Abstract
Condition monitoring of rolling element bearings through the use of vibration analysis is an established technique for detecting early stages of component degradation. The location dependent characteristic defect frequencies make it possible to detect the presence of a defect and to diagnose on what part of the bearing the defect is. The difficulty of localized defect detection lies in the fact that the energy of the signature of a defective bearing is spread across a wide frequency band and hence can be easily buried by noise. To solve this problem, the adaptive Gaussian chirplet distribution for an integrated time-frequency signature extraction of the machine vibration is developed; the method offers the advantage of good localization of the vibration signal energy in the time-frequency domain. Independent component analysis (ICA) is used for the redundancy reduction and feature extraction in the time-frequency domain, and the self-organizing map (SOM) was employed to identify the faults of the rolling element bearings. Experimental results show that the proposed method is very effective.
Foundation item: Project supported by the Shenyang High-Tech. R&D Program, China (Grant No. 1053084-2-02).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yu, H.B., Guo, Q.J., Xu, A.D.: A self-constructing compensatory fuzzy wavelet network and Its applications. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3613, pp. 743–755. Springer, Heidelberg (2005)
Guo, Q.J., Yu, H.B., Xu, A.D.: Hybrid PSO based wavelet neural networks for intelligent fault diagnosis. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 521–530. Springer, Heidelberg (2005)
Bastiaans, M.J.: Gabor’expansion of a signal into Gaussian elementary signals. Proceedings of the IEEE 68, 538–539 (1980)
Cohen, L.: Time-frequency distribution—a review. Proc. IEEE. 77, 941–981 (1989)
Comon, P.: Independent component analysis—A new concept? Signal Processing 36, 287–314 (1994)
Hyvärinen, A.: Survey on independent component analysis. Neura Computing Surveys 2, 94–128 (1999)
Jutten, C., Herault, J.: Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24, 1–10 (1991)
Hurri, J., Hyvärinen, A., et al.: Image feature extraction using independent component analysis. In: IEEE Nordic Signal Processing Symp., NORSIG 1996, pp. 475–478 (1996)
Amari, S., Cichocki, A.: Adaptive blind signal processing—Neural network approaches. Proc. IEEE 86, 2026–2048 (1998)
Vigário, R.: Extraction of ocular artifacts from EEG using independent component analysis. Electroenceph. Clin. Neurophysiol. 103, 395–404 (1997)
Torkkola, K.: Blind separation for audio signals: Are we there yet? In: The Int. Workshop on Independent Component Analysis and Blind Separation of Signals (ICA 1999), Aussois, France, pp. 239–244 (1999)
Kohonen, T.: Self-Organizing Maps, 3rd edn., pp. 138–140. Springer, Berlin, Germany (2001)
Mann, Haykin, S.S.: The chirplet transform: Physical considerations. IEEE Trans. Signal Processing 43, 2461–2745 (1995)
Gabor, D.: Theory of communication. J. Inst. Elect. Eng. 93, 429–457 (1946)
Mann, S., Haykin, S.: ‘Chirplets’ and ‘warblets’: Novel timefrequency methods. Electron. Lett. 28, 114–116 (1992)
Mallat, S., Zhang, Z.: Matching pursuit with time-frequency dictionaries. IEEE Trans. Signal Processing 41, 3397–3415 (1993)
Qian, S., Chen, D.: Signal representation in adaptive Gaussian functions and adaptive spectrogram. In: Proc. Twenty-Seventh Annu. Conf. Inform. Sci. Syst., pp. 59–65 (1993)
Qian, S., Chen, D.: Signal representation using adaptive normalized Gaussian functions. Signal Process 36(1), 1–11 (1994)
Yin, Q., Qian, S., Feng, A.: A fast refinement for adaptive Gaussian chirplet decomposition. IEEE Trans. Signal Processing 50(6), 1298–1306 (2002)
Gadhok, N., Kinsner, W.: Estimating outlier impact on. In: FastICA using fuzzy inference, Conference of the North American Fuzzy Information Processing Society–NAFIPS, pp. 832–837 (2004)
Hyvärinen, A., Karhunen, A.J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001)
Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comput. 9(7), 1483–1492 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, H., Guo, Q., Hu, J., Xu, A. (2006). Rolling Bearings Fault Diagnosis Based on Adaptive Gaussian Chirplet Spectrogram and Independent Component Analysis. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_46
Download citation
DOI: https://doi.org/10.1007/11881070_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)