Abstract
In this paper, a new methodology is presented for developing a diagnostic system using waveform signals with limited or with no prior fault information. The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment. By using this methodology, a diagnostic system can be developed and its performance is continuously improved as the knowledge of process faults is automatically accumulated during production. As a real example, the tonnage signal analysis for stamping process monitoring is provided to demonstrate the implementation of this methodology.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alt, F. B. (1985) Encyclopedia of Statistical Science, Vol. 6, N. L. Johnson and S. Kotz, (eds.) John Wiley, New York, pp. 110-122.
Coifman, R. R. and Yale, F. M. (1992) Adapted waveform analysis and denoising, Meyer, Y. and Roques, S. (eds.) Progress in Wavelet Analysis and Applications, in the Proceeding of the International Conference, Wavelets and Applications, Toulouse, France, pp. 63-93.
Deibert, R., and Hofling, T. (1992) Model based fault diagnosis of industrial robot, a comparison of different methods, Judd, R. P. and Kheir, N. A., (eds.), IFAC Workshop: Intelligent Manufacturing Systems, Dearborn, MI.
Donoho, D. L. and Johnstone, I. M. (1994) Ideal spatial adaptation via wavelet shrinkage. Biometrika, 81, 425-455.
Daubechies, I. (1992) Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA.
Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition, Academic Press, New York.
Hotelling, H. (1947) Multivariate quality control. Techniques of Statistical Analysis, Eisenhart, Hastay and Wallis (eds.), McGraw-Hill, New York.
Jin, J. and Shi, J. (1999) Feature-preserving data compression of stamping tonnage information using wavelets. Technometrics, 41(4), 327-339.
Kasashima, N., Mori, K., Ruiz, G. H. and Taniguchi, N. (1995) Online failure detection in face milling using discrete wavelet transform. Annals of the CIRP, 44(1), 483-487.
Kharin, Y. (1992), Robustness in Statistical Pattern Recognition, Kluwer Academic Publishers, Dordrecht.
Mori, K., Kasashima, N., Yoshioka, T. and Ueno, Y. (1996) Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals. Wear 195, Elsevier Science S. A., pp. 162-168.
Pearson, E. S. and Hartley, H. O. (1972) Biometrika Tables for Statisticians, Cambridge. Published for the Biometrika Trustees at the University Press, II, pp. 66-68.
Seber, G. A. F. (1984) Multivariate Observations, John Wiley & Sons, New York, NY.
Strang, G. and Nguyen, T. (1996) Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, MA.
Tansel, I. N., Mekdeci, C., Rodriguez, O. and Uragun, B. (1993) Monitoring drill condition with wavelet based encoding and neural networks. International Journal of Machine Tools Manufacturing, 33(4), 559-575.
Tracy, N. D., Young, J. C. and Mason, R. L. (1992) Multivariate control charts for individual observations. Journal of Quality Technology, 24(2), 88-95.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Jin, J., Shi, J. Automatic feature extraction of waveform signals for in-process diagnostic performance improvement. Journal of Intelligent Manufacturing 12, 257–268 (2001). https://doi.org/10.1023/A:1011248925750
Issue Date:
DOI: https://doi.org/10.1023/A:1011248925750