The application of Shuffled Frog Leaping Algorithm to Wavelet Neural Networks for acoustic emission source location

被引:24
作者
Cheng, Xinmin [1 ]
Zhang, Xiaodan [2 ]
Zhao, Li [2 ]
Deng, Aideng [2 ]
Bao, Yongqiang [2 ]
Liu, Yong [3 ]
Jiang, Yunliang [1 ]
机构
[1] Huzhou Teachers Coll, Sch Informat & Engn, Huzhou 313000, Zhejiang, Peoples R China
[2] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China
[3] Zhejiang Univ, Inst Cyber Syst & Control, Hangzhou 310027, Zhejiang, Peoples R China
来源
COMPTES RENDUS MECANIQUE | 2014年 / 342卷 / 04期
关键词
Acoustic emission; Location; Wavelet Neural Network; Shuffled Frog Leaping Algorithm;
D O I
10.1016/j.crme.2013.12.006
中图分类号
O3 [力学];
学科分类号
070301 [无机化学];
摘要
When using acoustic emission to locate the friction fault source of rotating machinery, the effects of strong noise and waveform distortion make accurate locating difficult. Applying neural network for acoustic emission source location could be helpful. In the BP Wavelet Neural Network, BP is a local search algorithm, which falls into local minimum easily. The probability of successful search is low. We used Shuffled Frog Leaping Algorithm (SFLA) to optimize the parameters of the Wavelet Neural Network, and the optimized Wavelet Neural Network to locate the source. After having performed the experiments of friction acoustic emission's source location on the rotor friction test machine, the results show that the calculation of SFLA is simple and effective, and that locating is accurate with proper structure of the network and input parameters. (C) 2014 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:229 / 233
页数:5
相关论文
共 15 条
[1]
Bian Hai-long, 2008, Proceedings of the CSEE, V28, P104
[2]
Bodine H. L., 1995, Intelligent Engineering Systems Through Artificial Neural Networks. Vol.5. Fuzzy Logic and Evolutionary Programming. Proceedings of the Artificial Neural Networks in Engineering (ANNIE'95), P467
[3]
Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization [J].
Eusuff, M ;
Lansey, K ;
Pasha, F .
ENGINEERING OPTIMIZATION, 2006, 38 (02) :129-154
[4]
GORMAN MR, 2000, J ACOUSTIC EMISSION, V10, P53
[5]
HIRONOBU Y, 1995, ACOUSTIC EMISSION, V10, P35
[6]
Hu Feng, 2009, 2009 1st International Conference on Information Science and Engineering (ICISE 2009), P3600, DOI 10.1109/ICISE.2009.335
[7]
Repair Effects and Acoustic Emission Technique-Based Fracture Evaluation for Predamaged Concrete Columns Confined with Fiber-Reinforced Polymers [J].
Ma, Gao ;
Li, Hui ;
Duan, Zhongdong .
JOURNAL OF COMPOSITES FOR CONSTRUCTION, 2012, 16 (06) :626-639
[8]
Modal acoustic emission of damage accumulation in a woven SiC/SiC composite [J].
Morscher, GN .
COMPOSITES SCIENCE AND TECHNOLOGY, 1999, 59 (05) :687-697
[9]
Acoustic emission localization in plates with dispersion and reverberations using sparse PZT sensors in passive mode [J].
Perelli, Alessandro ;
De Marchi, Luca ;
Marzani, Alessandro ;
Speciale, Nicolo .
SMART MATERIALS AND STRUCTURES, 2012, 21 (02)
[10]
Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm [J].
Rahimi-Vahed, Alireza ;
Mirzaei, Ali Hossein .
SOFT COMPUTING, 2008, 12 (05) :435-452