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Original Articles

An improved cuckoo search algorithm and its application in vibration fault diagnosis for a hydroelectric generating unit

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Pages 1593-1608 | Received 23 Apr 2017, Accepted 21 Oct 2017, Published online: 28 Nov 2017

References

  • Bazan, Gustavo Henrique, Paulo Rogério Scalassara, Wagner Endo, Alessandro Goedtel, Wagner Fontes Godoy, and Rodrigo Henrique Cunha Paláciosa. 2017. “Stator Fault Analysis of Three-Phase Induction Motors Using Information Measures and Artificial Neural Networks.” Electric Power Systems Research 143: 347–356. doi: 10.1016/j.epsr.2016.09.031
  • Brest, Janez, Sǎso Greiner, Borko Bǒskovic, Marjan Mernik, and Viljem Zumer. 2006. “Self-Adapting Control Parameters in Differential Evolution: a Comparative Study on Numerical Benchmark Problems.” IEEE Transactions on Evolutionary Computation 10 (6): 646–657. doi: 10.1109/TEVC.2006.872133
  • Dhabal, Supriya, and Palaniandavar Venkateswaran. 2017. “An Efficient Gbest-Guided Cuckoo Search Algorithm for Higher Order Two Channel Filter Bank Design.” Swarm and Evolutionary Computation 33: 68–84. doi: 10.1016/j.swevo.2016.10.003
  • Erdal, F. 2017. “A Firefly Algorithm for Optimum Design of New-Generation Beams.” Engineering Optimization 49 (6): 915–931. doi: 10.1080/0305215X.2016.1218003
  • Firouzjaee, Hossein Abedi, Javidan Kazemi Kordestani, and Mohammad Reza Meybodi. 2017. “Cuckoo Search with Composite Flight Operator for Numerical Optimization Problems and its Application in Tunneling.” Engineering Optimization 49 (4): 597–616. doi: 10.1080/0305215X.2016.1206535
  • Geem, Zong Woo, and Yourim Yoon. 2017. “Harmony Search Optimization of Renewable Energy Charging with Energy Storage System.” International Journal of Electrical Power & Energy Systems 86: 120–126. doi: 10.1016/j.ijepes.2016.04.028
  • Huang, L., S. Ding, S. H. Yu, J. Wang, and K. Lu. 2016. “Chaos-Enhanced Cuckoo Search Optimization Algorithms for Global Optimization.” Applied Mathematical Modelling 40: 3860–3875. doi: 10.1016/j.apm.2015.10.052
  • Huang, J. D., L. Gao, and X. Y. Li. 2015. “An Effective Teaching-Learning-Based Cuckoo Search Algorithm for Parameter Optimization Problems in Structure Designing and Machining Processes.” Applied Soft Computing 36: 349–356. doi: 10.1016/j.asoc.2015.07.031
  • Jiang, Q. Y., L. Wang, X. H. Hei, G. L. Yu, and Y. Y. Lin. 2016. “The Performance Comparison of a New Version of Artificial Raindrop Algorithm on Global Numerical Optimization.” Neurocomputing 179: 1–25. doi: 10.1016/j.neucom.2015.09.093
  • Khajeh, Mostafa, and Elham Jahanbin. 2014. “Application of Cuckoo Optimization Algorithm-Artificial Neural Network Method of Zinc Oxide Nanoparticles-Chitosan for Extraction of Uranium from Water Samples.” Chemometrics and Intelligent Laboratory Systems 135: 70–75. doi: 10.1016/j.chemolab.2014.04.003
  • Kiani, Morteza, and Ali R. Yildiz. 2016. “A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and NVH Optimization.” Archives of Computational Methods in Engineering 23 (4): 723–734. doi: 10.1007/s11831-015-9155-y
  • Lashkari, Negin, Javad Poshtan, and Hamid Fekri Azgomi. 2015. “Simulative and Experimental Investigation on Stator Winding Turn and Unbalanced Supply Voltage Fault Diagnosis in Induction Motors Using Artificial Neural Networks.” ISA Transactions 59: 334–342. doi: 10.1016/j.isatra.2015.08.001
  • Li, C. S., J. Z. Zhou, J. Xiao, and H. Xiao. 2013. “Hydraulic Turbine Governing System Identification Using T-S Fuzzy Model Optimized by Chaotic Gravitational Search Algorithm.” Engineering Applications of Artificial Intelligence 26: 2073–2082. doi: 10.1016/j.engappai.2013.04.002
  • Liu, X. Y., and M. L. Fu. 2015. “Cuckoo Search Algorithm Based on Frog Leaping Local Search and Chaos Theory.” Applied Mathematics and Computation 266: 1083–1092. doi: 10.1016/j.amc.2015.06.041
  • Lu, S. B., J. H. Wang, and Y. G. Xue. 2016. “Study on Multi-fractal Fault Diagnosis Based on EMD Fusion in Hydraulic Engineering.” Applied Thermal Engineering 103: 798–806. doi: 10.1016/j.applthermaleng.2016.04.036
