Issue
Mechanics & Industry
Volume 25, 2024
Recent advances in vibrations, noise, and their use for machine monitoring
Article Number 28
Number of page(s) 11
DOI https://doi.org/10.1051/meca/2024028
Published online 23 October 2024
  1. Q. Wang, Z. Dong, R. Li, L. Wang, Renewable energy and economic growth: new insight from country risks, Energy 238, 122018 (2022) [CrossRef] [Google Scholar]
  2. M.I.H. Tusar, B.R. Sarker, Maintenance cost minimization models for offshore wind farms: A systematic and critical review, Int. J. Energy Res. 46, 3739–3765 (2022) [CrossRef] [Google Scholar]
  3. Z. Liu, L. Zhang, A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings, Measurement 149, 107002 (2020) [CrossRef] [Google Scholar]
  4. H. Gu, W.Y. Liu, Q.W. Gao, Y. Zhang, A review on wind turbines gearbox fault diagnosis methods, J. Vibroeng. 23, 26–43 (2022) [Google Scholar]
  5. M.D. Reder, E. Gonzalez, J.J. Melero, Wind turbine failures-tackling current problems in failure data analysis, J. Phys.: Conf. Ser. 753, 072027 (2016) [CrossRef] [Google Scholar]
  6. F. Castellani, A. Garinei, L. Terzi, D. Astolfi, M. Gaudiosi, Improving windfarm operation practice through numerical modelling and supervisory control and data acquisition data analysis, IET Renew. Power Generat. 8, 367–379 (2014) [CrossRef] [Google Scholar]
  7. F. Harrou, K.R. Kini, M. Madakyaru, Y. Sun, Uncovering sensor faults in wind turbines: an improved multivariate statistical approach for condition monitoring using SCADA data, Sustain. Energy Grids Netw. 35, 101126 (2023) [CrossRef] [Google Scholar]
  8. D. Astolfi, F. Castellani, L. Terzi, Mathematical methods for SCADA data mining of onshore wind farms: Performance evaluation and wake analysis, Wind Eng. 40, 69–85 (2016) [CrossRef] [Google Scholar]
  9. X. Chesterman, T. Verstraeten, P.J. Daems, A. Nowé, J. Helsen, Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms, Wind Energy Sci. 8, 893–924 (2023) [CrossRef] [Google Scholar]
  10. R. Pandit, D. Astolfi, J. Hong, D. Infield, M. Santos, SCADA data for wind turbine data-driven condition/performance monitoring: a review on state-of-art, challenges and future trends, Wind Eng. 47, 422–441 (2023) [CrossRef] [Google Scholar]
  11. S.Y. Oh et al., Condition-based maintenance of wind turbine structures: a state-of-the-art review, Renew. Sustain. Energy Rev. 204, 114799 (2024) [CrossRef] [Google Scholar]
  12. F. Castellani, A. Garinei, L. Terzi, D. Astolfi, M. Gaudiosi, Improving windfarm operation practice through numerical modelling and supervisory control and data acquisition data analysis, IET Renew. Power Generat. 8, 367–379 (2014) [CrossRef] [Google Scholar]
  13. D. Astolfi, F. Castellani, L. Terzi, Mathematical methods for SCADA data mining of onshore wind farms: Performance evaluation and wake analysis, Wind Eng. 40, 69–85 (2016) [CrossRef] [Google Scholar]
  14. Y. Liu, L. Zhang, Data-driven fault identification of ageing wind turbine, in 2022 UKACC 13th international conference on Control (CONTROL). IEEE (2022) [Google Scholar]
  15. D. Astolfi, R. Byrne, F. Castellani, Analysis of wind turbine aging through operation curves, Energies 13, 5623 (2020) [CrossRef] [Google Scholar]
  16. C. Tutiven, A. Encalada-Davila, Y. Vidal, C. Benalcazar-Parra, Detecting bearing failures in wind energy parks: a main bearing early damage detection method using SCADA data and a convolutional autoencoder, Energy Sci. Eng. 11, 1395–1411 (2023) [CrossRef] [Google Scholar]
