Contact Us Search Paper

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

Pedro H. Feijó de Sousa1, Navar de Medeiros M. e Nascimento1, Jefferson S. Almeida1, Pedro P. Rebouças Filho1, Victor Hugo C. de Albuquerque2,*

Corresponding Author:

Victor Hugo C. de Albuquerque

Affiliation(s):

1 Laboratório de Processamento de Imagens, Sinais e Computação Aplicada, Instituto Federal do Ceará, Fortaleza, CE, Brasil
Email: [email protected]; [email protected]; [email protected]; [email protected]
2 Universidade de Fortaleza, Fortaleza, CE, Brasil
*Corresponding Author: Victor Hugo C. de Albuquerque, Email: [email protected]

Abstract:

The eagerness and necessity to develop so-called smart applications has taken the Internet of Things (IoT) to a whole new level. Industry has been implementing services that use IoT to increase productivity as well as management systems over the past couple of years. Such services are now encroaching on wind energy, which nowadays is the most acceptable source among renewable energies for electricity generation. This work proposes an intelligent system to identify incipient faults in the electric generators of wind turbines to improve maintenance routines. Four feature extraction methods were applied to vibration signals, and different classifiers were used to predict the running status of the wind turbine. We correctly identified 94.44% of normal conditions, reducing the false positive and negative rates to 0.4% and 1.84%, respectively; a better result than other approaches already reported in the literature.

Keywords:

Intelligent IoT, Wind turbines, Feature Extraction, Vibration, Artificial Intelligence

Downloads: 694 Views: 5219
Cite This Paper:

Sousa, P. H. F.; Nascimento, N. M. M.; Almeida, J. S.; Rebouças Filho, P. P. and Albuquerque, V. H. C. (2019). Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment. Journal of Artificial Intelligence and Systems, 1, 1–19. https://doi.org/10.33969/AIS.2019.11001.

References:

