Comprehensive review of vibration-based analysis for wind turbine condition monitoring
Main Article Content
Abstract
Wind energy production relies heavily on the efficiency of wind turbine systems. The routine condition monitoring and maintenance of these systems are necessary to maintain healthy operation, reduce maintenance costs, minimize downtime, and extend the lifespan. Vibration based analysis is an essential technique for wind turbine condition monitoring that enables early detection of mechanical faults, abnormal behavior and degradation mechanisms, and lessens the risk of unexpected failures. This review paper explores an intensive review of various vibration based techniques of condition monitoring, their advancements, challenges, and trends. This review paper reveals that this technique of condition monitoring is effective and essential to ensure the efficiency of wind energy systems. The review paper identifies future research prospects and potential technological advancements to ensure wind energy systems' reliability, safety, and optimal performance.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
[2] E. Achdi, B. F. T. Kiono, S. H. Winoto, and M. Facta, “Improving cross-axis wind turbine performance: A Lab-scale investigation of rotor size and blades number,” Mechanical Engineering for Society and Industry, vol. 4, no. 1, pp. 82–91, Jul. 2024, doi: 10.31603/mesi.10837.
[3] A. D. Korawan and R. Febritasari, “Experimental investigations of number of blades effect on archimedes spiral wind turbine performance,” Mechanical Engineering for Society and Industry, vol. 4, no. 2, 2024, doi: 10.31603/mesi.12373.
[4] S. Roga, S. Bardhan, Y. Kumar, and S. K. Dubey, “Recent technology and challenges of wind energy generation: A review,” Sustainable Energy Technologies and Assessments, vol. 52, p. 102239, 2022, doi: 10.1016/j.seta.2022.102239.
[5] Global Wind Energy Council, “Global Wind Report 2023,” Global Wind Energy Council, 2023. .
[6] U. Singh, M. Rizwan, H. Malik, and F. P. García Márquez, “Wind Energy Scenario, Success and Initiatives towards Renewable Energy in India—A Review,” Energies, vol. 15, no. 6, p. 2291, 2022, doi: 10.3390/en15062291.
[7] A. S. Darwish and R. Al-Dabbagh, “Wind energy state of the art: present and future technology advancements,” Renewable Energy and Environmental Sustainability, vol. 5, p. 7, 2020, doi: 10.1051/rees/2020003.
[8] P. Sadorsky, “Wind energy for sustainable development: Driving factors and future outlook,” Journal of Cleaner Production, vol. 289, p. 125779, 2021, doi: 10.1016/j.jclepro.2020.125779.
[9] T. W. Verbruggen, “Wind Turbine Operation and Maintenance based on Condition Monitoring WT-{omega},” Petten (Netherlands), 2003.
[10] W. Qiao and D. Lu, “A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6536–6545, 2015, doi: 10.1109/tie.2015.2422112.
[11] P. Tchakoua, R. Wamkeue, M. Ouhrouche, F. Slaoui-Hasnaoui, T. Tameghe, and G. Ekemb, “Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges,” Energies, vol. 7, no. 4, pp. 2595–2630, 2014, doi: 10.3390/en7042595.
[12] X.-Y. He, T.-W. Zhao, H.-N. Li, and J. Zhang, “Multi-dimensional seismic response control of offshore platform structures with viscoelastic dampers (II-Experimental study),” Structural Monitoring and Maintenance, vol. 3, no. 2, pp. 175–194, 2016, doi: 10.12989/smm.2016.3.2.175.
[13] P. Bórawski, A. Bełdycka-Bórawska, K. J. Jankowski, B. Dubis, and J. W. Dunn, “Development of wind energy market in the European Union,” Renewable Energy, vol. 161, pp. 691–700, 2020, doi: 10.1016/j.renene.2020.07.081.
[14] W. Musial, “Offshore wind market report: 2023 edition,” Golden, CO, USA, 2023.
[15] C. Choe Wei Chang et al., “Recent advancements in condition monitoring systems for wind turbines: A review,” Energy Reports, vol. 9, pp. 22–27, 2023, doi: 10.1016/j.egyr.2023.08.061.
[16] W. Y. Liu, B. P. Tang, J. G. Han, X. N. Lu, N. N. Hu, and Z. Z. He, “The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review,” Renewable and Sustainable Energy Reviews, vol. 44, pp. 466–472, 2015, doi: 10.1016/j.rser.2014.12.005.
[17] L. Alhmoud and B. Wang, “A review of the state-of-the-art in wind-energy reliability analysis,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 1643–1651, 2018, doi: 10.1016/j.rser.2017.05.252.
[18] Interplay Learning, “Different Types of Maintenance,” Interplay Learning, 2020. .
[19] R. Orsagh, H. Lee, and M. Watson, “Advanced vibration monitoring for wind turbine health management,” Impact Technologies, no. April, 2006.
[20] P. Zhang and D. Lu, “A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines,” Energies, vol. 12, no. 14, p. 2801, 2019, doi: 10.3390/en12142801.
[21] R. Yan, S. Dunnett, and L. Jackson, “Impact of condition monitoring on the maintenance and economic viability of offshore wind turbines,” Reliability Engineering & System Safety, vol. 238, p. 109475, 2023, doi: 10.1016/j.ress.2023.109475.
[22] R. Valdez-Yepez, C. Tutivén, and Y. Vidal, “Structural health monitoring of jacket-type support structures in offshore wind turbines: A comprehensive dataset for bolt loosening detection through vibrational analysis,” Data in brief, vol. 53, p. 110222, Feb. 2024, doi: 10.1016/j.dib.2024.110222.
[23] Y. Kong, Q. Han, F. Chu, Y. Qin, and M. Dong, “Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox,” Renewable Energy, vol. 219, p. 119373, 2023, doi: 10.1016/j.renene.2023.119373.
