Kim K B, Yoon D J, Jeong J C, et al. Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements[J]. NDT&E International, 2004, 37(6): 423-429.
[3]
Sasikumar T, RajendraBoopathy S, Usha K M, et al. Failure strength prediction of unidirectional tensile coupons using acoustic emission peak amplitude and energy parameter with artificial neural networks[J]. Composites Science and Technology, 2009, 69(7-8): 1151-1155.
[4]
Rajendraboopathy S, Sasikumar T, Usha K M, et al. Neural network prediction of failure strength of composite tensile specimens using acoustic emission counts[J]. Journal of Non Destructive Testing & Evaluation, 2008, 7(2): 21-26.
[5]
颜威利, 杨庆新, 汪友华, 等. 电气工程电磁场数值计算
[6]
[M]. 北京: 机械工业出版社, 2005.
[7]
陈明祥. 弹塑性力学
[8]
Leone C, Caprino G, de Iorio I. Interpreting acoustic emission signals by artificial neural networks to predict the residual strength of pre-fatigued GFRP laminates[J]. Composites Science and Technology, 2006, 66(2): 233-239.
[9]
Liu Suzhen, Yang Qinxin, Jin Liang. Application of electromagnetic acoustic emission technology in non-destructive testing[J]. Transactions of China Electro-technical Society, 2009, 24(1): 23-27.
[10]
Finkel P, Godinez V. Electromagnetic simulation of the ultrasonic signal for nondestructive detection of ferromagnetic inclusions and flaws[J]. IEEE Trans-actions on Magnetics, 2004, 40(4): 2179-2181.
[11]
Kuo C C. Artificial recognition system for defective types of transformers by acoustic emission[J]. Expert Systems with Applications, 2009, 36(7): 10304-10311.
[12]
[M]. 北京: 科学出版社, 2007.
[13]
Velayudham A, Krishnamurthy R, Soundarapandian T. Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform[J]. Materials Science and Engineering A, 2005, 412: 141-145.
[14]
Oliveira R de, Marques A T. Health monitoring of FRP using acoustic emission and artificial neural networks[J]. Computers and Structures, 2008, 86(3-5): 367-373.