基于小波变换的航天继电器多余物材质分类

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (496 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The material information is very important for preventing from generation of the remainders. According to ever research around with sound signal, this paper proposes methods of material classification based on wavelet transform for aerospace relay using acceleration-disturbing signal. The disturbing signal is jointed together for features in focus by detection algorithm with three thresholds. Metallic and nonmetallic particles are categorized by spectrum barycenter defined in frequency domain, and the boundary of classification is determined as 50 kHz in experiments. The definite material of remainder paticles are categorized based on BP neural network by energy distribution vector, which is defined by wavelet transform. The algorithms are validated by experiment using particles whose mass exceed 1 mg, and the classification accuracy is up to 67.78% and 76.67% respectively. The classification methods present in this paper can be applied in remainder detecting for other military electronic components and electronic devices.

Key words： Aerospace relay PIND wavelet transform BP neural network spectrum barycenter energy distribution vector

Received: 03 July 2008 Published: 14 February 2014

PACS:

TM581.3

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhai Guofu
	Wang Shicheng
	Wang Shujuan

Cite this article:

Zhai Guofu,Wang Shicheng,Wang Shujuan. Classification of Remainder Material for Aerospace Relay Based on Wavelet Transform[J]. Transactions of China Electrotechnical Society, 2009, 24(5): 52-59.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2009/V24/I5/52

[1] MIL-STD-883G, Department of Defense Test Method Standard Microcircuits [S]. 2006.
[2] Losi Jean Scaglione. Neural network application to particle impact noise detection[C]. IEEE International Conference on Neural Nerworks, 1994: 3415-3419.
[3] Gao Hongliang, Zhang Hui, Wang Shujuan. Research on auto-detection for remainder particles of aerospace relay based on wavelet analysis[J]. Chinese Journal of Aeronautics, 2007, 20(1): 75-80.
[4] Wang Shujuan, Gao Hongliang, Zhai Guofu. Research on feature extraction of remnant particles of aerospace relays[J]. Chinese Journal of Aeronautics, 2007, 20(3): 253-259.
[5] 张勇, 李昕, 刘君华, 等. 数据融合新技术在识别变压器油中四种特征气体的研究[J]. 中国电机工程学报, 2001, 21(8): 11-14.
[6] Daubechies I. Where do wavelets come from? -a personal point of view[J]. In: Proc. IEEE, 1996, 84(4): 510-513.
[7] 潘泉, 张磊, 孟晋丽, 等. 小波滤波方法及应用[M].北京: 清华大学出版社, 2005.
[8] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Trans. on Pattren Anal. Machine Intell., 1989, 11(7): 674-693.
[9] 胡广书. 现代信号处理教程[M]. 北京: 清华大学出版社, 2004.
[10] 张君, 韩璞, 董泽, 等. 基于小波变换的振动信号分析中能量两泄露的研究[J]. 中国电机工程学报, 2004, 24(10): 238-243.
[11] Ma Guangcheng, Yi Guoxing, Wen Qiyong, et al. Particle impact noise detection in sealed relays based on neural network[C]. Proceedings of the 49th IEEE Holm Conference on Electrical Contacts, 2003: 214-218.
[12] 李瑞莹, 康锐. 基于神经网络的故障率预测方法[J]. 航空学报, 2008, 29(2): 357-363.
[13] Mahanty R N, Dutta Gupta P B. Application of RBF neual network to fault classification and location in transmission lines[J]. IEEE Proceedings on Gen- eration, Transmission and Distribution, 2004, 151(2): 201-212.
[14] 周建华, 胡敏强, 周鹗.基于思维模式融合故障诊断的专家系统御神经网络[J]. 电工技术学报, 1999, 14(2): 1-4.