Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

Author(s): Jiatang Cheng*, Yan Xiong.

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 12 , Issue 3 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents.

Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network.

Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy.

Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

Keywords: Wind turbine, gearbox, fault diagnosis, Quantum Particle Swarm Optimization (QPSO), BP neural network, PSOBP.

[1]
Y. Gui, Q.K. Han, Z. Li, and F.L. Chu, "Fault diagnosis of planetary gear system under time-varying speed conditions", J. Vib. Meas. Diagn., vol. 36, no. 2, pp. 220-226, 2016.
[2]
L. Piancastelli, L. Frizziero, and I. Rocchi, "A low-cost, mass-producible, wheeled wind turbine for easy production of renewableenergy", Far East J. Electron. Commun., vol. 12, pp. 19-37, 2014.
[3]
Y. Luo, and L.J. Zhen, "Diagnosis method of turbine gearbox gearcrack based on wavelet packet and cepstrum analysis", J. Vib. Shock, vol. 34, no. 3, pp. 210-214, 2015.
[4]
A. Hajnayeb, A. Ghasemloonia, S.E. Khadem, and M.H. Moradi, "Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis", Expert Syst. Appl., vol. 38, pp. 10205-10209, 2011.
[5]
J.T. Cheng, L. Ai, Z.M. Duan, and Y. Xiong, "Application of CQPSO-BP algorithm in fault diagnosis method of wind turbine gearbox", Acta Energiae Solaris Sinica,, vol. 38, no. 8, pp. 2112-2116, 2017.
[6]
F.F. Chen, B.P. Tang, and R.X. Chen, "A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm", Measurement, vol. 46, pp. 220-232, 2013.
[7]
C.Q. Shen, Z.K. Zhu, W.G. Huang, and F.R. Kong, "Gearbox fault diagnosis based on support vector regression", J. Vib. Meas. Diagn., vol. 33, no. 5, pp. 775-781, 2013.
[8]
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.
[9]
Z.P. Feng, and M. Liang, "Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding", J. Sound Vibrat., vol. 333, no. 20, pp. 5196-5211, 2014.
[10]
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", Acta Energiae Solaris Sinica,, vol. 33, no. 1, pp. 120-125, 2012.
[11]
C.H. Zhao, H.J. Dong, and X.Y. Zhong, "SVM parameter optimization in fault diagnosis for wind power gear box", China Mech. Eng., vol. 26, no. 16, pp. 2222-2225, 2015.
[12]
D.L. Yang, Y.L. Liu, X.J. Li, and W. Zhou, "Gearbox fault diagnosis based on bacterial foraging algorithm optimization decisions", J. Cent. South Univ. Sci. Technol., vol. 46, no. 4, pp. 1224-1230, 2015.
[13]
A.N. Hanoon, M.S. Jaafar, F. Hejazi, and F.N.A. Abdul Aziz, "Energy absorption evaluation of reinforced concrete beams under various loading rates based on particle swarm optimization technique", Eng. Optim., vol. 49, no. 9, pp. 1483-1501, 2017.
[14]
N. Yadav, A. Yadav, M. Kumar, and J.H. Kim, "An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem", Neural Comput. Appl., vol. 28, no. 1, pp. 171-178, 2017.
[15]
R. Gupta, S.K. Muttoo, and S.K. Pal, "Fuzzy c-means clustering and particle swarm optimization based scheme for common service center location allocation", Appl. Intell., vol. 47, no. 3, pp. 624-643, 2017.
[16]
H. Li, and K. Chandrashekhara, "Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades", Eng. Optim., vol. 47, no. 9, pp. 1191-1207, 2015.
[17]
N. Kumar, and D.P. Vidyarthi, "A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems", Eng. Comput., vol. 32, no. 1, pp. 35-47, 2016.
[18]
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.
[19]
M.M. Soliman, and A.E. Hassanien, "Hoda M. Onsi, “An adaptive watermarking approach based on weighted quantum particle swarm optimization", Neural Comput. Appl., vol. 27, no. 2, pp. 469-481, 2016.
[20]
H.W. Chen, J.F. Zhu, Y. Ruan, Z.H. Liu, and S.M. Zhao, "Quantum particle swarm optimization algorithm with crossover operator", J. Southeast Univ. Nat. Sci. Ed., vol. 46, no. 1, pp. 23-29, 2016.
[21]
M.R. Singh, and S.S. Mahapatra, "A quantum behaved particle swarm optimization for flexible job shop scheduling", Comput. Ind. Eng., vol. 93, pp. 36-44, 2016.
[22]
R. Logesh, V. Subramaniyaswamy, V. Vijayakumar, X.Z. Gao, and V. Indragandhi, "A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city", Future Gener. Comput. Syst., vol. 83, pp. 653-673, 2018.
[23]
O.E. Turgut, "Hybrid chaotic quantum behaved particle swarm optimization algorithm for thermal design of plate fin heat exchangers", Appl. Math. Model., vol. 40, pp. 50-69, 2016.
[24]
B. Haddar, M. Khemakhem, S. Hanafi, and C. Wilbaut, "A hybrid quantum particle swarm optimization for the multidimensional Knapsack problem", Eng. Appl. Artif. Intell., vol. 55, pp. 1-13, 2016.
[25]
J.T. Cheng, L. Wang, and Y. Xiong, "Modified cuckoo search algorithm and the prediction of flashover voltage of insulators", Neural Comput. Appl., vol. 28, pp. 1-16, 2017.
[26]
R.V. Rao, V.J. Savsani, and D.P. Vakharia, "Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems", Inf. Sci., vol. 183, pp. 1-15, 2012.
[27]
J.T. Cheng, L. Wang, and Y. Xiong, "An improved cuckoo search algorithm and its application in vibration fault diagnosis for a hydroelectric generating unit", Eng. Optim., vol. 50, pp. 1-16, 2017.
[28]
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.
[29]
Y.Q. Liu, and Q. Xu, "D.I., D. Tian and Q. Long, “Fault identification of wind turbine drivetrain using BP neural network based on gravitational search algorithm", J. Vib. Shock, vol. 34, no. 2, pp. 134-137, 2015.
[30]
Q. Cheng, H.B. Zhou, and J. Cheng, "The Fisher-markov selector: Fast selecting maximally separable feature subset for multi-class classification with applications to high-dimensional data", IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 6, pp. 1217-1233, 2011.


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 12
ISSUE: 3
Year: 2019
Page: [277 - 283]
Pages: 7
DOI: 10.2174/2352096511666180629152127
Price: $58

Article Metrics

PDF: 13
HTML: 6