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|Title:||A study on automatic machine condition monitoring and fault diagnosis for bearing and unbalanced rotor faults|
|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|
|Appears in Collections:||Staff Publications|
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