A fault diagnosis method of rolling bearing through wear particle and vibration analyses

Applied Mechanics and Materials

Volumn 26-28, Issue , 2010, Pages 676-681

  Huang, Zhongyu ^a   Yu, Zhiqiang ^b   Li, Zhixiong ^c   Geng, Yuancheng ^a  

^a Hubei Three Gorges Vocational and Technical College   (China)

^b Wuhan Especial Equipment Supervise Test Institute   (China)

^c WUHAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

GAFNN; Rolling bearing; Vibration analysis; Wear debris

Indexed keywords

ANALYSIS RESULTS; CHEMICAL COMPOSITIONS; CLASSIFICATION RATES; COMPLEX NATURE; EXPERIMENTAL TEST; FAULT DIAGNOSIS; FAULT DIAGNOSIS METHOD; FREQUENCY CHARACTERISTIC; GAFNN; INTELLIGENT DATA; MACHINERY MAINTENANCE; MAINTENANCE PROGRAMS; MONITORING TECHNIQUES; OIL SAMPLES; PARTICLE TEXTURE; ROLLING BEARING; ROLLING BEARINGS; TYPICAL FAULTS; VIBRATION DATA; WEAR DEBRIS; WEAR PARTICLES;

BEARINGS (MACHINE PARTS); CHEMICAL ANALYSIS; CONDITION MONITORING; DATA FUSION; DEBRIS; DIESEL ENGINES; FLIGHT DYNAMICS; FUZZY NEURAL NETWORKS; MACHINERY; MAINTENANCE; PARTICLE SIZE ANALYSIS;

VIBRATION ANALYSIS;

EID: 78650888636     PISSN: 16609336     EISSN: 16627482     Source Type: Book Series    
DOI: 10.4028/www.scientific.net/AMM.26-28.676     Document Type: Conference Paper

References (14)

1. Z. Peng, N. J. Kessissoglou, and M. Cox: A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques, Wear, Vol. 258 (2005), p. 1651-1662.
2. Z. Peng and N. Kessissoglou: An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis, Wear, Vol. 255 (2003) 1221-1232.
3. J. Gao, P. Zhang, B. Liu, et al.: An Integrated Fault Diagnosis Method of Gearboxes Using Oil Analysis and Vibration Analysis, in: Proceedings of the 8th International Conference on Electronic Measurement and Instruments, 2007, Xian, CHINA, p. 3371-3374.
4. H. Yu, Q. Guo, J. Hu, et al.: Hybrid PSO based wavelet neural networks for intelligent fault diagnosis. Springer LNCS, Vol. 4221 (2007), p. 321-330.
5. Z. Kiral and H. Karagülle: Vibration analysis of rolling element bearings with various defects under the action of an unbalanced force, Mechanical Systems and Signal Processing, Vol. 20 (2006), p. 1967-1991.
6. Y. Yu, D. Yu and J. Cheng: A roller bearing fault diagnosis method based on EMD energy entropy and ANN, Journal of sound and vibration, Vol. 294 (2006), p. 269-277.
7. Z. Peng, P. Tse and F.Chu: A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing, Mechanical Systems and Signal Processing, Vol. 19 (2005), p. 974-988.
8. T. Chee, I. Phil, and M. David: A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears, Mechanical Systems and Signal Processing, Vol. 21 (2007), p. 208-233.
9. J. Mathew and J. S. Stecki: Comparison of vibration and direct reading ferrographic techniques in application to high-speed gears operating under steady and varying load conditions, Journal of the Society of Tribologists and Lubrication Engineers, Vol. 43 (1987), p. 646-653.
10. H. Maxwell and B. Johnson: Vibration and lube oil analysis in an integrated predictive maintenance program, in: Proceedings of the 21st Annual Meeting of the Vibration Institute, 1997, USA, p. 117-124.
11. T. Akagaki, M. Nakamur, T. Monzen, and M. Kawabata: Analysis of the behaviour of rolling bearings in contaminated oil using some condition monitoring techniques, Proc. IMechE Part J: J. Engineering Tribology, Vol. 220 (2006), p. 447-453.
12. M. M. Maru, R. S. Castillo, and L. R. Padovese: Study of solid contamination in ball bearings through vibration and wear analyses, Tribology International, Vol. 40 (2007), p. 433-440.
13. H. Ishigami, T. Fukuda, T. Shibata, and F. Arai: Structure optimization of fuzzy neural network by genetic algorithm, Fuzzy Sets and Systems, Vol. 71 (1995), p. 257-264.
14. H. Kuo and H. Chang: A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems, Engineering Applications of Artificial Intelligence, Vol. 17 (2004), p. 919-930.