Degradation assessment of bearing fault using SOM network

Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011

Volumn 1, Issue , 2011, Pages 561-565

  Hu, Jinqiu ^a   Zhang, Laibin ^a   Liang, Wei ^a  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

Author keywords

bearing fault; degradation assessment; fault classification; SOM; wavelet analysis

Indexed keywords

BEARING FAULT; DEGRADATION ASSESSMENT; DEGRADATION BEHAVIOR; FAULT CHARACTERISTICS; FAULT CLASSIFICATION; FAULT CONDITIONS; FAULT DIAMETER; FAULT MAPPING; LEVEL OF SAFETIES; OUTER RACES; ROLLING ELEMENT BEARING; ROTATING MACHINE; SOM; SOM NETWORKS;

CONDITION MONITORING; RATING; ROTATING MACHINERY; ROTATION; WAVELET ANALYSIS;

DEGRADATION;

EID: 80053395604     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNC.2011.6021914     Document Type: Conference Paper

References (14)

1. Kankar P K, Sharma S C, and Harsha S P. Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 2011, 38: 1876-1886.
2. Kankar P K, Sharma S C, and Harsha S P. Fault diagnosis of ball bearings using continuous wavelet transform. Appl. Soft Comput. J. (2010), doi:10.1016/j.asoc.2010.08.011.
3. Purushotham V, Narayanan S and Prasad S A N. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT & E International, 2005, 38(8): 654-664. (Pubitemid 41261408)
4. Samant B, Al-Balushi K R and Al-Araimi S A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, 2003, 16(7-8): 657-665.
5. Yang Y, Yu D and Cheng J. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement, 2007, 40(9-10): 943-950. (Pubitemid 47503473)
6. Rojas A and Nandi A K. Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines. Mechanical Systems and Signal Processing, 2006, 20(7): 1523-1536.
7. Hong H and Liang M. Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform. Journal of Sound and Vibration, 2009, 320(1-2): 452-468.
8. Qiu H, Lee J and Yu G. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics, 2003, 17: 127-140.
9. Yang B S, Hwang W W, Kim D J, et al. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines. Mechanical Systems and Signal Processing, 2005, 19(2): 371-390.
10. Li T S and Huang C L. Defect spatial pattern recognition using a hybrid SOM-SVM approach in semiconductor manufacturing. Expert Systems with Applications, 2009, 36(1): 374-385.
11. Kohonen T. Self-organizing neural projections. Neural Networks, 2006, 19(6-7): 723-733. (Pubitemid 44148941)
12. http://www.eecs.case.edu/laboratory/bearing/download.htm
13. Kangas J and Kohonen T. Developments and applications of the self-organizing map and related algorithms. Mathematics and Computers in Simulation. 1996, 41(1-2): 3-12. (Pubitemid 126411642)
14. http://www.cis.hut.fi/projects/somtoolbox/.