Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
General Research Article

Fault Diagnosis of Wind Turbine Gearbox Based on Neighborhood QPSO and Improved D-S Evidence Theory

Author(s): Jiatang Cheng, Yan Xiong and Li Ai*

Volume 13, Issue 2, 2020

Page: [248 - 255] Pages: 8

DOI: 10.2174/2213275912666181218124805

Price: $65

Abstract

Background: Gearbox is the key equipment of wind turbine drive chain. Due to the harsh operating environment of wind turbine, gearbox failures occur frequently.

Methods: To improve the accuracy of fault identification for wind turbine gearbox, an intelligent fault diagnosis method based on Neighborhood Quantum Particle Swarm Optimization (NQSPO) and improved Dempster-Shafer (D-S) evidence theory is proposed. In NQPSO algorithm, the best solution information in the neighborhood is introduced to guide the individual search behavior and enhance the population diversity. Also, the consistency coefficient is used to determine the weight of evidence, and the original evidence is amended to enhance the ability of D-S theory to fuse conflict evidence.

Results: Experimental results show that the proposed method can overcome the influence of bad evidence on the diagnosis result and has high reliability.

Conclusion: The research can effectively improve the accuracy of fault diagnosis of wind turbine gearbox, and provide a feasible idea for the fault diagnosis of nonlinear complex system.

Keywords: Wind turbine gearbox, fault diagnosis, neighborhood quantum particle swarm optimization, neural network, improved D-S evidence theory, evidence fusion.

