Lee,et al.  A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: fundamentals, concepts and applications , 2020, Measurement Science and Technology.

[23]  Pramod Bangalore,et al.  An artificial neural network‐based condition monitoring method for wind turbines, with application to the monitoring of the gearbox , 2017 .