Articles | Volume 8, issue 4
https://doi.org/10.5194/wes-8-557-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-557-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Anomaly-based fault detection in wind turbine main bearings
Lorena Campoverde-Vilela
Mechatronic Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
María del Cisne Feijóo
Mechatronic Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
Control, Data, and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d'Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besós (CDB), Eduard Maristany 16, 08019 Barcelona, Spain
Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain
José Sampietro
Facultad de Ingenierías, Universidad Ecotec, Km. 13.5 Samborondón, Samborondón, EC092302, Ecuador
Christian Tutivén
Mechatronic Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
Related subject area
Thematic area: Materials and operation | Topic: Structural monitoring and testing
Wear test programs for roller-type pitch bearings of wind turbines
Exploring limiting factors of wear in pitch bearings of wind turbines with real-scale tests
Matthias Stammler
Wind Energ. Sci., 8, 1821–1837, https://doi.org/10.5194/wes-8-1821-2023, https://doi.org/10.5194/wes-8-1821-2023, 2023
Short summary
Short summary
Wind turbines subject their components to highly variable loads over very long lifetimes. Tests of components like the pitch bearings that connect rotor blades and the rotor hub serve to validate their ability to withstand these loads. Due to the complexity of the operational loads, the definition of test programs is challenging. This work outlines a method that defines wear test programs for specific pitch bearings and gives a case study for an example turbine.
Karsten Behnke and Florian Schleich
Wind Energ. Sci., 8, 289–301, https://doi.org/10.5194/wes-8-289-2023, https://doi.org/10.5194/wes-8-289-2023, 2023
Short summary
Short summary
The objective of this work is to find limits within typical operating conditions of a wind turbine below which wear on the bearing raceway does not occur. It covers the test of blade bearings with an outer diameter of 2.6 m. The test parameters are based on a 3 MW reference turbine and are compared to values from the literature. It was shown that it can be possible to avoid wear, which again can be used to design a wind turbine controller.
Cited articles
Artigao, E., Martín-Martínez, S., Honrubia-Escribano, A., and Gómez-Lázaro, E.: Wind turbine: A comprehensive review towards effective condition monitoring development, Appl. Energy, 228, 1569–1583, https://doi.org/10.1016/j.apenergy.2018.07.037, 2018. a
Astolfi, D., Castellani, F., and Natili, F.: Wind turbine generator slip ring damage detection through temperature data analysis, Diagnostyka, 20, 3–9, https://doi.org/10.29354/diag/109968, 2019. a
Bahar, K. P., Yıldız, G. B., and Soylu, B.: Predictive Maintenance System Integrated with Periodic Maintenance: Machine Learning and Classical Approaches, EasyChair, 5806 pp., https://wvvw.easychair.org/publications/preprint_download/GsVf (last access: April 2023), 2021. a
Baloch, Z. A., Tan, Q., Kamran, H. W., Nawaz, M. A., Albashar, G., and Hameed, J.: A multi-perspective assessment approach of renewable energy production: policy perspective analysis, Environ. Dev. Sustain., 24, 2164–2192, https://doi.org/10.1007/s10668-021-01524-8, 2022. a
Borchersen, A. B. and Kinnaert, M.: Model‐based fault detection for generator cooling system in wind turbines using SCADA data, Wind Energy, 19, 593–606, https://doi.org/10.1002/we.1852, 2016. a
