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A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

Citations

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Cited by:

  1. Yolanda Vidal & Francesc Pozo & Christian Tutivén, 2018. "Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data," Energies, MDPI, vol. 11(11), pages 1-18, November.
  2. Xihui Chen & Aimin Ji & Gang Cheng, 2019. "A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis," Energies, MDPI, vol. 12(23), pages 1-18, November.
  3. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
  4. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
  5. Fan, Yuantao & Nowaczyk, Sławomir & Rögnvaldsson, Thorsteinn, 2020. "Transfer learning for remaining useful life prediction based on consensus self-organizing models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
  6. Liu, Wei & Wang, Xian & Long, Qingcan & Zeng, Bing & Zhong, Shuai, 2025. "A sensitive and easy-to-deploy condition monitoring method for main drive chain of large wind turbines," Renewable Energy, Elsevier, vol. 254(C).
  7. Leite, Gustavo de Novaes Pires & Araújo, Alex Maurício & Rosas, Pedro André Carvalho, 2018. "Prognostic techniques applied to maintenance of wind turbines: a concise and specific review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1917-1925.
  8. Haileyesus B. Endeshaw & Stephen Ekwaro-Osire & Fisseha M. Alemayehu & João Paulo Dias, 2017. "Evaluation of Fatigue Crack Propagation of Gears Considering Uncertainties in Loading and Material Properties," Sustainability, MDPI, vol. 9(12), pages 1-15, November.
  9. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
  10. Zemali, Zakaria & Cherroun, Lakhmissi & Hadroug, Nadji & Hafaifa, Ahmed & Iratni, Abdelhamid & Alshammari, Obaid S. & Colak, Ilhami, 2023. "Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 205(C), pages 873-898.
  11. 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.
  12. Miao, Yonghao & Zhao, Ming & Liang, Kaixuan & Lin, Jing, 2020. "Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal," Renewable Energy, Elsevier, vol. 151(C), pages 192-203.
  13. 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).
  14. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
  15. Altinpulluk, Nur Banu & Altinpulluk, Deniz & Yildirim, Murat & Zhao, Shijia & Qiu, Feng & Greco, Aaron, 2025. "A survey on degradation modeling, prognosis, and prognostics-driven maintenance in wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
  16. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
  17. Bakir, I. & Yildirim, M. & Ursavas, E., 2021. "An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  18. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
  19. Xu, Xuefang & Li, Bo & Qiao, Zijian & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong, 2023. "Caputo-Fabrizio fractional order derivative stochastic resonance enhanced by ADOF and its application in fault diagnosis of wind turbine drivetrain," Renewable Energy, Elsevier, vol. 219(P1).
  20. He, Guolin & Ding, Kang & Wu, Xiaomeng & Yang, Xiaoqing, 2019. "Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear," Renewable Energy, Elsevier, vol. 139(C), pages 718-729.
  21. Abu Al Hassan & Phong Ba Dao, 2025. "Bridging Data and Diagnostics: A Systematic Review and Case Study on Integrating Trend Monitoring and Change Point Detection for Wind Turbines," Energies, MDPI, vol. 18(19), pages 1-50, September.
  22. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.
  23. Thorsten Neumann & Beate Dutschk & René Schenkendorf, 2019. "Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management," Journal of Risk and Reliability, , vol. 233(5), pages 761-774, October.
  24. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
  25. Jiang, Ruolin & Fang, Fang & Rodríguez-Andina, Juan José & Song, Ziqiu & Liu, Jizhen & Chen, Yuanye & Wang, Hua, 2026. "Artificial intelligence in wind turbine fault diagnosis: A systematic knowledge mapping and trend analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
  26. Zhang, Xiaohong & Liao, Haitao & Zeng, Jianchao & Shi, Guannan & Zhao, Bing, 2021. "Optimal Condition-based Opportunistic Maintenance and Spare Parts Provisioning for a Two-unit System using a State Space Partitioning Approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
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