Rolling Bearings Fault Diagnosis Based on Adaptive Gaussian Chirplet Spectrogram and Independent Component Analysis
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.
KeywordsIndependent Component Analysis Fault Diagnosis Vibration Signal Independent Component Analysis Blind Source Separation
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- 6.Hyvärinen, A.: Survey on independent component analysis. Neura Computing Surveys 2, 94–128 (1999)Google Scholar
- 8.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)Google Scholar
- 11.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)Google Scholar
- 14.Gabor, D.: Theory of communication. J. Inst. Elect. Eng. 93, 429–457 (1946)Google Scholar
- 17.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)Google Scholar
- 20.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)Google Scholar