A Fault Diagnosis Method of Rolling Bearing through Wear Particle and Vibration Analyses

Abstract:

Wear particle and vibration analysis are the two main condition monitoring techniques for machinery maintenance and fault diagnosis in industry. Due to the complex nature of machinery, these two techniques can only diagnose about 30% to 40% of faults when used independently. Therefore, it is critical to integrate vibration analysis and wear particle analysis to provide a more effective maintenance program. This paper presents a new fault diagnosis approach of rolling bearings via the combination of vibration analysis and wear particle analysis. Both the tribological and vibrant information of the rolling bearings with typical faults were collected by an experimental test rig. Wear particle analysis was applied to the oil samples to obtain the wear particle number and size distribution, the particle texture and the chemical compositions, etc. Vibration analysis was used to get the time and frequency characteristics of the vibration data. Then, an intelligent data fusion method based on the genetic algorithm based fuzzy neural network was employed to identify the rolling bearing conditions. The analysis results suggest that the proposed method is more feasible and effective for the rolling bearing fault diagnosis than separated use of the two techniques with respect to the classification rate, and thus has application importance.

• Periodical: Applied Mechanics and Materials (Volumes 26-28)
• Pages: 676-681
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.26-28.676
• Online since: June 2010
• Authors: Zhong Yu Huang, Zhi Qiang Yu, Zhi Xiong Li, Yuan Cheng Geng
• Keywords: GAFNN, Rolling Bearing, Vibration Analysis (VA), Wear Debris

References

[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), pp.1651-1662.

DOI: 10.1016/j.wear.2004.11.020

[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.

DOI: 10.1016/s0043-1648(03)00098-x

[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, pp.3371-3374.

DOI: 10.1109/icemi.2007.4350932

[4] H. Yu, Q. Guo, J. Hu, et al.: Hybrid PSO based wavelet neural networks for intelligent fault diagnosis. Springer LNCS, Vol. 4221 (2007), pp.321-330.

[5] Z. Kıral 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).

DOI: 10.1016/j.ymssp.2005.05.001

[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), pp.269-277.

DOI: 10.1016/j.jsv.2005.11.002

[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), pp.974-988.

DOI: 10.1016/j.ymssp.2004.01.006

[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).

DOI: 10.1016/j.ymssp.2005.09.015

[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).

[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, pp.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), pp.447-453.

DOI: 10.1243/13506501j00605

[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), pp.433-440.

DOI: 10.1016/j.triboint.2006.04.007

[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), pp.257-264.

DOI: 10.1016/0165-0114(94)00283-d

[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), pp.919-930.

DOI: 10.1016/j.engappai.2004.08.009