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A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring

Author

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  • Kerman López de Calle

    (Intelligent Information Systems unit, IK4-TEKNIKER, Iñaki Goenaga street 5, 20600 Eibar, Spain
    Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Faculty of Informatics 649, 20080 Donostia, Spain)

  • Susana Ferreiro

    (Intelligent Information Systems unit, IK4-TEKNIKER, Iñaki Goenaga street 5, 20600 Eibar, Spain)

  • Constantino Roldán-Paraponiaris

    (Intelligent Information Systems unit, IK4-TEKNIKER, Iñaki Goenaga street 5, 20600 Eibar, Spain)

  • Alain Ulazia

    (Department of NE and Fluid Mechanics, University of the Basque Country (UPV/EHU), Otaola 29, 20600 Eibar, Spain)

Abstract

One of the greatest challenges of optimising the correct operation of wind turbines is detecting the health status of their core components, such as gearboxes in particular. Gearbox monitoring is a widely studied topic in the literature, nevertheless, studies showing data of in-service wind turbines are less frequent and tend to present difficulties that are otherwise overlooked in test rig based works. This work presents the data of three wind turbines that have gearboxes in different damage stages. Besides including the data of the SCADA (Supervisory Control And Signal Acquisition) system, additional measurements of online optical oil debris sensors are also included. In addition to an analysis of the behaviour of particle generation in the turbines, a methodology to identify regimes of operation with lower variation is presented. These regimes are later utilised to develop a health index that considers operation states and provides valuable information regarding the state of the gearboxes. The proposed health index allows distinguishing damage severity between wind turbines as well as tracking the evolution of the damage over time.

Suggested Citation

  • Kerman López de Calle & Susana Ferreiro & Constantino Roldán-Paraponiaris & Alain Ulazia, 2019. "A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring," Energies, MDPI, vol. 12(17), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3373-:d:263118
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    References listed on IDEAS

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    Cited by:

    1. Maria Rosaria Termite & Piero Baraldi & Sameer Al-Dahidi & Luca Bellani & Michele Compare & Enrico Zio, 2019. "A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments," Energies, MDPI, vol. 12(24), pages 1-26, December.
    2. Gonzalo Gil & Aitor Arnaiz & Mariví Higuero & Francisco Javier Diez & Eduardo Jacob, 2022. "Context-Aware Policy Analysis for Distributed Usage Control," Energies, MDPI, vol. 15(19), pages 1-25, September.
    3. Wang, Ziqi & Liu, Changliang & Yan, Feng, 2022. "Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique," Renewable Energy, Elsevier, vol. 184(C), pages 343-360.
    4. Valentin Belopukhov & Andrey Blinov & Sergey Borovik & Mariya Luchsheva & Farit Muhutdinov & Petr Podlipnov & Aleksey Sazhenkov & Yuriy Sekisov, 2022. "Monitoring Metal Wear Particles of Friction Pairs in the Oil Systems of Gas Turbine Power Plants," Energies, MDPI, vol. 15(13), pages 1-15, July.
    5. Francesco Castellani & Luigi Garibaldi & Alessandro Paolo Daga & Davide Astolfi & Francesco Natili, 2020. "Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements," Energies, MDPI, vol. 13(6), pages 1-18, March.
    6. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.
    7. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

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