[1] Jerome Antoni. The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20(2): 282–307, 2006. ISSN 08883270.
[2] DA Asfani, MH Purnomo, and DR Sawitri. Naıve bayes classifier for temporary short circuit fault detection in stator winding. In Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on, pages 288–294. IEEE, 2013.
[3] Raziyeh Azizi, Behrooz Attaran, Ali Hajnayeb, Afshin Ghanbarzadeh, and Maziar Changizian. Improving accuracy of cavitation severity detection in centrifugal pumps using a hybrid feature selection technique. Measurement, 108: 9–17, 2017.
[4] Debasis Bandyopadhyay and Jaydip Sen. Internet of things: Applications and challenges in technology and standardization. Wireless Personal Communications, 58 (1): 49–69, 2011.
[5] Raymond S Beebe. Predictive maintenace of pumps using condition monitoring. Number April. 2004.
[6] James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. J. Mach. Learn. Res., 13:281–305, February 2012. ISSN 1532-4435.
[7] E Oran Brigham and E Oran Brigham. The fast Fourier transform and its applications, volume 448. prentice Hall Englewood Cliffs, NJ, 1988.
[8] China Telecom Americas. 5 things you need to know about IoT in China. Technical report, 2017.
[9] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20 (3): 273–297, 1995.
[10] Thomas M Cover, Peter E Hart, et al. Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1): 21–27, 1967.
[11] Phong B Dao, Wieslaw J Staszewski, Tomasz Barszcz, and Tadeusz Uhl. Condition monitoring and fault detection in wind turbines based on cointegration analysis of scada data. Renewable Energy, 116: 107–122, 2018.
[12] Atila Girao De Oliveira, Ricardo Silva The Pontes, and Claudio Marques de Sa Medeiros. Neural network used to stator winding interturn short-circuit fault detection in an induction motor driven by frequency converter. In 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, pages 459–464. IEEE, 2013.
[13] Pedro Henrique Feijo de Sousa, Navar Medeiros M e Nascimento, Pedro Pedrosa Reboucas Filho, and Claudio Marques Sa de Medeiros. Detection and classification of faults in induction generator applied into wind turbines through a machine learning approach. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE, 2018.
[14] R. Dwyer. Detection of non-Gaussian signals by frequency domain Kurtosis estimation. ICASSP ’83. IEEE International Conference on Acoustics, Speech, and Signal Processing, 8: 607–610, 1983.
[15] Okan K Ersoy. A comparative review of real and complex fourier-related transforms. Proceedings of the IEEE, 82(3): 429–447, 1994.
[16] M Faheem and V C Gungor. Energy efficient and QoS-aware Routing Protocol for Wireless Sensor Network-based Smart Grid Applications in the Context of Industry 4.0. Applied Soft Computing Journal, 2017.
[17] Fiorenzo Filippetti, Alberto Bellini, and Gerard-Andre Capolino. Condition monitoring and diagnosis of rotor faults in induction machines: State of art and future perspectives. In Electrical Machines Design Control and Diagnosis (WEMDCD), 2013 IEEE Workshop on, pages 196–209. IEEE, 2013.
[18] Xiang Gong and Wei Qiao. Bearing fault diagnosis for direct-drive wind turbines via current-demodulated signals. IEEE Transactions on Industrial Electronics, 60(8): 3419–3428, 2013.
[19] Rafael C Gonzalez, Richard E Woods, Steven L Eddins, et al. Digital image processing using MATLAB., volume 624. Pearson-Prentice-Hall Upper Saddle River, New Jersey, 2004.
[20] GWEC. Wind Energy Outlook: 2000 gigawatts by 2030, 2017. URL http://www.gwec.net/wind-energy-outlook-2000-gigawatts-2030/.
[21] GWEC. Global Wind Report 2016. Technical report, Global Wind Energy Council, 2017. URL http://files.gwec.net/files/GWR2016.pdf.
[22] Berthold Hahn, Michael Durstewitz, and Kurt Rohrig. Reliability of Wind Turbines. Wind Energy, pages 1–4, 2007.
[23] Simon Haykin. Neural networks: principles and practice. Bookman, 2001.
[24] David He, Ruoyu Li, and Junda Zhu. Plastic bearing fault diagnosis based on a two-step data mining approach. IEEE Transactions on Industrial Electronics, 60(8): 3429–3440, 2013.
[25] Fabio Immovilli, Alberto Bellini, Riccardo Rubini, and Carla Tassoni. Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. IEEE Transactions on Industry Applications, 46(4): 1350–1359, 2010.
[26] Warren Katzenstein and Jay Apt. The cost of wind power variability. Energy Policy, 51: 233–243, 2012.
