Vibration analysis for fault detection in wind turbines using machine learning techniques

Vives, Javier

doi:10.1007/s43674-021-00029-1

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…The PCI 4472B performs with a cut-off frequency of only 0.5Hz when AC coupled with very low frequency AC vibration measurements. Audio measurements, fractional octave analysis, frequency analysis, transitory analysis, and order tracking are all performed using the NI Sound and Vibration Measurement Suite and the NI Sound and Vibration Toolkit [20]. Each PCI-4472B card provides 8 inputs, which two PCI-4472B cards.…”

Section: Data Collection and Descriptionmentioning

confidence: 99%

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

Vives¹,

Palací²,

Heart³

2022

JCC

Self Cite

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With the implementation of supervised machine learning techniques, wind turbine maintenance has been transformed. A wind turbine's electrical and mechanical components can be automatically identified, monitored, and detected to predict, detect, and anticipate their degeneration using this method of automatic and autonomous learning. Two different failure states are simulated due to bearing vibrations and compared with machine learning classifier and frequency analysis. A wind turbine can be monitored, monitored, and faulted efficiently by implementing SVM. With these technologies, downtime can be reduced, breakdowns can be anticipated, and aspects can be imported if they are offshore.

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Section: Data Collection and Descriptionmentioning

confidence: 99%

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

Vives¹,

Palací²,

Heart³

2022

JCC

Self Cite

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“…In the predictive maintenance, data-driven solution, by analyzing the data either from supervisory control and data acquisition (SCADA) system [2][3][4][5][6][7] or from condition monitoring system (CMS) [8][9][10][11], has drawn rising interests in performance assessment and fault diagnosis of WTs. Compared to CMS data-based approach, the use of SCADA data has the advantage of avoiding additional expenses from the installation and maintenance of sensors, cables, and dedicated acquisition systems.…”

Section: Introductionmentioning

confidence: 99%

Health assessment of wind turbines based on output power modelling

Yin

2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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Machine learning based performance assessment of wind turbines using the widely available SCADA data has been receiving growing interest. This work proposes a pragmatic health evaluation approach of wind turbines based on output power modelling. For that, random forests regression models to predict output power are trained respectively by ‘normal’ and ‘under-performance’ samples. The trained two models are used to predict output power of testing data. By comparing the absolute values of the residuals of testing data, the working state is identified at each time point. A health index, which is based on the percentiles of ‘normal’ samples in a defined period, is introduced to evaluate the health condition of the wind turbine. The degradation of the HI can give an early warning of the wind turbine. The proposed approach successfully give an early warning for the potential fault around 6 hours prior the traditional SCADA system.

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“…Vibrations are present under normal operating conditions, and are focused on turbine failure situations, such as failures in the outer and inner bearing raceway, rolling element failures, imbalance, and misalignment-all failures that are caused by vibrations [33]. As a result of studying vibrations, combinations of variables can be found that describe main trends and fluctuations within vibration response measured in structures to create a reference state to which new observations for damage diagnosis are evaluated; such methods can be used to detect wind turbine failure in real time [20,29].…”

Section: Introductionmentioning

confidence: 99%

A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions

et al. 2023

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Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%.

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Vibration analysis for fault detection in wind turbines using machine learning techniques

Cited by 12 publications

References 24 publications

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

Health assessment of wind turbines based on output power modelling

A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions

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