Borgi, T., Hidri, A., Neef, B., and Naceur, M. S.: Data analytics for predictive maintenance of industrial robots, in: International Conference on Advanced Systems and Electric Technologies (IC_ASET), 14–17 January 2017, Hammamet, Tunisia, 412–417, https://doi.org/10.1109/ASET.2017.7983729, 2017. a
Brownlee, J.: Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python, Machine Learning Mastery, https://machinelearningmastery.com/data-preparation-for-machine-learning/
(last access: April 2023), 2020. a
Carroll, J., McDonald, A., and McMillan, D.: Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines, Wind Energy, 19, 1107–1119, https://doi.org/10.1002/we.1887, 2016. a
Chacón, A. and Märquez, F.: SCADA data analytics for fault detection and diagnosis of wind turbines, in: IEEE 7th International Conference on Control, Instrumentation and Automation (ICCIA), 23–24 February 2021,
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran, 1–6, https://doi.org/10.1109/ICCIA52082.2021, 2021. a
Chen, B., Xie, L., Li, Y., and Gao, B.: Acoustical damage detection of wind turbine yaw system using Bayesian network, Renew. Energy, 160, 1364–1372, https://doi.org/10.1016/j.renene.2020.07.062, 2020. a
Dahiya, R.: Condition monitoring of wind turbine for rotor fault detection under non stationary conditions, Ain Shams Eng. J., 9, 2441–2452, https://doi.org/10.1016/j.asej.2017.04.002, 2018. a
Dameshghi, A. and Refan, M. H.: Wind turbine gearbox condition monitoring and fault diagnosis based on multi-sensor information fusion of SCADA and DSER-PSO-WRVM method, Int. J. Simul. Model., 39, 48–72, https://doi.org/10.1080/02286203.2018.1476008, 2019. a, b
Dao, P. B.: Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data, Renew. Energy, 185, 641–654, https://doi.org/10.1016/j.renene.2021.12.051, 2022. a
Dao, P. B., Staszewski, W. J., Barszcz, T., and Uhl, T.: Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data, Renew. Energy, 116, 107–122, https://doi.org/10.1016/j.renene.2017.06.089, 2018. a
Dervilis, N., Choi, M. , Taylor, S. G., Barthorpe, R. J., Park, G., Farrar, C. R., and Worden, K.: On damage diagnosis for a wind turbine blade using pattern recognition, J. Sound Vibrat., 333, 1833–1850, https://doi.org/10.1016/j.jsv.2013.11.015, 2014. a
El Naqa, I. and Murphy, M. J.: What Is Machine Learning?, Springer, https://doi.org/10.1007/978-3-319-18305-3_1, 2015. a
Encalada-Dávila, Á., Puruncajas, B., Tutivén, C., and Vidal, Y.: Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data, Sensors, 21, 2228, https://doi.org/10.3390/s21062228, 2021. a, b, c, d
Encalada-Dávila, Á., Moyón, L., Tutivén, C., Puruncajas, B., and Vidal, Y.: Early Fault Detection in the Main Bearing of Wind Turbines Based on Gated Recurrent Unit (GRU) Neural Networks and SCADA Data, IEEE/ASME Trans. Mechatron., 27, 5583–5593, https://doi.org/10.1109/TMECH.2022.3185675, 2022. a, b
Feng, Y., Qiu, Y., Crabtree, C. J., Long, H., and Tavner, P. J.: Use of SCADA and CMS signals for failure detection and diagnosis of a wind turbine gearbox, in: European Wind Energy Association Conference, EWEC 2011, 14–17 March 2014, Brussels, Belgium, https://eprints.whiterose.ac.uk/83334/7/2011 Feng, Qiu, Crabtree, Long, Tavner_EWEA.pdf (last access: April 2023), 2014. a
Fu, J., Chu, J., Guo, P., and Chen, Z.: Condition monitoring of wind turbine gearbox bearing based on deep learning model, IEEE, 7, 57078–57087, https://doi.org/10.1109/ACCESS.2019.2912621, 2019. a