  • Ma, D. Y., Y. C. Liang, X. S. Zhao, R. C. Guan, and X. H. Shi. 2013. “Multi-BP Expert System for Fault Diagnosis of Power System.” Engineering Applications of Artificial Intelligence 26: 937–944. doi: 10.1016/j.engappai.2012.03.017
  • Moharam, Riham, and Ehab Morsy. 2017. “Genetic Algorithms to Balanced Tree Structures in Graphs.” Swarm and Evolutionary Computation 32: 132–139. doi: 10.1016/j.swevo.2016.06.005
  • Naik, Manoj Kumar, and Rutuparna Panda. 2016. “A Novel Adaptive Cuckoo Search Algorithm for Intrinsic Discriminant Analysis Based Face Recognition.” Applied Soft Computing 38: 661–675. doi: 10.1016/j.asoc.2015.10.039
  • Pitakaso, Rapeepan, and Kanchana Sethanan. 2016. “Modified Differential Evolution Algorithm for Simple Assembly Line Balancing with a Limit on the Number of Machine Types.” Engineering Optimization 48 (2): 253–271. doi: 10.1080/0305215X.2015.1005082
  • Rakhshani, Hojjat, and Amin Rahati. 2017. “Snap-Drift Cuckoo Search: A Novel Cuckoo Search Optimization Algorithm.” Applied Soft Computing 52: 771–794. doi: 10.1016/j.asoc.2016.09.048
  • Wang, Q. H., T. H. Yang, R. J. Shen, and B. Yang. 2012. “Fault Caused by Vibration Diagnosis Expert System for a Pump Storage Group.” [In Chinese]. Journal of Vibration and Shock 31 (7): 158–161.
  • Wang, L. J., Y. L. Yin, and Y. W. Zhong. 2013. “Cuckoo Search Algorithm with Dimension by Dimension Improvement.” [In Chinese]. Journal of Software 24 (11): 2687–2698. doi: 10.3724/SP.J.1001.2013.04476
  • Wang, L. J., Y. L. Yin, and Y. W. Zhong. 2015. “Cuckoo Search with Varied Scaling Factor.” Frontiers of Computer Science 9 (4): 623–635. doi: 10.1007/s11704-015-4178-y
  • Wang, J., and B. H. Zhou. 2016. “A Hybrid Adaptive Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation.” Neural Computing and Applications 27 (6): 1511–1517. doi: 10.1007/s00521-015-1949-1
  • Yang, X. S., and S. Deb. 2010. “Engineering Optimisation by Cuckoo Search.” International Journal of Mathematical Modelling and Numerical Optimisation 1 (4): 330–343. doi: 10.1504/IJMMNO.2010.035430
  • Yang, X. S., and S. Deb. 2014. “Cuckoo Search: Recent Advances and Applications.” Neural Computing and Applications 24 (1): 169–174. doi: 10.1007/s00521-013-1367-1
  • Yildiz, Ali R. 2013. “Cuckoo Search Algorithm for the Selection of Optimal Machining Parameters in Milling Operations.” The International Journal of Advanced Manufacturing Technology 64 (1): 55–61. doi: 10.1007/s00170-012-4013-7
  • Yildiz, Betül Sultan. 2017. “A Comparative Investigation of Eight Recent Population-Based Optimisation Algorithms for Mechanical and Structural Design Problems.” International Journal of Vehicle Design 73 (1-3): 208–218. doi: 10.1504/IJVD.2017.082603
  • Zhang, B. D., Y. Tian, J. P. Zou, X. F. Liu, H. F. Wu, and L. Long. 2013. “Vibration Fault Diagnosis of a Hydro-Generating Unit Based on Choquet Fuzzy Integration.” [In Chinese]. Journal of Vibration and Shock 32 (12): 61–66.
  • Zhang, Y. W., L. Wang, and Q. D. Wu. 2014. “Dynamic Adaptation Cuckoo Search Algorithm.” [In Chinese]. Control and Decision 29 (4): 617–622.
  • Zhang, X. Y., X. P. Zhang, and B. P. Su. 2015. “Vibrant Fault Diagnosis for Hydro-Turbine Generating Unit Using Minmax Kernel K-Means Clustering Algorithm.” [In Chinese]. Power System Protection and Control 43 (5): 27–34.
  • Zhang, X. Y., J. Z. Zhou, J. Guo, Q. Zou, and Z. W. Huang. 2012. “Vibrant Fault Diagnosis for Hydroelectric Generator Units with a New Combination of Rough Sets and Support Vector Machine.” Expert Systems with Applications 39: 2621–2628. doi: 10.1016/j.eswa.2011.08.117
  • Zhao, X. Z., X. S. Gao, and Z. C. Hu. 2007. “Evolutionary Programming Based on Non-uniform Mutation.” Applied Mathematics and Computation 192: 1–11. doi: 10.1016/j.amc.2006.06.107
  • Zhao, Z. C., G. L. Liu, H. Q. Liu, and G. S. Zhao. 2014. “Particle Swarm Optimization Algorithm Based on Non-uniform Mutation and Multiple Stages Perturbation.” [In Chinese]. Chinese Journal of Computers 37 (9): 2058–2070.
  • Zhu, W. L., J. Z. Zhou, X. Xia, C. S. Li, J. Xiao, H. Xiao, and X. X. Zhang. 2014. “A Novel KICA-PCA Fault Detection Model for Condition Process of Hydroelectric Generating Unit.” Measurement 58: 197–206. doi: 10.1016/j.measurement.2014.08.026

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