[24] Z. Daneshi-Far, G. A. Capolino, and H. Henao, “Review of failures and condition monitoring in wind turbine generators,” The XIX International Conference on Electrical Machines - ICEM 2010. IEEE, pp. 1–6, 2010, doi: 10.1109/icelmach.2010.5608150.
[25] S. Sheng and P. Veers, “Wind turbine drivetrain condition monitoring - An overview,” in Mechanical Failures Prevention Group: Applied Systems Health Management Conference 2011, 2011, no. October.
[26] I. El-Thalji and E. Jantunen, “On the Development of Condition Based Maintenance Strategy for Offshore Wind Farm: Requirement Elicitation Process,” Energy Procedia, vol. 24, pp. 328–339, 2012, doi: 10.1016/j.egypro.2012.06.116.
[27] M. Lydia and G. Edwin Prem Kumar, “Condition monitoring in wind turbines,” in Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy, Elsevier, 2023, pp. 229–247.
[28] P. Tchakoua, R. Wamkeue, T. A. Tameghe, and G. Ekemb, “A review of concepts and methods for wind turbines condition monitoring,” 2013 World Congress on Computer and Information Technology (WCCIT). IEEE, pp. 1–9, 2013, doi: 10.1109/wccit.2013.6618706.
[29] M. C. M. Gowda, N. P. Mallikarjun, P. Gowda, and R. Chandrashekhar, “Improvement of the performance of Wind Turbine Generator using Condition Monitoring techniques,” 2013 7th International Conference on Intelligent Systems and Control (ISCO). IEEE, pp. 495–501, 2013, doi: 10.1109/isco.2013.6481205.
[30] W. Yang, P. J. Tavner, C. J. Crabtree, Y. Feng, and Y. Qiu, “Wind turbine condition monitoring: technical and commercial challenges,” Wind Energy, vol. 17, no. 5, pp. 673–693, 2012, doi: 10.1002/we.1508.
[31] A. A. Salem, A. Abu-Siada, and S. Islam, “Condition Monitoring Techniques of the Wind Turbines Gearbox and Rotor,” International Journal of Electrical Energy, pp. 53–56, 2014, doi: 10.12720/ijoee.2.1.53-56.
[32] P. Zhang and Z. Chen, “Non-invasive condition monitoring and diagnostics techniques for wind turbines,” 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia). IEEE, pp. 3249–3254, 2016, doi: 10.1109/ipemc.2016.7512815.
[33] P. Bangalore, S. Letzgus, D. Karlsson, and M. Patriksson, “An artificial neural network‐based condition monitoring method for wind turbines, with application to the monitoring of the gearbox,” Wind Energy, vol. 20, no. 8, pp. 1421–1438, 2017, doi: 10.1002/we.2102.
[34] J. Tautz‐Weinert and S. J. Watson, “Using SCADA data for wind turbine condition monitoring – a review,” IET Renewable Power Generation, vol. 11, no. 4, pp. 382–394, 2016, doi: 10.1049/iet-rpg.2016.0248.
[35] M. L. Hossain, A. Abu-Siada, and S. M. Muyeen, “Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review,” Energies, vol. 11, no. 5, p. 1309, 2018, doi: 10.3390/en11051309.
[36] E. Hoxha, Y. Vidal, and F. Pozo, “Supervised classification with scada data for condition monitoring of wind turbines,” in 9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019, 2019, pp. 263–274.
[37] J. Maldonado-Correa, S. Martín-Martínez, E. Artigao, and E. Gómez-Lázaro, “Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review,” Energies, vol. 13, no. 12, p. 3132, 2020, doi: 10.3390/en13123132.
[38] M. Benbouzid, T. Berghout, N. Sarma, S. Djurović, Y. Wu, and X. Ma, “Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review,” Energies, vol. 14, no. 18, p. 5967, 2021, doi: 10.3390/en14185967.
[39] H. Badihi, Y. Zhang, B. Jiang, P. Pillay, and S. Rakheja, “A Comprehensive Review on Signal-Based and Model-Based Condition Monitoring of Wind Turbines: Fault Diagnosis and Lifetime Prognosis,” Proceedings of the IEEE, vol. 110, no. 6, pp. 754–806, 2022, doi: 10.1109/jproc.2022.3171691.
[40] R. K. Pandit, D. Astolfi, and I. Durazo Cardenas, “A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines,” Energies, vol. 16, no. 4, p. 1654, 2023, doi: 10.3390/en16041654.
[41] F. Castellani, F. Natili, D. Astolfi, and Y. Vidal, “Wind turbine gearbox condition monitoring through the sequential analysis of industrial SCADA and vibration data,” Energy Reports, vol. 12, pp. 750–761, 2024, doi: 10.1016/j.egyr.2024.06.041.
[42] S. K. Sahu, V. Kumar, S. Chandra Dutta, R. Sarkar, S. Bhattacharya, and P. Debnath, “Structural safety of offshore wind turbines: Present state of knowledge and future challenges,” Ocean Engineering, vol. 309, p. 118383, 2024, doi: 10.1016/j.oceaneng.2024.118383.
[43] A. Nagy and I. Jahn, “Advanced Data Acquisition System for Wind Energy Applications,” Periodica Polytechnica Transportation Engineering, vol. 47, no. 2, pp. 124–130, 2018, doi: 10.3311/pptr.11515.
[44] A. Günel, T. Bley, A. Meshram, M. Klusch, and A. Schütze, “D8.1 - Statistical and Semantic Multisensor Data Evaluation for Fluid Condition Monitoring in Wind Turbines,” Proceedings SENSOR 2013. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, pp. 604–609, 2013, doi: 10.5162/sensor2013/d8.1.
