Convolutional neural networks for wind turbine gearbox health monitoring

Zare, Samira; Ayati, Moosa; Ha&#039;iri Yazdi, Mohammad Reza; Kabir Anaraki, Amin

doi:10.22059/ees.2022.251289

Convolutional neural networks for wind turbine gearbox health monitoring

Document Type : Research Paper

Authors

School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

10.22059/ees.2022.251289

Abstract

Between different sources of renewable energy, wind energy, as an economical source of electrical power, has undergone a pronounced thriving. However, wind turbines are exposed to catastrophic failures, which may bring about irrecoverable ramifications. Therefore, they necessarily need condition monitoring and fault detection systems. These systems aim to reduce the number of attempts operators are required to do through the use of smart software algorithms, which are able to understand and decide with no human involvement. The gearboxes are usually responsible for the WT breakdowns. In this paper, convolutional neural networks are employed to develop an intelligent data-based condition-monitoring algorithm to differentiate healthy and damaged conditions that are evaluated with the national renewable energy laboratory (NREL) GRC database on the WT gearbox. Since it is much easier for convolutional neural networks to extract clues from high dimensional data, time-domain signals are embodied as texture images. Results show that the proposed methodology by utilizing a 2-D convolutional neural network for binary classification is capable of classifying the NREL GRC database with 99.76% accuracy.

Keywords

References

[1] World Wind Energy Association. Wind Power Capacity reaches 600 GW, 53.9 GW added in 2018. World Wind Energy Association, https://wwindea.org/blog/2019/02/25/wind-power-capacity-worldwide-reaches-600-gw-539-gw-added-in-2018/ (2019, accessed 23 April 2019).

[2] Crabtree CJ, Zappalá D, Hogg SI. Wind energy: UK experiences and offshore operational challenges. Proc Inst Mech Eng Part A J Power Energy 2015; 229: 727–746.

[3] Igba J, Alemzadeh K, Durugbo C, et al. Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends. Renew Sustain Energy Rev 2015; 50: 144–159.

[4] Qiu Y, Feng Y, Sun J, et al. Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data. 2016; 10: 661–668.

[5] Wang W. An intelligent system for machinery condition monitoring. IEEE Trans Fuzzy Syst 2008; 16: 110–122.

[6] Arcos Jiménez A, Gómez Muñoz CQ, García Márquez FP. Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliab Eng Syst Saf 2019; 184: 2–12.

[7] Arcos Jiménez A, Gómez Muñoz C, Márquez FPG. Machine Learning for Wind Turbine Blades Maintenance Management. Energies 2017; 11: 13.

[8] Ju L, Song D, Shi B, et al. Fault predictive diagnosis of wind turbine based on LM arithmetic of artificial neural network theory. In: Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. 2011, pp. 575–579.

[9] Yang S, Li W, Wang C. The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network. In: Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, pp. 1327–1330.

[10] Huang Q, Jiang D, Hong L, et al. Application of wavelet neural networks on vibration fault diagnosis for wind turbine gearbox. In: Proceedings of Advances in Neural Networks-ISNN. 2008, pp. 313–320.

[11] Bangalore P, Letzgus S, Karlsson D, et al. An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox. Wind Energy 2017; 20: 1421–1438.

[12] Schlechtingen M, Ferreira Santos I, Santos llmar F. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection. Mech Syst Signal Process 2011; 25: 1849–1875.

[13] Kusiak A, Verma A. Analyzing bearing faults in wind turbines: A data-mining approach. Renew Energy 2012; 48: 110–116.

[14] Janssens O, Slavkovikj V, Vervisch B, et al. Convolutional Neural Network Based Fault Detection for Rotating Machinery. J Sound Vib 2016; 377: 331–345.

[15] Sun W, Yao B, Zeng N, et al. An Intelligent Gear Fault Diagnosis Methodology Neural Network. Materials (Basel); 10. Epub ahead of print 2017. DOI: 10.3390/ma10070790.

[16] Jing L, Zhao M, Li P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 2017; 111: 1–10.

[17] Zhang W, Li C, Peng G, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 2018; 100: 439–453.

[18] Shahriar MR, Ahsan T, Chong U. Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis. Eurasip J Image Video Process; 2013. Epub ahead of print 2013. DOI: 10.1186/1687-5281-2013-29.

[19] Hoang DT, Kang HJ. Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cogn Syst Res 2019; 53: 42–50.

[20] Zhang W, Peng G, Li C. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input. MATEC Web Conf; 95. Epub ahead of print 2017. DOI: 10.1051/matecconf/20179513001.

[21] Ruiz M, Mujica LE, Alférez S, et al. Wind turbine fault detection and classification by means of image texture analysis. Mech Syst Signal Process 2018; 107: 149–167.

[22] LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, pp. 255–258.

[23] Karpathy A. CS231n Convolutional Neural Network for Visual Recognition, https://web.stanford.edu/class/cs379c/class_messages_listing/content/Artificial_Neural_Network_Technology_Tutorials/KarparthyCONVOLUTIONAL-NEURAL-NETWORKS-16.pdf (accessed 20 February 2019).

[24] Zare S, Ayati M. Simultaneous fault diagnosis of wind turbine using multichannel convolutional neural networks. ISA Transactions, 2021; 108:230-239.

[25] Fu Y, Zhang Y, Gao Y, et al. Machining vibration states monitoring based on image representation using convolutional neural networks. Eng Appl Artif Intell 2017; 65: 240–251.

[26] Link H, Lacava W, Dam J Van, et al. Gearbox Reliability Collaborative Project Report : Findings from Phase 1 and Phase 2 Testing. 2011. Epub ahead of print 2011. DOI: 10.2172/1018489.

[27] Sheng S. Wind Turbine Gearbox Condition Monitoring Round Robin Study–Vibration Analysis. Epub ahead of print 2012. DOI: 10.2172/1048981.

[28] Errichello R, Geartech JM. Gearbox Reliability Collaborative Gearbox 1 Failure Analysis Report December 2010 – January 2011, NREL Report No. SR-5000-53062, https://www.nrel.gov/docs/fy12osti/53062.pdf (2012).