Fuentes, R., Dwyer-Joyce, R. S., Marshall, M. B., Wheals, J., and Cross, E. J.: Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling, Renew. Energy, 147, 776–797, https://doi.org/10.1016/j.renene.2019.08.019, 2020. a
García, S. and Luengo, F. H.: Data Preprocessing in Data Mining, in: Intelligent Systems Reference Library, 1st Edn., Springer, Cham, 320 pp., https://doi.org/10.1007/978-3-319-10247-4, 2015. a
Guo, J., Liu, C., Cao, J., and Jiang, D.: Damage identification of wind turbine blades with deep convolutional neural networks, Renew. Energy, 174, 122–133, https://doi.org/10.1016/j.renene.2021.04.040, 2021. a
Guo, P., Fu, J., and Yang, X.: Condition monitoring and fault diagnosis of wind turbines gearbox bearing temperature based on kolmogorov-smirnov test and convolutional neural network model, Energies, 11, 2248, https://doi.org/10.3390/en11092248, 2018. a
Hameed, Z., Hong, Y. S., Cho, Y. M., Ahn, S. H., and Song, C. K.: Condition monitoring and fault detection of wind turbines and related algorithms: A review, Renew. Sustain. Energ. Rev., 13, 1–39, https://doi.org/10.1016/j.rser.2007.05.008, 2009. a
Hansen, M.: Aerodynamics of wind turbines. Editorial, Routledge, Taylor and Francis Group, https://doi.org/10.4324/9781315769981, 2015. a
Harris, T. A. and Kotzalas, M. N.: Essential Concepts of Bearing Technology. Editorial, Routledge, Taylor and Francis Group, ISBN 9780429123351, https://doi.org/10.1201/9781420006599, 2006. a
Hart, E., Clarke, B., Nicholas, G., Kazemi Amiri, A., Stirling, J., Carroll, J., Dwyer-Joyce, R., McDonald, A., and Long, H.: A review of wind turbine main bearings: design, operation, modelling, damage mechanisms and fault detection, Wind Energ. Sci., 5, 105–124, https://doi.org/10.5194/wes-5-105-2020, 2020. a
Hu, J. and Chen, P.: Predictive maintenance of systems subject to hard failure based on proportional hazards model, Reliab. Eng. Syst. Safe., 196, 106707, https://doi.org/10.1016/j.ress.2019.106707, 2020. a
Hu, Y., Li, H., Shi, P., Chai, Z., Wang, K., Xie, X., and Chen, Z.: A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process, Renew. Energy, 127, 452–460, https://doi.org/10.1016/j.renene.2018.04.033, 2018. a
Hubbard, P. G., Xu, J., Zhang, S., Dejong, M., Luo, L., Soga, K., and Minto, C.: Dynamic structural health monitoring of a model wind turbine tower using distributed acoustic sensing (DAS), J. Civ. Struct. Health Monit., 11, 833–849, https://doi.org/10.1007/s13349-021-00483-y, 2021. a
Hunter, J. S.: The exponentially weighted moving average, J. Qual. Technol., 18, 203–210, https://doi.org/10.1080/00224065.1986.11979014, 1986. a
Jakhar, D. and Kaur, I.: Artificial intelligence, machine learning and deep learning: definitions and differences, Clin. Exp. Dermatol., 45, 131–132, https://doi.org/10.1111/ced.14029, 2020. a
Jiang, G., He, H., Yan, J., and Xie, P.: Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox, IEEE, 66, 3196–3207, https://doi.org/10.1109/TIE.2018.2844805, 2019. a
Jiang, Z., Hu, W., Dong, W., Gao, Z., and Ren, Z.: Structural reliability analysis of wind turbines: A review, Energies, 10, 2099, https://doi.org/10.3390/en10122099, 2017. a
Jolliffe, I. T. and Cadima, J.: Principal component analysis: a review and recent developments, Philos. T. Roy. Soc. A, 374, 20150202, https://doi.org/10.1098/rsta.2015.0202, 2016. a
Joshuva, A. and Sugumaran, V. : A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features, Measurement, 152, 107295, 984, https://doi.org/10.1016/j.measurement.2019.107295, 2020. a
