Abstract
Energy production through Wind turbine installations is increasing fast. In fact, wind turbines become bigger in size and power, what incurs that a simple unit defect causes huge energy losses. They are running in severe conditions of speed and load due to the variation of the wind speed. In addition, Wind turbines are subject to the environmental conditions such as wind shear, turbulence, gusts, rain, snow, sand and sea for offshore wind turbines. For this, their diagnoses and their follow-up is a priority to avoid the stops of production. In, developing techniques for prognostic and remaining useful life estimation is a very urgent necessity in wind turbine maintenance. Maintenance 4.0 is smart maintenance which refers to the last industrial revolution “Industry 4.0”: It proposes strategies to meet these expectations by implementing advanced monitoring techniques through highly developed instruments and real-time signal processing techniques and by building models based on algorithm that will ensure a self- improvement and optimize the failure prediction. In this paper, a process of the predictive maintenance 4.0 is proposed and applied to a wind turbine in order to optimize operating costs and improve the energy efficiency of this system. In fact, dynamic, thermal and material information which are extracted from sensors are combined and characterized in the real time for a global process monitoring.
Similar content being viewed by others
References
Ansari M, Nobari MRH, Amani E (2019) Determination of pitch angles and wind speeds ranges to improve wind turbine performance when using blade tip plates. Renew Energy 140:957–969
Dempsey PJ, Sheng S (2013) Investigation of data fusion applied to health monitoring of wind turbine drivetrain components. Wind Energy 16:479–489
Derbel S, Feki N, Barbot JP, Nicolau F, Abbes MS, Haddar M (2019) Electro-mechanical system control based on observers, in advances in acoustics and vibration II. Appl Condition Monit 13:101–110
Feki N, Clerc G, Velex P (2012) An integrated electro-mechanical model of motor-gear units—applications to tooth fault detection by electric measurements. Mech Syst Signal Process 29:377–390
Feki N, Clerc G, Velex P (2013) Gear and motor fault modeling and detection based on motor current analysis. Electr Power Syst Res 95:28–37
Fourati A, Bourdon A, Feki N, Rémond D, Chaari F, Haddar M (2017) Angular-based modeling of induction motors for monitoring. J Sound Vib 395:371–392
Griffin DA, Zuteck MD (2001) Scaling of composite wind turbine blades for rotors of 80 to 120 m diameter. J Sol Energy Eng 123(4):310–318
Hammami A, Fernandez A, Viadero F, Chaari F, Haddar M (2015a) Modal analysis of back-to-back planetary gear: experiments and correlation against lumped-parameter model. J Theor Appl Mech 53(1):125–138
Hammami A, Fernandez A, Viadero F, Chaari F, Haddar M (2015b) Dynamic behaviour of back to back planetary gear in run up and run down transient regimes. J Mech 31(4):481–491
Hammami A, Hmida A, Chaari F, Khabou MT, Haddar M (2019) Effect of cracked tooth on the dynamic response of simple gearbox with flexible coupling for acyclism operation. J Theor Appl Mech 53(3):591–603
Haarman M, Mulders M, Vassiliadis C (2017) Predictive maintenance 4.0, Mainnovation
Hayat I, Chatterjee T, Liu H, Peet YT, Chamorro LP (2019) Exploring wind farms with alternating two- and three-bladed wind turbines. Renew Energy 138:764–774
Hmida A, Hammami A, Khabou MT, Chaari F, Haddar M (2019) Effect of elastic coupling on the modal characteristics of spur gearbox system. Appl Acoust 144:71–84
Hua X, Zhang C, Wei J, Hu X, Wei H (2019) Wind turbine bionic blade design and performance analysis. J Vis Commun Image Represent 60:258–265
Jensen FM, Falzon BG, Ankersen J, Stang H (2006) Structural testing and numerical simulation of a 34 m composite wind turbine blade. Compos Struct 76:52–61
