Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISIE.2011.5984486
Title: A study on automatic machine condition monitoring and fault diagnosis for bearing and unbalanced rotor faults
Authors: Chen, W.-Y.
Xu, J.-X. 
Panda, S.K. 
Keywords: bearing fault
k-NN algorithm
normalized cross-correlation
unbalanced fault
Issue Date: 2011
Source: Chen, W.-Y.,Xu, J.-X.,Panda, S.K. (2011). A study on automatic machine condition monitoring and fault diagnosis for bearing and unbalanced rotor faults. Proceedings - ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics : 2105-2110. ScholarBank@NUS Repository. https://doi.org/10.1109/ISIE.2011.5984486
Abstract: this paper demonstrates a simple and effective data-based scheme for the continuous automatic condition monitoring and diagnosis of bearing and unbalanced rotor faults. The key idea is to use a normalized cross-correlation sum operator as similarity measure for the automatic classification of machine faults using the k-nearest neighbor (k-NN) algorithm. This technique is both noise tolerance and shift-invariance. The experiments showed an error rate of 0.74% is achieved over a wide range of machine operating speed from 15Hz to 32Hz. © 2011 IEEE.
Source Title: Proceedings - ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics
URI: http://scholarbank.nus.edu.sg/handle/10635/69092
ISBN: 9781424493128
DOI: 10.1109/ISIE.2011.5984486
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