Kang, J. N., Wei, Y. M., Liu, L. C., Han, R., Yu, B. Y., and Wang, J. W.: Energy systems for climate change mitigation: A systematic review, Appl. Energy, 263, 114602, https://doi.org/10.1016/j.apenergy.2020.114602, 2020. a
Karabacak, Y. and Özmen, N.: Common spatial pattern-based feature extraction and worm gear fault detection through vibration and acoustic measurements, Measurement, 187, 110366, https://doi.org/10.1016/j.measurement.2021.110366, 2022. a
Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S., and Fleming, P.: Use of SCADA data for failure detection in wind turbines, in: ASME 2011 5th International Conference on Energy Sustainability, 7–10 August 2011, Washington, DC, USA, https://doi.org/10.1115/ES2011-54243, 2011. a, b
Kotsiantis, S. B., Zaharakis, I. D., and Pintelas, P. E.: Machine learning: a review of classification and combining techniques, Artif. Intell. Rev., 26, 159–190, https://doi.org/10.1007/s10462-007-9052-3, 2006. a
Kurita, T.: Principal component analysis (PCA), in: Computer Vision: A Reference Guide, Springer, 1–4, https://doi.org/10.1007/978-3-030-03243-2_649-1, 2019. a, b, c
Leite, G. D. N. P., Araújo, A. M., and Rosas, P. A. C.: Prognostic techniques applied to maintenance of wind turbines: a concise and specific review, Renew. Sustain. Energ. Rev., 81, 1917–1925, https://doi.org/10.1016/j.rser.2017.06.002, 2018. a
Li, Y., Jiang, W., Zhang, G., and Shu, L.: Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data, Renew. Energy, 171, 103–115, https://doi.org/10.1016/j.renene.2021.01.143, 2021. a
May, A., McMillan, D., and Thöns, S.: Economic analysis of condition monitoring systems for offshore wind turbine sub-systems, IET Renew. Power Generat., 9, 900–907, https://doi.org/10.1049/iet-rpg.2015.0019, 2015. a
May, N.: The impact of wind power support schemes on technology choices, Energy Econ., 65, 343–354, https://doi.org/10.1016/j.eneco.2017.05.017, 2017. a
McKinnon, C., Turnbull, A., Koukoura, S., Carroll, J., and McDonald, A.: Effect of time history on normal behaviour modelling using SCADA data to predict wind turbine failures, Energies, 13, 4745, https://doi.org/10.3390/en13184745, 2020. a
Microsoft: PCA-Based Anomaly Detection Component, https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/pca-based-anomaly-detection, (last access: 7 March 2022), 2021. a
Motlagh, M. M., Bahar, A., and Bahar, O.: Damage detection in a 3D wind turbine tower by using extensive multilevel 2D wavelet decomposition and heat map, including soil-structure interaction, Structures, 31, 842–861, https://doi.org/10.1016/j.istruc.2021.01.018, 2021. a
Murgia, A., Verbeke, R., Tsiporkova, E., Terzi, L., and Astolfi, D.: Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis, Energies, 16, 620, https://doi.org/10.3390/en16020620, 2023. a
Natili, F., Daga, A. P., Castellani, F., and Garibaldi, L.: Multi-Scale Wind Turbine Bearings Supervision Techniques Using Industrial SCADA and Vibration Data, Appl. Sci., 11, 6785, https://doi.org/10.3390/app11156785, 2021. a
Nguyen, C., Huynh, T., and Kim, J.: Vibration-based damage detection in wind turbine towers using artificial neural networks, Struct. Monit. Maint., 5, 507–519, https://doi.org/10.12989/smm.2018.5.4.507, 2018. a
Nguyen, T., Huynh, T., and Kim, J.: Numerical evaluation for vibration-based damage detection in wind turbine tower structure, Wind Struct., 21, 657–675, https://doi.org/10.12989/was.2015.21.6.657, 2015. a
Oliveira, M., Simas, E., Albuquerque, M., Santos, Y., da Silva, I., and Farias, C.: Ultrasound-based identification of damage in wind turbine blades using novelty detection, Ultrasonics, 108, 106166, https://doi.org/10.1016/j.ultras.2020.106166, 2020. a