[45] S. Nese, O. Kilic, and T. C. Akinci, “Condition monitoring with signal processing in wind turbines,” Journal of Vibroengineering, vol. 13, p. 439, Sep. 2011.
[46] Z. Zhang and A. Kusiak, “Monitoring Wind Turbine Vibration Based on SCADA Data,” Journal of Solar Energy Engineering, vol. 134, no. 2, 2012, doi: 10.1115/1.4005753.
[47] R. Dupuis, “Application of Oil Debris Monitoring For Wind Turbine Gearbox Prognostics and Health Management,” Annual Conference of the PHM Society, vol. 2, no. 1, 2010, doi: 10.36001/phmconf.2010.v2i1.1867.
[48] R. L. Hu, K. Leahy, I. C. Konstantakopoulos, D. M. Auslander, C. J. Spanos, and A. M. Agogino, “Using Domain Knowledge Features for Wind Turbine Diagnostics,” 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, pp. 300–307, 2016, doi: 10.1109/icmla.2016.0056.
[49] M. Kordestani, M. Rezamand, R. Carriveau, D. S. K. Ting, and M. Saif, “Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS),” Lecture Notes in Computer Science. Springer International Publishing, pp. 545–556, 2019, doi: 10.1007/978-3-030-20521-8_45.
[50] F. Harrou, K. R. Kini, M. Madakyaru, and Y. Sun, “Uncovering sensor faults in wind turbines: An improved multivariate statistical approach for condition monitoring using SCADA data,” Sustainable Energy, Grids and Networks, vol. 35, p. 101126, 2023, doi: 10.1016/j.segan.2023.101126.
[51] F. C. Mehlan and A. R. Nejad, “Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring,” Renewable Energy, vol. 212, pp. 70–81, 2023, doi: 10.1016/j.renene.2023.04.102.
[52] A. S. Verma, J. Yan, W. Hu, Z. Jiang, W. Shi, and J. J. E. Teuwen, “A review of impact loads on composite wind turbine blades: Impact threats and classification,” Renewable and Sustainable Energy Reviews, vol. 178, p. 113261, 2023, doi: 10.1016/j.rser.2023.113261.
[53] Y. Zhang et al., “Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning,” Expert Systems with Applications, vol. 232, p. 120854, 2023, doi: 10.1016/j.eswa.2023.120854.
[54] F. Zhang, M. Chen, Y. Zhu, K. Zhang, and Q. Li, “A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines,” Energies, vol. 16, no. 3, p. 1125, 2023, doi: 10.3390/en16031125.
[55] M. R. Machado and M. Dutkiewicz, “Wind turbine vibration management: An integrated analysis of existing solutions, products, and Open-source developments,” Energy Reports, vol. 11, pp. 3756–3791, 2024, doi: 10.1016/j.egyr.2024.03.014.
[56] C. Velandia-Cardenas, Y. Vidal, and F. Pozo, “Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data,” Energies, vol. 14, no. 6, p. 1728, 2021, doi: 10.3390/en14061728.
[57] B. Lu, Y. Li, X. Wu, and Z. Yang, “A review of recent advances in wind turbine condition monitoring and fault diagnosis,” 2009 IEEE Power Electronics and Machines in Wind Applications. IEEE, pp. 1–7, 2009, doi: 10.1109/pemwa.2009.5208325.
[58] X. Wu, H. Wang, G. Jiang, P. Xie, and X. Li, “Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data,” Energies, vol. 12, no. 6, p. 982, 2019, doi: 10.3390/en12060982.
[59] Z.-Q. Xiang et al., “Vibration-based health monitoring of the offshore wind turbine tower using machine learning with Bayesian optimisation,” Ocean Engineering, vol. 292, p. 116513, 2024, doi: 10.1016/j.oceaneng.2023.116513.
[60] K. Kong, K. Dyer, C. Payne, I. Hamerton, and P. M. Weaver, “Progress and Trends in Damage Detection Methods, Maintenance, and Data-driven Monitoring of Wind Turbine Blades – A Review,” Renewable Energy Focus, vol. 44, pp. 390–412, 2023, doi: 10.1016/j.ref.2022.08.005.
[61] C. J. Crabtree, “Condition Monitoring of Wind Turbines: Challenges and opportunities,” in 5th PhD Seminar on Wind Energy in Europe, 2008, pp. 1–4.
[62] Z. Zemali, L. Cherroun, N. Hadroug, M. Nadour, and A. Hafaifa, “Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators,” Diagnostyka, vol. 24, no. 1, pp. 1–13, 2023, doi: 10.29354/diag/161307.
[63] J. Leng, G. Li, and L. Duan, “The impact of extreme wind conditions and yaw misalignment on the aeroelastic responses of a parked offshore wind turbine,” Ocean Engineering, vol. 313, p. 119403, 2024, doi: 10.1016/j.oceaneng.2024.119403.
[64] A. Czyżewski, “Remote Health Monitoring of Wind Turbines Employing Vibroacoustic Transducers and Autoencoders,” Frontiers in Energy Research, vol. 10, 2022, doi: 10.3389/fenrg.2022.858958.
[65] B. S. Dhillon, “Corrective Maintenance,” in Engineering Maintenance, CRC Press, 2002.
[66] Y. Wang, W. Lu, K. Dai, M. Yuan, and S.-E. Chen, “Dynamic Study of a Rooftop Vertical Axis Wind Turbine Tower Based on an Automated Vibration Data Processing Algorithm,” Energies, vol. 11, no. 11, p. 3135, 2018, doi: 10.3390/en11113135.
[67] H. Li, J. Deng, S. Yuan, P. Feng, and D. D. K. Arachchige, “Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts,” Frontiers in Energy Research, vol. 9, 2021, doi: 10.3389/fenrg.2021.799039.
