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TECHNICAL PAPERS

Pattern Recognition for Automatic Machinery Fault Diagnosis

[+] Author and Article Information
Qiao Sun, Ping Chen, Dajun Zhang

Dept of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada T2N 1N4

Fengfeng Xi

Dept of Mechanical, Aerospace, and Industrial Eng., Ryerson University, Toronto, Ontario, Canada M5B 2K3e-mail: fengxi@ryerson.ca

J. Vib. Acoust 126(2), 307-316 (May 04, 2004) (10 pages) doi:10.1115/1.1687391 History: Received December 01, 2002; Revised April 01, 2003; Online May 04, 2004
Copyright © 2004 by ASME
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References

Howard, I., 1994, “A Review of Rolling Element Bearing Vibration—Detection, Diagnosis and Prognosis,” Defense Science and Technology Organization, Australia.
Li,  C. Q., and Pickering,  C. J. D., 1992, “Robustness and Sensitivity of Non-Dimensional Amplitude Parameters for Diagnosis of Fatigue Spalling,” Condition Monitoring and Diagnostic Technology, 2(3), pp. 81–84.
McFadden,  P. D., and Smith,  J. D., 1984, “Vibration Monitoring of Rolling Element Bearings by the High Frequency Resonance Technique—A Review,” Tribol. Int., 17(1), pp. 3–10.
Sun,  Q., and Ying,  T., 2002, “Singularity Detection Using Continuous Wavelet Transform for Bearing Fault Diagnosis,” Mech. Syst. Signal Process., 16(6), pp. 1025–1041.
Xi,  F., Sun,  Q., and Krishnappa,  G., 2000, “Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters,” J. Vib. Control, 6, pp. 375–392.
Li, C. J., Ma, J., Hwang, B., and Nickerson, G. W., 1991, “Pattern Recognition Based Bicoherence Analysis of Vibrations for Bearing Condition Monitoring,” Proceedings of the Symposium on Sensors, Controls, and Quality Issues in Manufacturing, ASME Winter Annual Meeting, Atlanta Georgia, pp. 1–11.
Michael,  D., and Houchin,  J., 1979, “Automatic EEG Analysis: a Segmentation Procedure Based on the Autocorrelation Function,” Electroenceph. Clin. Neurophysiol., 46, pp. 232–235.
Bodenstein,  G., and Praetorious,  H. M., 1977, “Feature Extraction from the Electroencephalogram by Adaptive Segmentation,” Proc. IEEE, 65(5), pp. 642–652.
Sun, Q., Xi, F., and Krishnappa, G., 1998, “Signature Analysis of Rolling Element Bearing Defects,” Proceedings of CSME Forum, Toronto, pp. 423–429.
Sun, Q., Xi, F., Chen, P., and Krishnappa, G., 1999, “Bearing Condition Monitoring through Pattern Recognition Analysis,” The 6th International Conference on Sound and Vibration, Denmark.
Sun, Q., Tang, Y., and Lu, W., 2004, “Feature Extraction and Classification for Drill Wear Monitoring,” The 14th International Conference on Flexible Automation and Intelligent Manufacturing, Toronto, Canada, July.
Tarassenko, L., 1998 A Guide to Neural Computing Applications, John Wiley & Sons.
Andrews, H. C., 1972, Introduction to Mathematical Techniques in Pattern Recognition, Wiley-Interscience.

Figures

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Pattern recognition method for machinery fault diagnosis
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(a) Vibration signal from bearing with inner race defect (b) Segmented vibration signal in one inner race rotation
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Schematic of rolling element bearing
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(a) Vibration signal of bearing with roller defect (b) Segmented vibration signal within one cage rotation
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(a) Vibration signal of bearing with outer race defect (b) Segmented vibration signal within one cage rotation
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A fully connected three layered neural network
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(a) Mapping from feature space to classification space (b) Piecewise linear boundaries
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Diagnosis on the classification space

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