Lee J, Wu F, Zhao W, Ghaffari M, Siegel D, Liao L (2014) Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mech Syst Signal Process 42:314–334
Leite GNP, Araújo AM, Rosas PAC (2018) Prognostic techniques applied to maintenance of wind turbines: a concise and specific review. Renew Sustain Energy Rev 81:1917–1925
Marquez FPG, Tobias AM, Perez JMP, Papaelias M (2012) Condition monitoring of wind turbines: techniques and methods. Renew Energy 46:169–178
Marquez FPG, Perez JMP, Zarugan AP, Papaelias M (2016) Identification of critical components of wind turbines using FTA over the time. Renew Energy 87:869–883
Mbarek A, Fernandez A, Hammami A, Iglesias M, Chaari F, Viadero F, Haddar M (2018) Comparison of experimental and operational modal analysis on a back to back planetary gear. Mech Mach Theory 124:226–247
Mbarek A, Hammami A, Fernandez A, Chaari F, Viadero F, Haddar M (2019) Effect of load and meshing stiffness variation on modal properties of planetary gear. Appl Acoust 147:32–43
Mishnaevsky JL (2019) Repair of wind turbine blades: Review of methods and related computational mechanics problems. Renew Energy 140:828–839
Musial W, Butterfield S, McNiff B (2007) Improving wind turbine gearbox reliability. In: European wind energy conference Milan, Italy
Pan Y, Hong R, Chen J, Singh J, Jia X (2019) Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion. Mech Mach Theory 137:509–526
Sawalhi N, Randall RB, Forrester D (2014) Separation and enhancement of gear and bearing signals for the diagnosis of wind turbine transmission systems. Wind Energy 17:729–743
Schröder K, Apitz J, Ecke W, Lembke E, Lenschow G (2005) Fibre Bragg grating sensor system monitors operational load in a wind turbine rotor blade. Proc SPIE 5855:270–273
Srikanth P, Sekhar AS (2016) Wind turbine drive train dynamic characterization using vibration and torque signals. Mech Mach Theory 98:2–20
Tounsi M, Beyaoui M, Abboudi K, Feki N, Walha L, Haddar M (2016) Influence of uncertainty in aerodynamic performance on the dynamic response of a two stage gear system. J Theor Appl Mech 54(2):601–612
van Leeuwen H, van Delft D, Heijdra J, Braam H, Jorgensen ER, Lekou D, Vionis P (2002) Comparing fatigue strength from full scale blade tests with coupon-based predictions. J Sol Energy Eng Trans ASME 124:404–411
Viadero F, Fernández A, Iglesias M, de-Juan A, Liaño E, Serna MA (2014) Non-stationary dynamic analysis of a wind turbine power drivetrain: Offshore considerations. Appl Acoust 77:204–211
Yang D, Li H, Hu Y, Zhao J, Xiao H, Lan Y (2016) Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion. Renew Energy 92:104–116
Ye Q, Pan H, Liu C (2015) Enhancement of ELM by clustering discrimination manifold regularization and multiobjective FOA FOR semisupervised classification. Comput Intell Neurosci, ID 731494:1–9
Zhang Z, Verma A, Kusiak A (2012) Fault analysis and condition monitoring of the wind turbine gearbox. IEEE Trans Energy Convers 27(2):526–535
Ziane K (2017) Analysis, assessment and risk reduction of a wind farm. Dissertation, University of Oran 2, Algeria
Acknowledgements
The authors gratefully acknowledge the Project No. “19PEJC10-06” funded by the Tunisian Ministry of Higher Education and Scientific Research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hammami, A., Djemal, F., Hmida, A., Chaari, F., Haddar, M. (2020). Maintenance 4.0 of Wind Turbine. In: Barkallah, M., Choley, JY., Louati, J., Ayadi, O., Chaari, F., Haddar, M. (eds) Mechatronics 4.0. MECHATRONICS 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-46729-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-46729-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46728-9
Online ISBN: 978-3-030-46729-6
eBook Packages: EngineeringEngineering (R0)