Ou, Y., Chatzi, E. N., Dertimanis, V. K., and Spiridonakos, M. D.: Vibration-based experimental damage detection of a small-scale wind turbine blade, Struct. Control Health Monit., 16, 79–96, https://doi.org/10.1177/1475921716663876, 2017. a
Peres, R., Rocha, A., Leitao, P., and Barata, J.: IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0, Comput. Indust., 101, 138–146, https://doi.org/10.1016/j.compind.2018.07.004, 2018. a
Ratner, B.: Statistical and machine-learning data mining: Techniques for better predictive modeling and analysis of big data, in: 3rd Edn., CRC Press, Taylor & Francis Group, USA, ISBN 9781498797603, 2017. a
Ren, H., Liu, W., Shan, M., and Wang, X.: A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning, Measurement, 148, 106906, https://doi.org/10.1016/j.measurement.2019.106906, 2019. a
Rezamand, M., Kordestani, M., Carriveau, R., Ting, D., and Saif, M.: A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF, IEEE Sensors J., 20, 2023–2033, https://doi.org/10.1109/JSEN.2019.2948997, 2020. a
Sabilla, S. I., Sarno, R., and Triyana, K.: Optimizing threshold using pearson correlation for selecting features of electronic nose signals, Int. J. Intell. Eng. Syst., 12, 81–90, 2019. a
Sezer, E., Romero, D., Guedea, F., Macchi, M., and Emmanouilidis, C.: An Industry 4.0-Enabled Low Cost Predictive Maintenance Approach for SMEs, in: IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 17–20 June 2018, Stuttgart, Germany, 1–8,
https://doi.org/10.1109/ICE.2018.8436307, 2018. a
Shin, T.: An Extensive Step by Step Guide to Exploratory Data Analysis, https://towardsdatascience.com/an-extensive-guide-to-exploratory-data-analysis-ddd99a03199e (last access: 10 January 2022), 2020. a
Son, J., Kang, D., Boo, D., and Ko, K.: An experimental study on the fault diagnosis of wind turbines through a condition monitoring system, J. Mech. Sci. Technol., 32, 5573–5582, https://doi.org/10.1007/s12206-018-1103-y, 2018. a
Teng, W., Ding, X., Zhang, X., Liu, Y., and Ma, Z.: Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform, Renew. Energy, 93, 591–598, bhttps://doi.org/10.1016/j.renene.2016.03.025, 2016. a
Tibaduiza, D., Mujica, L., and Rodellar, J.: Comparison of several methods for damage localization using indices and contributions based on PCA, J. Phys.: Conf. Ser., 305, 012013, https://doi.org/10.1088/1742-6596/305/1/012013, 2011. a
Turnbull, A., Carroll, J., and McDonald, A.: A comparative analysis on the variability of temperature thresholds through time for wind turbine generators using normal behaviour modelling, Energies, 15, 5298, https://doi.org/10.3390/en15145298, 2022. a
Tutivén, C., Vidal, Y., Insuasty, A., Campoverde-Vilela, L., and Achicanoy, W.: Early fault diagnosis strategy for WT main bearings based on SCADA data and one-class SVM, Energies, 15, 4381, https://doi.org/10.3390/en15124381, 2022. a
Vanawat, N.: How To Perform Exploratory Data Analysis – A Guide for Beginners, https://www.analyticsvidhya.com/blog/2021/08/how-to-perform-exploratory-data-analysis-a-guide-for-beginners/ (last access: 10 January 2022), 2021. a
Velmurugan, R. S. and Dhingra, T.: Maintenance strategy selection and its impact in maintenance function: A conceptual framework, Int. J. Oper. Prod. Manage., 35, 1622–1661, https://doi.org/10.1108/IJOPM-01-2014-0028, 2015. a, b
Vidal, Y., Aquino, G., Pozo, F., and Gutiérrez-Arias, J. E. .: Structural health monitoring for jacket-type offshore wind turbines: Experimental proof of concept, Sensors, 20, 1835, https://doi.org/10.3390/s20071835, 2020. a
