Abstract
Damage location detection has direct relationship with the field of aerospace structure as the detection system can inspect any exterior damage that may affect the operations of the equipment. In the literature, several kinds of learning algorithms have been applied in this field to construct the detection system and some of them gave good results. However, most learning algorithms are time-consuming due to their computational complexity so that the real-time requirement in many practical applications cannot be fulfilled. Kernel extreme learning machine (kernel ELM) is a learning algorithm, which has good prediction performance while maintaining extremely fast learning speed. Kernel ELM is originally applied to this research to predict the location of impact event on a clamped aluminum plate that simulates the shell of aerospace structures. The results were compared with several previous work, including support vector machine (SVM), and conventional back-propagation neural networks (BPNN). The comparison result reveals the effectiveness of kernel ELM for impact detection, showing that kernel ELM has comparable accuracy to SVM but much faster speed on current application than SVM and BPNN.












Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Sohn H, Farrar CR, Hunter NF, Worden K (2001) Structural health monitoring using statistical pattern recognition techniques. Trans Am Soc Mech Eng J Dyn Syst Meas Control 123:706–711
Mujica LE, Vehi J, Staszewski W, Worden K (2008) Impact damage detection in aircraft composites using knowledge-based reasoning. Struct Health Monit 7:215–230
LeClerc J, Worden K, Staszewski W, Haywood J (2007) Impact detection in an aircraft composite panel: a neural-network approach. J Sound Vib 299:672–682
Watkins SE, Akhavan F, Dua R, Chandrashekhara K, Wunsch DC (2007) Impact-induced damage characterization of composite plates using neural networks. Smart Mater Struct 16:515
Jones RT, Sirkis JS, Friebele E (1997) Detection of impact location and magnitude for isotropic plates using neural networks. J Intell Mater Syst Struct 8:90–99
Jones RT, Sirkis JS, Friebele EJ, Kersey AD (1995) Location and magnitude of impact detection in composite plates using neural networks. In: Smart structures and materials’ 95, pp 469–480
Sung D-U, Oh J-H, Kim C-G, Hong C-S (2000) Impact monitoring of smart composite laminates using neural network and wavelet analysis. J Intell Mater Syst Struct 11:180–190
Friswell MI, Penny JET, Garvey SD (1998) A combined genetic and eigensensitivity algorithm for the location of damage in structures. Comput and Struct 69:547–556
Okafor AC, Otieno AW, Dutta A, Rao VS (2001) Detection and characterization of high-velocity impact damage in advanced composite plates using multi-sensing techniques. Compos Struct 54:289–297
Maseras-Gutierrez MA, Staszewski WJ, Found MS, Worden K (1998) Detection of impacts in composite materials using piezoceramic sensors and neural networks. pp 491–497
Yan G, Zhou L (2009) Impact load identification of composite structure using genetic algorithms. J Sound Vib 319:869–884
Worden K, Staszewski W (2000) Impact location and quantification on a composite panel using neural networks and a genetic algorithm. Strain 36:61–68
Wong P-K, Xu Q, Vong C-M, Wong H-C (2012) Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine. IEEE Trans Ind Electron 59:1988–2001
Xu Q, Wong P-K (2011) Hysteresis modeling and compensation of a piezostage using least squares support vector machines. Mechatronics 21:1239–1251
Xie J (2010) Improved least square support vector machine for structural damage detection. In: 2010 2nd international conference on computer engineering and technology (ICCET), pp V6-237–V6-240
Fu H, Xu Q (2013) Locating impact on structural plate using principal component analysis and support vector machines. Math Probl Eng 2013. http://dx.doi.org/10.1155/2013/352149
Huang GB, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of 2004 IEEE international joint conference on neural networks, pp 985–990
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
Huang G-B, Chen L, Siew C-K (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892
Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybernet 2:107–122
Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42:513–529
Huang G-B, Li M-B, Chen L, Siew C-K (2008) Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 71:576–583
Li M-B, Huang G-B, Saratchandran P, Sundararajan N (2005) Fully complex extreme learning machine. Neurocomputing 68:306–314
Liang N-Y, Huang G-B, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423
Huang G-B, Liang N-Y, Rong H-J, Saratchandran P, Sundararajan N (2005) On-line sequential extreme learning machine. Comput Intell 2005:232–237
Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062
Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468
(2013) kernel ELM toolbox, Matlab toolbox for kernel ELM. Available: http://www.ntu.edu.sg/home/egbhuang/elm_kernel.html
Acknowledgments
This work is co-supported by FDCT Macau SAR, Grant No. FDCT/075/2013/A, and the research grants of University of Macau, Grant Nos. MYRG075(Y1-L2)-FST13-VCM and MYRG075(Y2-L2)-FST12-VCM. The authors would also like to express their sincere gratitude to Dr. Q. S. Xu and Mr. Kehn Wong for offering the experiment devices and technical support.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fu, H., Vong, CM., Wong, PK. et al. Fast detection of impact location using kernel extreme learning machine. Neural Comput & Applic 27, 121–130 (2016). https://doi.org/10.1007/s00521-014-1568-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-014-1568-2