Signal representation using adaptive normalized Gaussian functions
Signal Processing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Self-Organizing Maps
A self-constructing compensatory fuzzy wavelet network and its applications
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Hybrid PSO based wavelet neural networks for intelligent fault diagnosis
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
The chirplet transform: physical considerations
IEEE Transactions on Signal Processing
A fast refinement for adaptive Gaussian chirplet decomposition
IEEE Transactions on Signal Processing
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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.