[68] Z. Gao and X. Liu, “An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems,” Processes, vol. 9, no. 2, p. 300, 2021, doi: 10.3390/pr9020300.
[69] K. Lin, J. Pan, Y. Xi, Z. Wang, and J. Jiang, “Vibration anomaly detection of wind turbine based on temporal convolutional network and support vector data description,” Engineering Structures, vol. 306, p. 117848, 2024, doi: 10.1016/j.engstruct.2024.117848.
[70] X. Gong and W. Qiao, “Imbalance Fault Detection of Direct-Drive Wind Turbines Using Generator Current Signals,” IEEE Transactions on Energy Conversion, vol. 27, no. 2, pp. 468–476, 2012, doi: 10.1109/tec.2012.2189008.
[71] T. Wang, Q. Han, F. Chu, and Z. Feng, “Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review,” Mechanical Systems and Signal Processing, vol. 126, pp. 662–685, 2019, doi: 10.1016/j.ymssp.2019.02.051.
[72] H. Han and D. Yang, “Correlation analysis based relevant variable selection for wind turbine condition monitoring and fault diagnosis,” Sustainable Energy Technologies and Assessments, vol. 60, p. 103439, 2023, doi: 10.1016/j.seta.2023.103439.
[73] P. Santos, L. F. Villa, A. Reñones, A. Bustillo, and J. Maudes, “An SVM-based solution for fault detection in wind turbines,” Sensors (Basel, Switzerland), vol. 15, no. 3, pp. 5627–5648, Mar. 2015, doi: 10.3390/s150305627.
[74] G. Jiang, H. He, J. Yan, and P. Xie, “Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox,” IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3196–3207, 2019, doi: 10.1109/tie.2018.2844805.
[75] L. Xiang, P. Wang, X. Yang, A. Hu, and H. Su, “Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism,” Measurement, vol. 175, p. 109094, 2021, doi: 10.1016/j.measurement.2021.109094.
[76] Q. Yao, H. Bing, G. Zhu, L. Xiang, and A. Hu, “A novel stochastic process diffusion model for wind turbines condition monitoring and fault identification with multi-parameter information fusion,” Mechanical Systems and Signal Processing, vol. 214, p. 111397, 2024, doi: 10.1016/j.ymssp.2024.111397.
[77] R. Uma Maheswari and R. Umamaheswari, “Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train – A contemporary survey,” Mechanical Systems and Signal Processing, vol. 85, pp. 296–311, 2017, doi: 10.1016/j.ymssp.2016.07.046.
[78] F. Cheng, L. Qu, and W. Qiao, “Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis,” IEEE Transactions on Sustainable Energy, vol. 9, no. 1, pp. 157–167, 2018, doi: 10.1109/tste.2017.2719626.
[79] R. Pandit, D. Astolfi, J. Hong, D. Infield, and M. Santos, “SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends,” Wind Engineering, vol. 47, no. 2, pp. 422–441, 2022, doi: 10.1177/0309524x221124031.
[80] D. Schlinkert and K. G. van den Boogaart, “The development of the market for rare earth elements: Insights from economic theory,” Resources Policy, vol. 46, pp. 272–280, 2015, doi: 10.1016/j.resourpol.2015.10.010.
[81] J. Lei et al., “Techno-Economic Assessment of a Full-Chain Hydrogen Production by Offshore Wind Power,” Energies, vol. 17, no. 11, p. 2447, 2024, doi: 10.3390/en17112447.
[82] S. Gasperin and J. Emden, “A second wind: Maximising the economic opportunity for UK wind manufacturing,” Institute for Public Policy Reserch, 2024. .
[83] R. B. Randall, Vibration–based Condition Monitoring. Wiley, 2021.
[84] P. Jayaswal and B. Agrawal, “New trends in Wind Turbine Condition Monitoring System,” International Journal of Emerging trends in Engineering and Development, vol. 3, no. 1, pp. 133–148, 2011.
[85] X. Yuan and L. Cai, “Variable amplitude Fourier series with its application in gearbox diagnosis—Part I: Principle and simulation,” Mechanical Systems and Signal Processing, vol. 19, no. 5, pp. 1055–1066, 2005, doi: 10.1016/j.ymssp.2004.10.011.
[86] J. Zhan, J. Huang, L. Niu, X. Peng, D. Deng, and S. Cheng, “Study of the key technologies of electric power big data and its application prospects in smart grid,” 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, pp. 1–4, 2014, doi: 10.1109/appeec.2014.7066162.
[87] B. K. Bose, “Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems—Some Example Applications,” Proceedings of the IEEE, vol. 105, no. 11, pp. 2262–2273, 2017, doi: 10.1109/jproc.2017.2756596.
[88] D. Siegel, W. Zhao, E. Lapira, M. AbuAli, and J. Lee, “A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains,” Wind Energy, vol. 17, no. 5, pp. 695–714, 2013, doi: 10.1002/we.1585.
[89] Y. Ou, E. N. Chatzi, V. K. Dertimanis, and M. D. Spiridonakos, “Vibration-based experimental damage detection of a small-scale wind turbine blade,” Structural Health Monitoring, vol. 16, no. 1, pp. 79–96, 2016, doi: 10.1177/1475921716663876.
[90] Z. Zhang, A. Verma, and A. Kusiak, “Fault Analysis and Condition Monitoring of the Wind Turbine Gearbox,” IEEE Transactions on Energy Conversion, vol. 27, no. 2, pp. 526–535, 2012, doi: 10.1109/tec.2012.2189887.