Wang, H., Wang, H., Jiang, G., Li, J., and Wang, Y.: Early fault detection of wind turbines based on operational condition clustering and optimized deep belief network modeling, Energies, 12, 984, https://doi.org/10.3390/en12060984, 2019. a
Wang, Q., Dong, Z., Li, R., and Wang, L.: Renewable energy and economic growth: new insight from country risks, Energy, 238, 122018, https://doi.org/10.1016/j.energy.2021.122018, 2022. a
Wang, T., Han, Q., Chu, F., and Feng, Z.: Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review, Mech. Syst. Signal Process., 126, 662–685, https://doi.org/10.1016/j.ymssp.2019.02.051, 2019. a
Wang, X., Zhang, L., and Heath, W. P.: Wind turbine blades fault detection using system identification-based transmissibility analysis, Insight, 64, 164–169, https://doi.org/10.1784/insi.2022.64.3.164, 2022. a
Wenske, J.: Wind Turbine System Design. Volume 1: Nacelles, drivetrains and verification. Editorial, IET –Institution of Engineering and Technology, https://doi.org/10.1049/PBPO142F, 2022. a
Xiang, L., Wang, P., Yang, X., Hu, A., and Su, H.: Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism, Measurement, 175, 109094, https://doi.org/10.1016/j.measurement.2021.109094, 2021. a, b
Xiuli, L., Xueying, Z., and Liyong, W. : Fault diagnosis method of wind turbine gearbox based on deep belief network and vibration signal, IEEE, SICE, Measurement, 148, 1699–1704, https://doi.org/10.1016/j.measurement.2019.106906, 2018. a
Yao, J., Liu, C., Song, K., Feng, C., and Jiang, D.: Fault diagnosis of planetary gearbox based on acoustic signals, Appl. Acoust., 181, 108151, https://doi.org/10.1016/j.apacoust.2021.108151, 2021. a
Yii, K. J. and Geetha, C.: The nexus between technology innovation and CO2 emissions in Malaysia: Evidence from granger causality test, Energ. Proced., 105, 3118–3124, https://doi.org/10.1016/j.egypro.2017.03.654, 2017. a
Zeng, X. J., Yang, M., and Bo, Y. F.: Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation, Int. J. Elect. Power Energ. Syst., 123, 106233, https://doi.org/10.1016/j.ijepes.2020.106233, 2020. a
Zhang, B., Zhang, F., and Luo, H.: Virtual shaft‐based synchronous analysis for bearing damage detection and its application in wind turbines, Wind Energy, 25, 1252–1269, https://doi.org/10.1002/we.2727, 2022. a
Zhang, P. and Lu, D.: A survey of condition monitoring and fault diagnosis toward integrated O&M for wind turbines, Energies, 12, 2801, https://doi.org/10.3390/en12142801, 2019.
a
Zhang, Z., Verma,A., and Kusiak, A.: Fault analysis and condition monitoring of the wind turbine gearbox, IEEE, 27, 526–535, https://doi.org/10.1109/TEC.2012.2189887, 2012. a
Zhang, Z. Y. and Wang, K. S.: Wind turbine fault detection based on SCADA data analysis using ANN, Adv. Manufact., 2, 70–78, https://doi.org/10.1007/s40436-014-0061-6, 2014. a
Short summary
In order to provide early warnings of faults in the main bearing, a fault detection system is developed by applying an anomaly detector based on principal component analysis. Without the need to obtain the fault history or install additional equipment or sensors that would require a larger investment, this model is constructed using only healthy supervisory control and data acquisition (SCADA) data. The results obtained enable failure detection even months before the fatal breakdown takes place.
In order to provide early warnings of faults in the main bearing, a fault detection system is...
Altmetrics
Final-revised paper
Preprint