[27] Iman Khajenasiri, Abouzar Estebsari, Marian Verhelst, and Georges Gielen. A Review on Internet of Things Solutions for Intelligent Energy Control in Buildings for Smart City Applications. Energy Procedia, 111(September 2016): 770–779, 2017.
[28] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
[29] Juha Kiviluoma, Hannele Holttinen, David Weir, Scharff Richard, S¨oder Lennart, Menemenli Nickie, A. Cutululis Nicolaos, Danti Lopez Irene, Lannoye Eamonn, Estanqueiro Ana, Gomez-Lazaro Emilio, Zhang Qin, Bai Jianhua, Wan Yih-Huei, and Michael Milligan. A simple atmospheric boundary layer model applied to large eddy simulations of wind turbine wakes. Wind Energy, 17(April 2013): 657–669, 2015.
[30] GB Kliman, WJ Premerlani, RA Koegl, and D Hoeweler. A new approach to on-line turn fault detection in ac motors. In Industry Applications Conference, 1996. Thirty-First IAS Annual Meeting, IAS’96., Conference Record of the 1996 IEEE, volume 1, pages 687–693. IEEE, 1996.
[31] Vlad Krotov. The Internet of Things and new business opportunities. Business Horizons, 2017.
[32] Hyunsoo Lee. Framework and development of fault detection classification using iot device and cloud environment. Journal of Manufacturing Systems, 43: 257–270, 2017.
[33] In Lee and Kyoochun Lee. The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4): 431–440, 2015.
[34] Gustavo de Novaes Pires Leite, Alex Maurıcio Araujo, and Pedro Andre Carvalho Rosas. Prognostic techniques applied to maintenance of wind turbines: a concise and specific review. Renewable and Sustainable Energy Reviews, 81: 1917–1925, 2018.
[35] WY Liu, WH Zhang, JG Han, and GF Wang. A new wind turbine fault diagnosis method based on the local mean decomposition. Renewable Energy, 48: 411–415, 2012.
[36] Aldısio G Medeiros, Solon A Peixoto, Antonio Carlos S Barros, Victor Hugo C de Albuquerque, and Pedro P Rebou¸cas Filho. Uma nova abordagem para a segmentacao de pulmoes utilizando o metodo de contorno ativo nao parametrico optimum path snakes em imagens de tomografia computadorizada. In 17o Workshop de Informatica Medica (WIM 2017), volume 17. SBC, 2017.
[37] J M Mendel. Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE, 79 (3): 278–305, 1991.
[38] Nasser M Nasrabadi. Pattern recognition and machine learning. Journal of electronic imaging, 16(4): 049901, 2007.
[39] Edson Cavalcanti Neto, Samuel Luz Gomes, Pedro Pedrosa Reboucas Filho, and Victor Hugo C de Albuquerque. Brazilian vehicle identification using a new embedded plate recognition system. Measurement, 70: 36–46, 2015.
[40] Joao P Papa, Alexandre X Falcao, and Celso TN Suzuki. Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology, 19(2): 120–131, 2009.
[41] Cedric Peeters, Patrick Guillaume, and Jan Helsen. Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy. Renewable Energy, 116: 74–87, 2018.
[42] J Penman, HG Sedding, BA Lloyd, and WT Fink. Detection and location of interturn short circuits in the stator windings of operating motors. IEEE transactions on Energy conversion, 9(4): 652–658, 1994.
[43] Henk Polinder, Jan Abraham Ferreira, Bogi Bech Jensen, Asger B Abrahamsen, Kais Atallah, and Richard a. McMahon. Trends in Wind Turbine Generator Systems. IEEE Journal of Emerging and Selected Topics in Power Electronics, 1(3): 174–185, 2013. ISSN 2168-6777.
[44] Miguel Delgado Prieto, Giansalvo Cirrincione, Antonio Garcia Espinosa, Juan Antonio Ortega, and Humberto Henao. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Transactions on Industrial Electronics, 60(8): 3398–3407, 2013.
[45] Aqsa Saeed Qureshi, Asifullah Khan, Aneela Zameer, and Anila Usman. Wind power prediction using deep neural network based meta regression and transfer learning. Applied Soft Computing Journal, 58, 2017.
[46] MA RA Fisher. On the mathematical foundations of theoretical statistics. Phil. Trans. R. Soc. Lond. A, 222(594-604): 309–368, 1922.
[47]Mona Khatami Rad, Mohammadehsan Torabizadeh, and Amin Noshadi. Artificial neural network-based fault diagnostics of an electric motor using vibration monitoring. In Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on, pages 1512–1516. IEEE, 2011.
[48] Ehsan Rahimi, Abdorreza Rabiee, Jamshid Aghaei, Kashem M. Muttaqi, and Ali Esmaeel Nezhad. On the management of wind power intermittency. Renewable and Sustainable Energy Reviews, 28(x): 643–653, 2013.
[49] Geraldo L Bezerra Ramalho, Daniel S Ferreira, Pedro P Reboucas Filho, and Fatima N Sombra de Medeiros. Rotation-invariant feature extraction using a structural co-occurrence matrix. Measurement, 94: 406–415, 2016.