[91] L. Cao, J. Zhang, Z. Qian, Z. Meng, and J. Li, “Condition monitoring of wind turbine based on a novel spatio-temporal feature aggregation network integrated with adaptive threshold interval,” Advanced Engineering Informatics, vol. 62, p. 102676, 2024, doi: 10.1016/j.aei.2024.102676.
[92] W. Yang, P. J. Tavner, S. Sheng, and R. Court, “Information entropy: An effective approach for wind turbine condition monitoring,” in European Wind Energy Conference and Exhibition 2012, EWEC 2012, Jan. 2012, pp. 772–778.
[93] R. Zimroz, J. Urbanek, T. Barszcz, W. Bartelmus, F. Millioz, and N. Martin, “Measurement of Instantaneous Shaft Speed by Advanced Vibration Signal Processing - Application to Wind Turbine Gearbox,” Metrology and Measurement Systems, vol. 18, no. 4, 2011, doi: 10.2478/v10178-011-0066-4.
[94] M. V KiranKumar, M. Lokesha, S. Kumar, and A. Kumar, “Review on Condition Monitoring of Bearings using vibration analysis techniques.,” IOP Conference Series: Materials Science and Engineering, vol. 376, p. 12110, 2018, doi: 10.1088/1757-899x/376/1/012110.
[95] M. N. Kotzalas and G. L. Doll, “Tribological advancements for reliable wind turbine performance,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 368, no. 1929, pp. 4829–4850, 2010, doi: 10.1098/rsta.2010.0194.
[96] K. Bassett, “Vibration Based Structural Health Monitoring for Utility Scale Wind Turbines,” Theory of Computing Systems, vol. 20, Jan. 2010.
[97] A. M. Abdelrhman, M. S. Leong, S. A. M. Saeed, and S. M. A. A.-O. Al Obiadi, “A Review of Vibration Monitoring as a Diagnostic Tool for Turbine Blade Faults,” Applied Mechanics and Materials, vol. 229–231, pp. 1459–1463, 2012, doi: 10.4028/www.scientific.net/amm.229-231.1459.
[98] J. Kumar Sasmal, “Condition Monitoring of Wind Turbine Gearbox by Vibration Analysis,” International Journal of Scientific Engineering and Research, vol. 3, no. 3, pp. 52–56, 2015, doi: 10.70729/ijser1510.
[99] A. Romero, Y. Lage, S. Soua, B. Wang, and T.-H. Gan, “Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System,” Shock and Vibration, vol. 2016, pp. 1–18, 2016, doi: 10.1155/2016/6423587.
[100] A. Joshuva. and V. Sugumaran., “A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study,” ISA Transactions, vol. 67, pp. 160–172, 2017, doi: 10.1016/j.isatra.2017.02.002.
[101] G. Oliveira, F. Magalhães, Á. Cunha, and E. Caetano, “Vibration-based damage detection in a wind turbine using 1 year of data,” Structural Control and Health Monitoring, vol. 25, no. 11, p. e2238, 2018, doi: 10.1002/stc.2238.
[102] S. Koukoura, J. Carroll, and A. McDonald, “On the use of AI based vibration condition monitoring of wind turbine gearboxes,” Journal of Physics: Conference Series, vol. 1222, no. 1, p. 12045, 2019, doi: 10.1088/1742-6596/1222/1/012045.
[103] D. Saputra and K. Marhadi, “Automatic Fault Diagnosis in Wind Turbine Condition Monitoring,” PHM Society European Conference, vol. 5, no. 1, p. 8, 2020, doi: 10.36001/phme.2020.v5i1.1251.
[104] S. Roy, B. Kundu, and D. Chatterjee, “Cloud Based Real-Time Vibration and Temperature Monitoring System for Wind Turbine,” Studies in Infrastructure and Control. Springer Singapore, pp. 89–99, 2021, doi: 10.1007/978-981-16-1011-0_9.
[105] M. Tiboni, C. Remino, R. Bussola, and C. Amici, “A Review on Vibration-Based Condition Monitoring of Rotating Machinery,” Applied Sciences, vol. 12, no. 3, p. 972, 2022, doi: 10.3390/app12030972.
[106] Y. Arbella-Feliciano, C. A. Trinchet-Varela, L. L. Lorente-Leyva, and D. H. Peluffo-Ordóñez, “Condition Monitoring of Wind Turbines: A Case Study of the Gibara II Wind Farm,” Journal Européen des Systèmes Automatisés, vol. 56, no. 2, pp. 329–335, 2023, doi: 10.18280/jesa.560218.
[107] D. Broda, “Description of varying operational conditions in wind turbines,” 2011.
[108] S. Bogoevska, M. Spiridonakos, E. Chatzi, E. Dumova-Jovanoska, and R. Höffer, “A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach,” Sensors (Basel, Switzerland), vol. 17, no. 4, p. 720, Mar. 2017, doi: 10.3390/s17040720.
[109] Y. Vidal, “Artificial Intelligence for Wind Turbine Condition Monitoring,” Energies, vol. 16, no. 4, p. 1632, 2023, doi: 10.3390/en16041632.
[110] T. Zhang, R. Dwight, and K. El-Akruti, “Condition Based Maintenance and Operation of Wind Turbines,” Lecture Notes in Mechanical Engineering. Springer International Publishing, pp. 1013–1025, 2014, doi: 10.1007/978-3-319-09507-3_87.
[111] W. Qiao, P. Zhang, and M.-Y. Chow, “Condition Monitoring, Diagnosis, Prognosis, and Health Management for Wind Energy Conversion Systems,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6533–6535, 2015, doi: 10.1109/tie.2015.2464785.
[112] V. M. Karbhari and L. S.-W. Lee, “Vibration-based damage detection techniques for structural health monitoring of civil infrastructure systems,” Structural Health Monitoring of Civil Infrastructure Systems. Elsevier, pp. 177–212, 2009, doi: 10.1533/9781845696825.1.177.
