Abstract
This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The main purpose is to investigate different fault conditions for rotating machinery, such as imbalance, misalignment, base looseness and combination of imbalance and misalignment. In this study, we measured the non-stationary vibration signals induced by these fault conditions. Applying wavelet packets transform to these signals, the fractal dimension of each frequency channel was extracted and the box counting dimension was used to depict the failure characteristics of the fault conditions. The failure modes were then identified by a radial basis function neural network. An experiment was conducted and the results showed that the proposed method can detect and recognize different kinds of fault conditions. Therefore, it is concluded that the combination of wavelet packets-fractal technology and neural networks can provide an effective method to diagnose fault conditions of rotating machinery.
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References
Arbib, Michael A., 2002, “The Handbook of Brain Theory and Neural Networks Second Edition, Massachusetts MIT Press.
Boulahbal, D., Golnaraghi, M. F. and Ismail, F., 1999, “Amplitude and Phase Wavelet Maps for the Detection of Cracks in Geared Systems,”International Journal of Mechanical Systems and Signal Processing,Vol. 13, No. 3, pp. 423–436.
Catelani, M. and Fort, A., 2000, “Fault Diagnosis of Electronic Analog Circuits Using a Radial Basis Function Network Classifier,”Measurement, Vol. 28, pp. 147–158.
Chen, S., Cowan, C. F. N. and Grant, P. M., 1991, “Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks,”IEEE Trans. on Bio. Eng., Vol. 2, pp. 302–309.
Fan, C., Jin, Z., Zhang, J. and Tian, W., 2002, “Application of Multisensor Data Fusion Based on RBF Neural Networks for Fault Diagnosis of SAMs,”Seventh International Conference on Control, Automation, Robotics and Vision, Vol. 3, pp. 1557–1562.
Geng, Z. and Qu, L. 1994, “Vibrational Diagnosis of Machine Parts Using the Wavelet Packet Technique,”British Journal of Non-Destructive Testing, Vol. 36, No. 1, pp. 11–15.
Haykin, S. S., 1999, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice Hall, Upper Saddle River, N.J., USA.
Huang, C. and Shi, H. B., 2004, “Study on Chemical Process Faults Diagnosis Based on Fractal Geometry,”Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, China, Vol. 2, pp. 1658–1662.
Lai, W., Tse, P. W., Zhang, G. and Shi, T., 2004, “Classification of Gear Faults Using Cumulates and the Radial Basis Function Network,”Mechanical Systems and Signal Processing, Vol. 18, No. 2, pp. 381–389.
Liu, B. 2005, “Selection of Wavelet Packet Basis for Rotating Machinery Fault Diagnosis,”Journal of Sound and Vibration, Vol. 284, pp. 567–582.
Lu, S., Chen, W. and Li, M., 2006, “Fault Pattern Recognition of Rolling Bearing Based on Wavelet Packet and Support Vector Machine,”The Sixth World Congress on Intelligent Control and Automation, Vol. 2, pp. 5516–5520.
Lyon, R. H., 1987, Machinery Noise And Diagnostics, Massachusetts Institute of Technology, Butterworth Publishers, USA.
Mallat, S., 1998, “A Wavelet Tour of Signal Processing,”Academic Press, San Diego, CA,USA.
Mandelbrot, B. B., 1977, Fractals: from, Chance and Dimension, Freeman, San Francisco, CA, New York.
Mandelbrot, B. B., 1983, “The Fractal Geometry of Nature,” Freeman, San Francisco, CA, New York.
McCauley, J. L., 1993, “Chaos, Dynamics, and Fractals: An Algorithmic Approach to Deterministic Chaos, Cambridge Nonlinear Science Series 2,USA
Popescu, D. C., Dimca, A. and Yan, H., 1997, “A Nonlinear Model for Fractal Image Coding,”IEEE Transactions on Image Processing, Vol. 6, No. 3, pp. 373–382.
Tandon, N. and Choudhury, A., 1999, “A Review of Vibration and Acoustic Measurement Methods for The Detection of Defects in Rolling Element Bearings,”Tribology International, Vol. 32, No. 8, pp. 469–480.
Wang, W. J., 2001, “Wavelets for Detecting Mechanical Faults with High Sensitivity,”Mechanical Systems and Signal Processing, Vol. 15, No. 4, pp. 685–696.
Zhang, H., Wang, S. J. and Zhang, Q. S., 2003, “The Research on Rolling Element Bearing Fault Diagnosis Based on Wavelet Packets Transform,”Industrial Electronics Society, The 29th Annual Conference of the IEEE, Vol. 2, pp. 1745–1749.
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Chen, CH., Shyu, RJ. & Ma, CK. Rotating machinery diagnosis using wavelet packets-fractal technology and neural networks. J Mech Sci Technol 21, 1058–1065 (2007). https://doi.org/10.1007/BF03027655
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DOI: https://doi.org/10.1007/BF03027655