Evaluating the challenges associated with the long-term reliable operation of industrial wind turbine gearboxes

Junior, Valter Luiz Jantara; Basoalto, Hector; Dong, Hanshan; Márquez, Fausto Pedro Garcı́a; Papaelias, Mayorkinos

doi:10.1088/1757-899x/454/1/012094

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…14,15 There are some studies about WT failure conditions and diagnosis by statistical and mathematical methods applied to the SCADA data. 16,17 It is generating a large number of false alarms. 18,19 Therefore, advanced and novel condition monitoring systems (CMSs) are required to increase the capabilities of current systems and integrate the real-time data acquisition with advanced failure diagnosis method.…”

Section: Introductionmentioning

confidence: 99%

Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring

Márquez

Sánchez

Ramírez

2021

Structural Health Monitoring

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Wind energy is considered as one of the most important renewable energies in the world, employing larger and more complex wind turbines. They need novel condition monitoring systems to ensure the reliability, availability, safety and maintainability of the main components of the wind turbines. It leads to early fault detection, increasing the productivity and minimizing the maintenance costs and downtimes. This article proposes a novel non-destructive testing system to analyse acoustically rotatory devices of wind turbines. It captures the noise emitted by the devices using an acoustic condition monitoring system embedded in an unmanned aerial vehicle. The signal acquired is sent to ground computer station for recording and analysing the data. It uses a test rig, previously validated, to carry out a set of experiments to simulate the main faults. A signal processing method is done by wavelet transforms that filters and analyses the energy patterns of the signals. The results are analysed qualitatively and quantitatively considering different scenarios. A statistical analysis is developed to compare the numerical results provided by different wavelet transform families and convolutional neural network. It is concluded that Symlets and Daubechies families report equivalent results for this case study. The accuracies of the results are more than 75%, reaching up to 100%. The approach is validated employing Friedman test.

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Section: Introductionmentioning

confidence: 99%

Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring

Márquez

Sánchez

Ramírez

2021

Structural Health Monitoring

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“…The monitoring techniques are based in performance monitoring, vibration (for gearbox and mechanic monitoring), acoustic emissions (this analysis provides data about waves generated by the failures) [15,16], thermal, oil and ultrasonic analysis [17], among others. Several types of maintenance operations are developed for incrementing the reliability of the energy production [18]. Industrial CMS provide information about the real state of the WT, being possible the identification of potential failures using different data analysis [19].…”

Section: Introductionmentioning

confidence: 99%

Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management

Ramírez

Márquez

2020

Proceedings of the Fourteenth International Conference on Management Science and Engineering Management

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Wind energy is growing to become a competitive energy source. An efficient wind turbine maintenance management is required for ensuring the reliability of the energy production and the costs reduction. Supervisory control and data acquisition system provide information about the condition of the wind turbine by signals of the different subsystems and alarm activations in case of failure or malfunction. Due to the volume and variety of the data, operators require advanced analytics to control the performance of the wind turbines and the identification and prediction of failures. The novelty proposed in this work is based on statistical analysis for analyzing supervisory control and data acquisition data to optimize the use of the data in neural networks. The first phase is the alarm analysis, quantifying the critical alarms regarding on the number and time of activation. A filtering algorithm is developed for considering only interest periods with enough range to make the study. The second phase is based on the initial data treatment, classifying alarms and signals identifying the interest time periods. Neural network is defined and trained for evaluating the signal trends, with the aim of detecting the alarm activations cause. This information will be used in the maintenance management plan for programming maintenance tasks.

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Loading on a wind turbine high-speed shaft gearbox bearing: Ultrasonic field measurements and predictions from a multi-body simulation

Clarke

Nicholas

Hart

et al. 2023

Tribology International

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Evaluating the challenges associated with the long-term reliable operation of industrial wind turbine gearboxes

Cited by 4 publications

References 12 publications

Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring

Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring

Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management

Loading on a wind turbine high-speed shaft gearbox bearing: Ultrasonic field measurements and predictions from a multi-body simulation

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