[113] Y. Amirat, V. Choqueuse, and M. Benbouzid, “Condition monitoring of wind turbines based on amplitude demodulation,” 2010 IEEE Energy Conversion Congress and Exposition. IEEE, pp. 2417–2421, 2010, doi: 10.1109/ecce.2010.5617914.
[114] G. T. Zheng and W. J. Wang, “A New Cepstral Analysis Procedure of Recovering Excitations for Transient Components of Vibration Signals and Applications to Rotating Machinery Condition Monitoring,” Journal of Vibration and Acoustics, vol. 123, no. 2, pp. 222–229, 2001, doi: 10.1115/1.1356696.
[115] C. Peeters, P. Guillaume, and J. Helsen, “Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy,” Renewable Energy, vol. 116, pp. 74–87, 2018, doi: 10.1016/j.renene.2017.01.056.
[116] D. Abboud, J. Antoni, S. Sieg-Zieba, and M. Eltabach, “Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment,” Mechanical Systems and Signal Processing, vol. 84, pp. 200–226, 2017, doi: 10.1016/j.ymssp.2016.06.033.
[117] N. Jamaludin, D. Mba, and R. H. Bannister, “Condition monitoring of slow-speed rolling element bearings using stress waves,” Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 215, no. 4, pp. 245–271, 2001, doi: 10.1177/095440890121500401.
[118] P. Waskito, S. Miwa, Y. Mitsukura, and H. Nakajo, “Parallelizing Hilbert-Huang Transform on a GPU,” 2010 First International Conference on Networking and Computing. IEEE, pp. 184–190, 2010, doi: 10.1109/ic-nc.2010.44.
[119] X. Wan, S. Zhang, J. Zhang, H. Chai, and H. Zhao, “Time-Frequency Analysis of Vibration Signal Distribution of Rotating Machinery Based on Machine Learning and EMD Decomposition,” Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, pp. 115–128, 2022, doi: 10.1007/978-3-030-94182-6_9.
[120] E. Filho, A. Santos, H. Sinezio, E. Filho, A. Fernandes, and J. Seixas, “Empirical Mode Decomposition: Theory and Applications in Underwater Acoustics,” Journal of Communication and Information Systems, vol. 37, no. 1, p. 145, 2022, doi: 10.14209/jcis.2022.16.
[121] A. R. Nejad et al., “Wind turbine drivetrains: state-of-the-art technologies and future development trends,” Wind Energy Science, vol. 7, no. 1, pp. 387–411, 2022, doi: 10.5194/wes-7-387-2022.
[122] A. Fernandez, “Demodulation or envelope analysis,” Power-MI, 2020. .
[123] E. P. Carden and P. Fanning, “Vibration Based Condition Monitoring: A Review,” Structural Health Monitoring, vol. 3, no. 4, pp. 355–377, 2004, doi: 10.1177/1475921704047500.
[124] M. Niyazi and A. N. Babadi, “Control and Monitoring of Wind Farms Based on IoT Application for Energy Conversion,” Handbook of Smart Energy Systems. Springer International Publishing, pp. 1–5, 2023, doi: 10.1007/978-3-030-72322-4_177-1.
[125] Z. Gao and P. Odgaard, “Real-time monitoring, fault prediction and health management for offshore wind turbine systems,” Renewable Energy, vol. 218, p. 119258, 2023, doi: 10.1016/j.renene.2023.119258.
[126] E. Kipchirchir, M. H. Do, J. G. Njiri, and D. Söffker, “Prognostics-based adaptive control strategy for lifetime control of wind turbines,” Wind Energy Science, vol. 8, no. 4, pp. 575–588, 2023, doi: 10.5194/wes-8-575-2023.
[127] M. D. Reder, E. Gonzalez, and J. J. Melero, “Wind Turbine Failures - Tackling current Problems in Failure Data Analysis,” Journal of Physics: Conference Series, vol. 753, p. 72027, 2016, doi: 10.1088/1742-6596/753/7/072027.
[128] X. Chen, M. A. Eder, A. Shihavuddin, and D. Zheng, “A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance,” Sustainability, vol. 13, no. 2, p. 561, 2021, doi: 10.3390/su13020561.
[129] Z. Tian, T. Jin, B. Wu, and F. Ding, “Condition based maintenance optimization for wind power generation systems under continuous monitoring,” Renewable Energy, vol. 36, no. 5, pp. 1502–1509, 2011, doi: 10.1016/j.renene.2010.10.028.
[130] D. Goyal, B. S. Pabla, S. S. Dhami, and K. Lachhwani, “Optimization of condition-based maintenance using soft computing,” Neural Computing and Applications, vol. 28, no. S1, pp. 829–844, 2016, doi: 10.1007/s00521-016-2377-6.
[131] P. Li, S. L. J. Hu, and H. J. Li, “Noise Issues of Modal Identification using Eigensystem Realization Algorithm,” Procedia Engineering, vol. 14, pp. 1681–1689, 2011, doi: 10.1016/j.proeng.2011.07.211.
[132] J. Yang, Y. Sun, H. Jing, and P. Li, “An improved NExT method for modal identification with tests validation,” Engineering Structures, vol. 274, p. 115192, 2023, doi: 10.1016/j.engstruct.2022.115192.
[133] M. Song, N. Partovi Mehr, B. Moaveni, E. Hines, H. Ebrahimian, and A. Bajric, “One year monitoring of an offshore wind turbine: Variability of modal parameters to ambient and operational conditions,” Engineering Structures, vol. 297, p. 117022, 2023, doi: 10.1016/j.engstruct.2023.117022.
