A study on automatic machine condition monitoring and fault diagnosis for bearing and unbalanced rotor faults

Author

Chen, W.-Y. ; Xu, J.-X. ; Panda, S.K.

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore

fYear

2011

fDate

27-30 June 2011

Firstpage

2105

Lastpage

2110

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.

Keywords

condition monitoring; fault diagnosis; machine bearings; mathematical operators; mechanical engineering computing; pattern classification; rotors; automatic machine condition monitoring; bearings; fault classification; fault diagnosis; k-nearest neighbor algorithm; normalized cross correlation sum operator; rotors; Classification algorithms; Error analysis; Noise; Prediction algorithms; Rotors; Support vector machine classification; Vibrations; bearing fault; k-NN algorithm; normalized cross-correlation; unbalanced fault;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics (ISIE), 2011 IEEE International Symposium on

Conference_Location

Gdansk

ISSN

Pending

Print_ISBN

978-1-4244-9310-4

Electronic_ISBN

Pending

Type

conf

DOI

10.1109/ISIE.2011.5984486

Filename

5984486