- Title
- Multi-size-window spectral augmentation: Neural network bearing fault classifier
- Creator
- Amar, Muhammad; Gondal, Iqbal; Wilson, Campbell
- Date
- 2013
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161433
- Identifier
- vital:12443
- Identifier
-
https://doi.org/10.1109/ICIEA.2013.6566377
- Abstract
- Features extraction has always been crucial in rotary machines for Condition based machine health monitoring. Time-domain-segmentation being among the preliminary steps for further classification process plays a momentous role. Vibration signals from bearing are quasistationary in nature therefore calculation of constituent frequencies amplitudes in the vibration signal is dependent upon time-segmentation-window size. The proposed research confers the effects of time-segmentation window size on spectral features amplitudes calculation and its impacts on classification accuracy of the Artificial Neural Network (ANN). Using multi-size time-segmentation-window, for comprehensive spectral features calculation, ANN pattern classifier has been trained for enhanced classification. ANN learning assigns importance based relative weights to the links using supervised learning. Experimental results have shown that multi-size-window spectral features for ANN fault classifier perform efficiently for quasi-stationary bearing vibrations.
- Publisher
- IEEE
- Relation
- 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) p. 261-266
- Rights
- © 2013 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0910 Manufacturing Engineering; 0913 Mechanical Engineering; Machine health monitoring; Neural network; Spectral contents; Fault diagnosis; Bearing faults
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