[134] J.-H. Wan, R. Bai, X.-Y. Li, and S.-W. Liu, “Natural Frequency Analysis of Monopile Supported Offshore Wind Turbines Using Unified Beam-Column Element Model,” Journal of Marine Science and Engineering, vol. 11, no. 3, p. 628, 2023, doi: 10.3390/jmse11030628.
[135] J. Valasek and W. Chen, “Observer/Kalman Filter Identification for Online System Identification of Aircraft,” Journal of Guidance, Control, and Dynamics, vol. 26, no. 2, pp. 347–353, 2003, doi: 10.2514/2.5052.
[136] W. Yang, P. J. Tavner, C. J. Crabtree, and M. Wilkinson, “Cost-Effective Condition Monitoring for Wind Turbines,” IEEE Transactions on Industrial Electronics, vol. 57, no. 1, pp. 263–271, 2010, doi: 10.1109/tie.2009.2032202.
[137] M.-Q. Tran, Y.-C. Li, C.-Y. Lan, and M.-K. Liu, “Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region,” Energies, vol. 13, no. 24, p. 6559, 2020, doi: 10.3390/en13246559.
[138] S. Y. Oh et al., “Condition-based maintenance of wind turbine structures: A state-of-the-art review,” Renewable and Sustainable Energy Reviews, vol. 204, p. 114799, 2024, doi: 10.1016/j.rser.2024.114799.
[139] R. A. Swartz, J. P. Lynch, S. Zerbst, B. Sweetman, and R. Rolfes, “Structural monitoring of wind turbines using wireless sensor networks,” Smart Structures and Systems, vol. 6, no. 3, pp. 183–196, 2010, doi: 10.12989/sss.2010.6.3.183.
[140] R. Niyirora, W. Ji, E. Masengesho, J. Munyaneza, F. Niyonyungu, and R. Nyirandayisabye, “Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: A systematic review,” Results in Engineering, vol. 16, p. 100761, 2022, doi: 10.1016/j.rineng.2022.100761.
[141] S. Sahoo, K. Kushwah, and A. K. Sunaniya, “Health Monitoring of Wind Turbine Blades through Vibration Signal Using Advanced Signal Processing Techniques,” 2020 Advanced Communication Technologies and Signal Processing (ACTS). IEEE, pp. 1–6, 2020, doi: 10.1109/acts49415.2020.9350405.
[142] W. Hernandez, J. L. Maldonado‐Correa, and A. Méndez, “Frequency‐domain analysis of performance of a wind turbine,” Electronics Letters, vol. 52, no. 3, pp. 221–223, 2016, doi: 10.1049/el.2015.2711.
[143] M. Islam, H. A. Mohammadpour, P. Stone, and Y.-J. Shin, “Time-frequency based power quality analysis of variable speed wind turbine generators,” IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society. IEEE, pp. 6426–6431, 2013, doi: 10.1109/iecon.2013.6700194.
[144] W. Xin, Y. Liu, Y. He, and B. Su, “Amplitude envelope analysis for feature extraction of direct-driven wind turbine bearing failure,” Proceedings of the 10th World Congress on Intelligent Control and Automation. IEEE, pp. 3173–3176, 2012, doi: 10.1109/wcica.2012.6358418.
[145] H. Toubakh, M. Sayed-Mouchaweh, and E. Duviella, “Advanced Pattern Recognition Approach for Fault Diagnosis of Wind Turbines,” 2013 12th International Conference on Machine Learning and Applications. IEEE, pp. 368–373, 2013, doi: 10.1109/icmla.2013.150.
[146] K. F. Abdulraheem and G. Al-Kindi, “Wind Turbine Condition Monitoring using Multi-Sensor Data system,” International Journal of Renewable Energy Research, no. v8i1, 2018, doi: 10.20508/ijrer.v8i1.6506.g7276.
[147] M. J. Ali, A. Mondal, and P. Dutta, “Intelligent Monitoring and Control of Wind Turbine Prototype Using Internet of Things (IoT),” 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE, pp. 1–6, 2022, doi: 10.1109/iemtronics55184.2022.9795845.
[148] J. Cuesta, U. Leturiondo, Y. Vidal, and F. Pozo, “Challenges on prognostics and health management for wind turbine components,” Journal of Physics: Conference Series, vol. 2745, no. 1, p. 12003, 2024, doi: 10.1088/1742-6596/2745/1/012003.
[149] N. O. Farrar, M. H. Ali, and D. Dasgupta, “Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review,” Energies, vol. 16, no. 3, p. 1530, 2023, doi: 10.3390/en16031530.
[150] J. Kang, Z. Wang, and C. Guedes Soares, “Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine,” Energies, vol. 13, no. 14, p. 3518, 2020, doi: 10.3390/en13143518.
[151] S. Oh and T. Ishihara, “On the parameter sensitivity in structural parameter identification using Eigensystem Realization Algorithm for a MW-size wind turbine,” Journal of Physics: Conference Series, vol. 1037, p. 52026, 2018, doi: 10.1088/1742-6596/1037/5/052026.
[152] Y. Liu, J.-M. Zhang, Y.-T. Min, Y. Yu, C. Lin, and Z.-Z. Hu, “A digital twin-based framework for simulation and monitoring analysis of floating wind turbine structures,” Ocean Engineering, vol. 283, p. 115009, 2023, doi: 10.1016/j.oceaneng.2023.115009.
[153] M. Alswaitti, M. K. Ishak, and N. A. M. Isa, “Optimized gravitational-based data clustering algorithm,” Engineering Applications of Artificial Intelligence, vol. 73, pp. 126–148, 2018, doi: 10.1016/j.engappai.2018.05.004.