Graphical Abstract

[1]
J.P. Salameh, S. Cauet, E. Etien, A. Sakout, and L. Rambault, "Gearbox condition monitoring in wind turbines: A review", Mech. Syst. Signal Process., vol. 111, pp. 251-264, 2018.
[http://dx.doi.org/10.1016/j.ymssp.2018.03.052]
[2]
J. Wang, F.Z. Cheng, W. Qiao, and L.Y. Qu, "Multiscale filtering reconstruction for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions", IEEE Trans. Ind. Electron., vol. 65, no. 5, pp. 4268-4278, 2018.
[http://dx.doi.org/10.1109/TIE.2017.2767520]
[3]
D.G. Lu, W. Qiao, and X. Gong, "Current-based gear fault detection for wind turbine gearboxes", IEEE Transactions on Sustainable Energy, vol. 8, no. 4, pp. 1453-1462, 2017.
[http://dx.doi.org/10.1109/TSTE.2017.2690835]
[4]
Y. Zhang, W.X. Lu, and F.L. Chu, "Planet gear fault localization for wind turbine gearbox using acoustic emission signals", Renew. Energy, vol. 109, pp. 449-460, 2017.
[http://dx.doi.org/10.1016/j.renene.2017.03.035]
[5]
Z. Li, Y.B. Liu, W. Teng, and Y. Lin, "Fault diagnosis of wind turbine gearbox based on KFCM Optimized by particle swarm optimization", J. Vibr. Measurement. Diag., vol. 37, no. 3, pp. 484-488, 2017.
[6]
R.Q. Chen, J.C. Li, T. Shang, and J. Zhang, "Intelligent fault diagnosis of gearbox based on improved fireworks algorithm and probabilistic neural network", Nongye Gongcheng Xuebao (Beijing), vol. 34, no. 17, pp. 192-198, 2018.
[7]
F.Z. Cheng, Y.Y. Peng, L.Y. Qu, and W. Qiao, "Current-based fault detection and identification for wind turbine drivetrain gearboxes", IEEE Trans. Ind. Appl., vol. 53, no. 2, pp. 878-887, 2017.
[http://dx.doi.org/10.1109/TIA.2016.2628362]
[8]
J. Yu, and Y.J. He, "Planetary gearbox fault diagnosis based on data-driven valued characteristic multigranulation model with incomplete diagnostic information", J. Sound Vibrat., vol. 429, pp. 63-77, 2018.
[http://dx.doi.org/10.1016/j.jsv.2018.05.020]
[9]
F.Z. Cheng, J. Wang, L.Y. Qu, and W. Qiao, "Rotor-current-based fault diagnosis for DFIG wind turbine drivetrain gearboxes using frequency analysis and a deep classifier", IEEE Trans. Ind. Appl., vol. 54, no. 2, pp. 1062-1071, 2018.
[http://dx.doi.org/10.1109/TIA.2017.2773426]
[10]
Y.Q. Liu, Q. Xu, and D. David Infield, "Tian and Q. Long, “Fault identification of wind turbine drivetrain using BP neural network based on gravitational search algorithm", J. Vibr. Shock, vol. 34, no. 2, pp. 134-137, 2015.
[11]
C.H. Zhao, H.J. Dong, and X.Y. Zhong, "SVM parameter optimization in fault diagnosis for wind power gear box", Zhongguo Jixie Gongcheng, vol. 26, no. 16, pp. 2222-2225, 2015.
[12]
H.H. Liu, X.Y. Zhao, H.S. Zhao, P. Song, and C. Deng, "Fault detection of wind turbine gearbox based on deep autoencoder network", Transactions of China Electrotechnical Society, vol. 32, no. 17, pp. 156-163, 2017.
[13]
Y.H. Ao, F.T. Wang, B. Cao, S.Q. Zhang, and Z.H. Zhang, Fault diagnosis method and system for wind turbine gearbox. CN Patent 201710768239, August 2017.
[14]
H.G. Chen, P. Chen, L. Cai, W.H. Chen, J.W. Wu, S.K. Liang, J.Y. Shen, and Y.Y. Yi, A fault diagnosis method for wind turbine gearbox. CN Patent 201610083751, February 2016.5
[15]
C.Q. Lu, S.P. Wang, and X.J. Wang, "A multi-source information fusion fault diagnosis for aviation hydraulic pump based on the new evidence similarity distance", Aerosp. Sci. Technol., vol. 71, pp. 392-401, 2017.
[http://dx.doi.org/10.1016/j.ast.2017.09.040]
[16]
B.P. Cai, Y.H. Liu, Q. Fan, Y.W. Zhang, Z.K. Liu, S.L. Yu, and R.J. Ji, "Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network", Appl. Energy, vol. 114, pp. 1-9, 2014.
[http://dx.doi.org/10.1016/j.apenergy.2013.09.043]
[17]
A. Moosavian, M. Khazaee, G. Najafi, M. Kettner, and R. Mamat, "Spark plug fault recognition based on sensor fusion and classifier combination using Dempster-Shafer evidence theory", Appl. Acoust., vol. 93, pp. 120-129, 2015.
[http://dx.doi.org/10.1016/j.apacoust.2015.01.008]
[18]
J.T. Cheng, L. Wang, and Y. Xiong, "Cuckoo search algorithm with memory and the vibrant fault diagnosis for hydroelectric generating unit", Eng. Comput., vol. 35, pp. 1-16, 2018.
[19]
K. Mason, M. Duggan, E. Barrett, J. Duggan, and E. Howley, "Predicting host CPU utilization in the cloud using evolutionary neural networks", Future Gener. Comput. Syst., vol. 86, pp. 162-173, 2018.
[http://dx.doi.org/10.1016/j.future.2018.03.040]
[20]
M. Hasanipanah, M. Noorian-Bidgoli, D.J. Armaghani, and H. Khamesi, "Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling", Eng. Comput., vol. 32, no. 4, pp. 705-715, 2016.
[http://dx.doi.org/10.1007/s00366-016-0447-0]
[21]
S. Moosazadeh, E. Namazi, H. Aghababaei, A. Marto, H. Mohamad, and M. Hajihassani, "Prediction of building damage induced by tunnelling through an optimized artificial neural network", Eng. Comput., vol. 35, pp. 1-13, 2018.
[22]
E.N. Ghaleini, M. Koopialipoor, M. Momenzadeh, M.E. Sarafraz, E.T. Mohamad, and B. Gordan, "A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls", Eng. Comput., vol. 35, pp. 1-12, 2018.
[23]
J.T. Cheng, L. Wang, Q.Y. Jiang, Z.J. Cao, and Y. Xiong, "Cuckoo search algorithm with dynamic feedback information", Future Gener. Comput. Syst., vol. 89, pp. 317-334, 2018.
[http://dx.doi.org/10.1016/j.future.2018.06.056]
[24]
J. Sun, X.J. Wu, V. Palade, W. Fang, C-H. Lai, and W.B. Xu, "Convergence analysis and improvements of quantum-behaved particle swarm optimization", Inf. Sci., vol. 193, pp. 81-103, 2012.
[http://dx.doi.org/10.1016/j.ins.2012.01.005]
[25]
J. Tian, "Improvement of quantum-behaved particle swarm optimization algorithm for high-dimensional and multi-modal functions", Control and Decision, vol. 31, no. 11, pp. 1967-1972, 2016.
[26]
Y.Q. Zhu, S.L. Zhou, and H.X. Zou, "Combination method of conflict evidences and evaluation rule based on compatibility ratio", J. Syst. Eng. Electron., vol. 36, no. 6, pp. 1118-1123, 2014.
[27]
T-C. Lin, "Decision-based filter based on SVM and evidence theory for image noise removal", Neural Comput. Appl., vol. 21, no. 4, pp. 695-703, 2012.
[http://dx.doi.org/10.1007/s00521-011-0648-9]
[28]
J. Wang, K.Y. Qiao, and Z.Y. Zhang, "An improvement for combination rule in evidence theory", Future Gener. Comput. Syst., vol. 91, pp. 1-9, 2019.
[http://dx.doi.org/10.1016/j.future.2018.08.010]
[29]
Q. Long, Y.Q. Liu, and Y.P. Yang, "Fault diagnosis method of wind turbine gearbox based on BP neural network trained by particle swarm optimization algorithm", Taiyang Neng Xuebao, vol. 33, no. 1, pp. 120-125, 2012.
[30]
J.T. Cheng, L. Wang, and Y. Xiong, "Modified cuckoo search algorithm and the prediction of flashover voltage of insulators", Neural Comput. Appl., vol. 30, no. 2, pp. 355-370, 2018.
[http://dx.doi.org/10.1007/s00521-017-3179-1]

Rights & Permissions Print Cite
Article Metrics
5
© 2024 Bentham Science Publishers | Privacy Policy