Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach

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Authors

  • G. Manjunatha
     Department of Mechanical Engineering, UVCE, Bangalore University, Bengaluru – 560001, Karnataka ,IN
  • H. C. Chittappa
     Department of Mechanical Engineering, UVCE, Bangalore University, Bengaluru – 560001, Karnataka ,IN

DOI:

https://doi.org/10.18311/jmmf/2022/30060

Keywords:

Bearing Fault Detection, Empirical Mode Decomposition, K-star, Rolling Element, Vibration Signal

Abstract

Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eight features in time domain were calculated for various bearing conditions like healthy, rolling element fault, outer and inner race fault. The feature extraction of vibration signal based on Empirical Mode Decomposition (EMD) is extensively explored and applied in diagnosis of fault in rolling bearings. This paper presents mathematical analysis for selection of valid Intrinsic Mode Functions (IMFs) of EMD. These chosen features are trained and classified using different classifiers. Among them K-star classifier is most reliable to categorize the bearing defects.

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Published

2022-06-20

How to Cite

Manjunatha, G., & Chittappa, H. C. (2022). Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach. Journal of Mines, Metals and Fuels, 70(4), 214–218. https://doi.org/10.18311/jmmf/2022/30060

Issue

Volume 70, Issue 4, April 2022

Section

Articles
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References

Tandon, N., & Choudhury. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(4): 69–480. https://doi.org/10.1016/S0301-679X(99)00077-8

Patil, M.S., Mathew, J., & Rajendrakumar, P.K. (2008). Bearing signature analysis as a medium for fault detection: A review. Journal of Tribology, 130: 014001- 1–014001-7.https://doi.org/10.1115/1.2805445

Taylor, J. (2003). The vibration analysis handbook. Vibration consultants, Tampa, FL.

Scheffer, C., & Girdhar, P. (2004). Practical machinery vibration analysis and predictive maintenance. Newnes. https://doi.org/10.1016/B978-075066275-8/50001-1

Peng, Z.K., Tse, P.W., & Chu, F.L. (2005). A comparison study of improved Hilbert–Huang transform and wavelet transform application to fault diagnosis for rolling bearing, Mechanical Systems and Signal Processing, 19: 974–988. https://doi.org/10.1016/j.ymssp.2004.01.006

Su, W.S., Wang, F.T., Zhu, H., Zhang, Z.X., & Guo, Z.G. (2010). Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement, Mechanical Systems and Signal Processing, 24: 1458–1472. https://doi.org/10.1016/j.ymssp.2009.11.011

Rafiee, J., Rafiee, M.A., Tse, P.W. (2010). Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications, 37: 4568–4579. https://doi.org/10.1016/j.eswa.2009.12.051

Feng, K., Jiang, Z.N., He, W., & Qin, Q. (2011). Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet. Measurement, 44: 1582–1591. https://doi.org/10.1016/j.measurement. 2011.06.011

Sunnersjo, C.S. (1978). Varying compliance vibrations of rolling bearings. Journal of Sound and Vibration, 58: 363–373. https://doi.org/10.1016/S0022-460X(78)80044-3

Sunnersjo, C.S. (1985). Rolling bearing vibrations-geometrical imperfections and wear. Journal of Sound and Vibration, 98: 455–474. https://doi.org/10.1016/0022-460X(85)90256-1

Huang, N.E., Shen, Z., Long, S.R., Wu, M.L.C, Shi, H.H., Zheng, Q.N., Yen, N.C., Tung, C.C., & Liu, H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis, Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 454: 903– 995. https://doi.org/10.1098/rspa.1998.0193

Cleary, J.G., & Trigg, L.E.K. (1995). An instancebased learner using an entropic distance measure. In Proceedings of the 12th International Conference on Machine learning, 5: 108–114. https://doi.org/10.1016/ B978-1-55860-377-6.50022-0

Jegadeeshwaran, R., & Sugumaran, V. (2014). Vibration based fault diagnosis study of an automobile brake system using K-Star (K*) algorithm A statistical approach, IV: 44–56. https://doi.org/10.2174/2210686304666140919011156