[154] J. Naik, A. Acharya, and J. Thaker, “Revolutionizing condition monitoring techniques with integration of artificial intelligence and machine learning,” Materials Today: Proceedings, 2023, doi: 10.1016/j.matpr.2023.08.262.
[155] B. A. Tama, M. Vania, S. Lee, and S. Lim, “Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals,” Artificial Intelligence Review, vol. 56, no. 5, pp. 4667–4709, 2022, doi: 10.1007/s10462-022-10293-3.
[156] N. P. Chavan, M. S. Kale, S. P. Deshmukh, and C. M. Thakar, “A Review on Development and Trend of Condition Monitoring and Fault Diagnosis,” ECS Transactions, vol. 107, no. 1, pp. 17863–17870, 2022, doi: 10.1149/10701.17863ecst.
[157] W. Yang, Z. Wang, and Y. Choy, “Prediction of sound radiation from an unbaffled long enclosure with the ground,” Mechanical Systems and Signal Processing, vol. 149, p. 107232, 2021, doi: 10.1016/j.ymssp.2020.107232.
[158] F. Xiao, C. Tian, I. Wait, Z. (Joey) Yang, B. Still, and G. S. Chen, “Condition monitoring and vibration analysis of wind turbine,” Advances in Mechanical Engineering, vol. 12, no. 3, p. 168781402091378, 2020, doi: 10.1177/1687814020913782.
[159] A. Al-Habaibeh, A. Boateng, and H. Lee, “Innovative Strategy for Addressing the Challenges of Monitoring Off-Shore Wind Turbines for Condition-Based Maintenance,” Springer Proceedings in Energy. Springer International Publishing, pp. 189–196, 2021, doi: 10.1007/978-3-030-63916-7_24.
[160] Z. Wu, Y. Li, and P. Wang, “A hierarchical modeling strategy for condition monitoring and fault diagnosis of wind turbine using SCADA data,” Measurement, vol. 227, p. 114325, 2024, doi: 10.1016/j.measurement.2024.114325.
[161] M. Schütt, “Wind Turbines and Property Values: A Meta-Regression Analysis,” Environmental and Resource Economics, vol. 87, no. 1, pp. 1–43, 2023, doi: 10.1007/s10640-023-00809-y.
[162] C. F. de Lima Munguba et al., “Ensemble learning framework for fleet-based anomaly detection using wind turbine drivetrain components vibration data.,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108363, 2024, doi: 10.1016/j.engappai.2024.108363.
[163] D. Ferguson and V. M. Catterson, “Big Data Techniques for Wind Turbine Condition Monitoring,” 2014.
[164] N. V Poorima, B. Srinivasan, and S. Karthikeyan, “Monitoring the Wind Turbine Condition Using Big Data Technique,” Asian Journal of Computer Science and Technology, vol. 8, no. S1, pp. 98–102, 2019, doi: 10.51983/ajcst-2019.8.s1.1941.
[165] C. Q. Gómez, M. A. Villegas, F. P. García, and D. J. Pedregal, “Big Data and Web Intelligence for Condition Monitoring,” Advances in Data Mining and Database Management. IGI Global, pp. 149–163, 2015, doi: 10.4018/978-1-4666-8505-5.ch008.
[166] N. Amuthan, M. B. M, P. Velrajkumar, N. Sivakumar, and T. Jarin, “IOT based adjustment mechanism for direct reference model adaptive IMC to support voltage sag in DFIG wind farm,” Measurement: Sensors, vol. 27, p. 100809, 2023, doi: 10.1016/j.measen.2023.100809.
[167] D. Chan and J. Mo, “Life Cycle Reliability and Maintenance Analyses of Wind Turbines,” Energy Procedia, vol. 110, pp. 328–333, 2017, doi: 10.1016/j.egypro.2017.03.148.
[168] E. Artigao, S. Martín-Martínez, A. Honrubia-Escribano, and E. Gómez-Lázaro, “Wind turbine reliability: A comprehensive review towards effective condition monitoring development,” Applied Energy, vol. 228, pp. 1569–1583, 2018, doi: 10.1016/j.apenergy.2018.07.037.
[169] D. Astolfi, F. De Caro, and A. Vaccaro, “Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques,” Sensors (Basel, Switzerland), vol. 23, no. 12, p. 5376, Jun. 2023, doi: 10.3390/s23125376.
[170] E. Bechhoefer, M. Wadham-Gagnon, and B. Boucher, “Initial Condition Monitoring Experience on a Wind Turbine,” Annual Conference of the PHM Society, vol. 4, no. 1, 2012, doi: 10.36001/phmconf.2012.v4i1.2119.
[171] G. Marsh and D. Robb, “Patently innovative,” Refocus, vol. 8, no. 2, pp. 30–35, 2007, doi: 10.1016/s1471-0846(07)70047-1.
[172] F. K. Moghadam and A. R. Nejad, “Online condition monitoring of floating wind turbines drivetrain by means of digital twin,” Mechanical Systems and Signal Processing, vol. 162, p. 108087, 2022, doi: 10.1016/j.ymssp.2021.108087.
[173] M. Fahim, V. Sharma, T.-V. Cao, B. Canberk, and T. Q. Duong, “Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines,” IEEE Access, vol. 10, pp. 14184–14194, 2022, doi: 10.1109/access.2022.3147602.
[174] P. Tchakoua, R. Wamkeue, F. Slaoui-Hasnaoui, T. A. Tameghe, and G. Ekemb, “New trends and future challenges for wind turbines condition monitoring,” 2013 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, pp. 238–245, 2013, doi: 10.1109/iccais.2013.6720561.
[175] P. S. Thakur, “Deficiency Diagnosis and Conditional Monitoring Way of a Wind Turbine,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5, no. 5, pp. 4296–4301, 2016, doi: 10.15662/IJAREEIE.2016.0505153.