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Identification of the Defective Transmission Devices Using the Wavelet Transform
June 2005 (vol. 27 no. 6)
pp. 919-928
In this paper, a system is described that uses the wavelet transform to automatically identify the particular failure mode of a known defective transmission device. The problem of identifying a particular failure mode within a costly failed assembly is of benefit in practical applications. In this system, external acoustic sensors, instead of intrusive vibrometers, are used to record the acoustic data of the operating transmission device. A skilled factory worker, who is unfamiliar with statistical classification, helps to determine the feature vector of the particular failure mode in the feature extraction process. In the automatic identification part, an improved learning vector quantization (LVQ) method with normalizing the inputting feature vectors is proposed to compensate for variations in practical data. Some acoustic data, which are collected from the manufacturing site, are utilized to test the effectiveness of the described identification system. The experimental results show that this system can identify the particular failure mode of a defective transmission device and find out the causes of failure successfully.

[1] J.J. Zakrajsek, “A Review of Transmission Diagnostics Research at NASA Lewis Research Center,” NASA TM-106746, ARL-TR-599, 1994.
[2] F.K. Choy, S. Huang, J.J. Zakrajsek, R.F. Handschuh, and D. Townsend, “Vibration Signature Analysis of a Faulted Gear Transmission System,” NASA TM-106623, 1994.
[3] W.J. Wang and P.D. McFadden, “Application of Wavelets to Gearbox Vibration Signals for Fault Detection,” J. Sound and Vibration, vol. 192, no. 5, pp. 927-939, May 1996.
[4] S. Furui, Digital Speech Processing, Synthesis, and Recognition. New York: Marcel Dekker, 1989.
[5] A. Gersho and R.M. Gray, Vector Quantization and Signal Compression. Kluwer Academic Publishers, 1991.
[6] M. Teranishi, S. Omatu, and T. Kosaka, “New and Used Bill Money Classification Using Spectral Information Based on Acoustic Data of Banking Machine,” Trans. IEE of Japan, vol. 117-C, no. 11, pp. 1677-1681, 1997.
[7] B. Wang, S. Omatu, and T. Abe, “Quality Evaluation of Machines Using the LVQ,” J. Signal Processing, vol. 7, no. 1, pp. 61-68, Jan. 2003.
[8] I. Daubechies, “The Wavelet Transform, Time-Frequency Localization and Signal Analysis,” IEEE Trans. Information Theory, vol. 36, no. 5, pp. 961-1005, Sept. 1990.
[9] L. Cohen, “Time-Frequency Distributions— A Review,” Proc. IEEE, vol. 77, no. 7, pp. 941-981, July 1989.
[10] W. Li, F. Gu, A.D. Ball, and A.Y.T. Leung, “Applying the Continuous Wavelet Transform to the Analysis of Diesel Engine Conditions,” Proc. Int'l Conf. Computational and Experimental Methods in Reciprocating Engines, Nov. 2000.
[11] M. Yamaguchi and A. Morimoto, “Wavelets and their Applications,” Bull. Japan Soc. Industrial and Applied Math., vol. 1, no. 3, pp. 202-213, 1991.
[12] A.K. Jain, R.P.W. Duin, J. Mao, “Statistical Pattern Recognition: A Review,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, Jan. 2000.
[13] T. Kohonen, Self-Organizing Maps. Berlin: Springer, 2001.
[14] A. Boggess and F.J. Narcowich, A First Course in Wavelets with Fourier Analysis. Beijing: Publishing House of Electronics Industry, 2002.

Index Terms:
Automatic identification, feature extraction, wavelet transform, LVQ, GA.
Bingchen Wang, Sigeru Omatu, Toshiro Abe, "Identification of the Defective Transmission Devices Using the Wavelet Transform," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 919-928, June 2005, doi:10.1109/TPAMI.2005.121
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