[50] Geraldo Luis Bezerra Ramalho, Pedro Pedrosa Reboucas Filho, Celso Rogerio Schmidlin Junior, and Samuel Vieira Dias. Deteccao de falhas atraves de caracterısticas do sinal de vibracao e rede SOFM. XI Simposio Brasileiro de Automacao Inteligente, 2013, Fortaleza-CE.Simposio Brasileiro de Automacao Inteligente 2013 (SBAI 2013), 2013. ISSN 1098-6596.
[51] Geraldo Luis Bezerra Ramalho, Adriano Holanda Pereira, Pedro Pedrosa Reboucas Filho, and Claudio Marques de Sa Medeiros. Detecao De Falhas Em Motores Eletricos Atraves Da Classificacao De Padroes De Vibracao Utilizando Uma Rede Neural Elm. Holos, 4(0): 185, 2014.
[52] Pedro P Reboucas Filho, Elizangela de S Reboucas, Leandro B Marinho, Roger M Sarmento, Joao Manuel RS Tavares, and Victor Hugo C de Albuquerque. Analysis of human tissue densities: A new approach to extract features from medical images. Pattern Recognition Letters, 94: 211–218, 2017.
[53] Pedro Pedrosa Reboucas Filho, Navar MM Nascimento, Igor R Sousa, Claudio MS Medeiros, and Victor Hugo C de Albuquerque. A reliable approach for detection of incipient faults of short-circuits in induction generators using machine learning. Computers & Electrical Engineering, 71: 440–451, 2018.
[54] Murillo B Rodrigues, Raul Victor M Da Nobrega, Shara Shami A Alves, Pedro Pedrosa Reboucas Filho, Joao Batista F Duarte, Arun K Sangaiah, and Victor Hugo C De Albuquerque. Health of things algorithms for malignancy level classification of lung nodules. IEEE Access, 6: 18592–18601, 2018.
[55] J Royo and FJ Arcega. Machine current signature analysis as a way for fault detection in squirrel cage wind generators. In Diagnostics for Electric Machines, Power Electronics and Drives, 2007. SDEMPED 2007. IEEE International Symposium on, pages 383–387. IEEE, 2007.
[56] Stuart J Russell and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, 2016.
[57] Ted Saarikko, Ulrika H. Westergren, and Tomas Blomquist. The Internet of Things: Are you ready for what’s coming? Business Horizons, 60(5): 667–676, 2017.
[58] Gervasio F. Santos, Eduardo A. Haddad, and Geoffrey J D Hewings. Energy policy and regional inequalities in the Brazilian economy. Energy Economics, 36: 241–255, 2013.
[59] Freescale Semiconductor. Three Axis Low-g Micromachined Accelerometer, 2008.
[60] Suliman Shanbr, Faris Elasha, Mohamed Elforjani, and Joao Teixeira. Detection of natural crack in wind turbine gearbox. Renewable energy, 118: 172–179, 2018.
[61] Benjamin K. Sovacool. The intermittency of wind, solar, and renewable electricity generators: Technical barrier or rhetorical excuse? Utilities Policy, 17(3-4): 288–296, 2009.
[62] Marcelo Martins Stopa, Braz J Cardoso Filho, and Carlos B Martinez. Incipient detection of cavitation phenomenon in centrifugal pumps. IEEE Transactions on Industry Applications, 50(1): 120–126, 2014.
[63] William T Thomson, Ronald J Gilmore, et al. Motor current signature analysis to detect faults in induction motor drives-fundamentals, data interpretation, and industrial case histories. In Proceedings of the 32nd Turbomachinery Symposium. Texas A&M University. Turbomachinery Laboratories, 2003.
[64] Ignas Valodka and Gitana Valodkien˙e. The impact of renewable energy on the economy of lithuania. Procedia-Social and Behavioral Sciences, 213: 123–128, 2015.
[65] Vladimir Naumovich Vapnik. An overview of statistical learning theory. IEEE transactions on neural networks, 10(5): 988–999, 1999.
[66] Bruce D. Weinberg, George R. Milne, Yana G. Andonova, and Fatima M. Hajjat. Internet of Things: Convenience vs. privacy and secrecy. Business Horizons, 58(6): 615–624, 2015.
[67] Li Da Xu, Wu He, and Shancang Li. Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4):2233–2243, 2014.
[68] Venkata Yaramasu, Bin Wu, Paresh C. Sen, Samir Kouro, and Mehdi Narimani. High-Power Wind Energy Conversion Systems: State-of-the-Art and Emerging Technologies. Proceedings of the IEEE, 103(5), 2015.
[69] Shen Yuan and Shaobing Peng. Trends in the economic return on energy use and energy use efficiency in China’s crop production. Renewable and Sustainable Energy Reviews, 70(May): 836–844, 2017.
[70] Dahai Zhang, Liyang Qian, Baijin Mao, Can Huang, Bin Huang, and Yulin Si. A data-driven design for fault detection of wind turbines using random forests and xgboost. IEEE Access, 6: 21020–21031, 2018.
[71] Z. Zhang, A. Chen, A. Matveev, R. Nilssen, and A. Nysveen. High-power generators for offshore wind turbines. Energy Procedia, 35(1876): 52–61, 2013.