IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i17p3373-d263118.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/17/3373/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/17/3373/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Unai Elosegui & Igor Egana & Alain Ulazia & Gabriel Ibarra-Berastegi, 2018. "Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms," Energies, MDPI, vol. 11(12), pages 1-20, December.
    2. Pere Marti-Puig & Alejandro Blanco-M & Juan José Cárdenas & Jordi Cusidó & Jordi Solé-Casals, 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction," Energies, MDPI, vol. 12(3), pages 1-18, January.
    3. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    4. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
    5. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    6. Estefania Artigao & Sofia Koukoura & Andrés Honrubia-Escribano & James Carroll & Alasdair McDonald & Emilio Gómez-Lázaro, 2018. "Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train," Energies, MDPI, vol. 11(4), pages 1-18, April.
    7. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    8. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
    9. Alejandro Blanco-M. & Karina Gibert & Pere Marti-Puig & Jordi Cusidó & Jordi Solé-Casals, 2018. "Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools," Energies, MDPI, vol. 11(4), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    2. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    3. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    4. Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    5. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    6. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    7. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    8. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    9. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    10. Dao, Phong B., 2022. "Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data," Renewable Energy, Elsevier, vol. 185(C), pages 641-654.
    11. Davide Astolfi & Francesco Castellani, 2019. "Wind Turbine Power Curve Upgrades: Part II," Energies, MDPI, vol. 12(8), pages 1-20, April.
    12. Koukoura, Sofia & Scheu, Matti Niclas & Kolios, Athanasios, 2021. "Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    13. Pere Marti-Puig & Alejandro Blanco-M & Juan José Cárdenas & Jordi Cusidó & Jordi Solé-Casals, 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction," Energies, MDPI, vol. 12(3), pages 1-18, January.
    14. Masoud Asgarpour & John Dalsgaard Sørensen, 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms," Energies, MDPI, vol. 11(2), pages 1-17, January.
    15. Mérigaud, Alexis & Ringwood, John V., 2016. "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 53-78.
    16. Ruiming, Fang & Minling, Wu & xinhua, Guo & Rongyan, Shang & Pengfei, Shao, 2020. "Identifying early defects of wind turbine based on SCADA data and dynamical network marker," Renewable Energy, Elsevier, vol. 154(C), pages 625-635.
    17. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    18. Hines, Eric M. & Baxter, Christopher D.P. & Ciochetto, David & Song, Mingming & Sparrevik, Per & Meland, Henrik J. & Strout, James M. & Bradshaw, Aaron & Hu, Sau-Lon & Basurto, Jorge R. & Moaveni, Bab, 2023. "Structural instrumentation and monitoring of the Block Island Offshore Wind Farm," Renewable Energy, Elsevier, vol. 202(C), pages 1032-1045.
    19. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    20. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3373-:d:263118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.