  17. A. Murgia, R. Verbeke, E. Tsiporkova, L. Terzi, D. Astolfi, Discussion on the suitability of SCADA-based condition monitoring for wind turbine fault diagnosis through temperature data analysis , Energies 16, 620 (2023) [CrossRef] [Google Scholar]
  18. A. Encalada-Davila, B. Puruncajas, C. Tutivén, Y. Vidal, Wind turbine main bearing fault prognosis based solely on scada data, Sensors 21, 2228 (2021) [CrossRef] [PubMed] [Google Scholar]
  19. Y. Vidal, F. Pozo, C. Tutivén, Wind turbine multi-fault detection and classification based on SCADA data, Energies 11, 3018 (2018) [CrossRef] [Google Scholar]
  20. E. Gonzalez, B. Stephen, D. Infield, J.J. Melero, Using high-frequency SCADA data for wind turbine performance monitoring: a sensitivity study, Renew. Energy 131, 841–853 (2019) [CrossRef] [Google Scholar]
  21. A. Verma, D. Zappala', S. Sheng, S.J. Watson, Wind turbine gearbox fault prognosis using high-frequency SCADA data, J. Phys.: Conf. Ser. 2265, 032067 (2022) [CrossRef] [Google Scholar]
  22. F. Natili, A.P. Daga, F. Castellani, L. Garibaldi, Multi-scale wind turbine bearings supervision techniques using industrial SCADA and vibration data, Appl. Sci. 11, 6785 (2021) [CrossRef] [Google Scholar]
  23. A. Turnbull, J. Carroll, A. McDonald, Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure, Wind Energy 24, 197–211 (2021) [CrossRef] [Google Scholar]
  24. X. Jin, Y. Chen, L. Wang, H. Han, P. Chen, Failure prediction, monitoring and diagnosis methods for slewing bearings of large-scale wind turbine: a review, Measurement 172, 108855 (2021) [CrossRef] [Google Scholar]
  25. A. Meyer, SCADA-based fault detection in wind turbines: data-driven techniques and applications, in: Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy. Elsevier, 2023, pp. 1–13. https://doi.org/10.1016/B978-0-323-99666-2.00001-0 [Google Scholar]
  26. M. Jankauskas, A. Serackis, M. Šapurov, R. Pomarnacki, A. Baskys, V.K. Hyunh,T. Vaimann, J. Zakis, Exploring the limits of early predictive maintenance in wind turbines applying an anomaly detection technique, Sensors 23, 5695 (2023) [CrossRef] [PubMed] [Google Scholar]
  27. P.F. Smith, S. Ganesh, P. Liu, A comparison of random forest regression and multiple linear regression for prediction in neuroscience, J. Neurosci. Methods 220, 85–91 (2013) [CrossRef] [Google Scholar]
  28. H.J. Shin, D.H. Eom, S.S. Kim, One-class support vector machines—an application in machine fault detection and classification, Comput. Ind. Eng. 48, 395–408 (2008) [Google Scholar]
  29. J. Saari, D. Strömbergsson, J. Lundberg, A. Thomson, Detection and identification of windmill bearing faults using a one-class support vector machine (SVM), Measurement 137, 287–301 (2019) [CrossRef] [Google Scholar]
  30. B. Schölkopf et al., Estimating the support of a high-dimensional distribution, Neural Computat. 13, 1443–1471 (2001) [CrossRef] [PubMed] [Google Scholar]
  31. G. Dorcas Wambui, G.A. Waititu, A. Wanjoya, The power of the pruned exact linear time (PELT) test in multiple changepoint detection, Am. J. Theor. Appl. Stat. 4, 581 (2015) [CrossRef] [Google Scholar]
  32. R. Killick, P. Fearnhead, I.A. Eckley, Optimal detection of changepoints with a linear computational cost, J. Am. Stat. Assoc. 107, 1590–1598 (2012) [CrossRef] [Google Scholar]

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