Selection of Mother Wavelet for Effective Wavelet Transform of Bearing Vibration Signals

Abstract:

Condition monitoring (CM) and fault diagnosis of equipments has gained greater attention in recent years, due to the need to reduce the down time and enhance the life/ condition of the equipments. The rolling element bearings (REB) are the most critical components in rotary machines. Hence, bearing fault detection and diagnosis is an integral part of the preventive maintenance activity. Vibration signal analysis provides wide range of information for analysis. So in this paper, vibration signals for four conditions of a deep groove ball bearing namely Normal (N), bearing with defect on inner race (IR), bearing with defect on ball (B), and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under maximum speed and variable load conditions. Depending on the machinery operating conditions and the extent of bearing defect severity, the measured vibration signals are non-stationary in nature. Non-stationary signals are effectively analyzed by wavelet transform technique, which is a popular and widely used time-frequency technique. The focus of this paper is to select a best possible mother wavelet for applying WT on bearing vibration signals. The two selection criteria includes minimum Shannon entropy criteria (MSEC) and Maximum Energy to Shannon Entropy Ratio criteria R(s). This helps in effective bearing CM using WT.

• Periodical: Advanced Materials Research (Volume 1039)
• Pages: 169-176
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1039.169
• Online since: October 2014
• Authors: H.S. Kumar*, P. Srinivasa Pai, N.S. Sriram, G.S. Vijay
• Keywords: Mother Wavelet, Rolling Element Bearing, Shannon Entropy, Time-Frequency Analysis, Wavelet Transform (WT)

References

[1] N. G. Nikolaou & I. A. Antoniadis, Rolling Element Bearing Fault Diagnosis using Wavelet Packets, NDT & E International 35 (2002) 197-205.

DOI: 10.1016/s0963-8695(01)00044-5

[2] H. S. Kumar, P. Srinivasa Pai, N. S. Sriram, G. S. Vijay., ANN based evaluation of performance of wavelet transform for condition monitoring of rolling element bearing , Procedia Engineering 64 (2013) 806-814.

DOI: 10.1016/j.proeng.2013.09.156

[3] M. S. Patil, Jose Mathew, P. K. RajendraKumar., Bearing Signature Analysis as a Medium for Fault Detection: A Review. Journal of Tribology, 130 (2008) 014001-1- 014001-7.

DOI: 10.1115/1.2805445

[4] R. Yan, Base wavelet selection criteria for non-stationary vibration analysis in bearing health diagnosis, Electronic Doctoral Dissertations for UMass Amherst, (2007).

[5] B. Sreejith, A. K. Vermaand and A. Srividya, Fault diagnosis of rolling element bearing using time-domain features and neural networks, IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA (2008) 1-6.

DOI: 10.1109/iciinfs.2008.4798444

[6] H.S. Kumar, P. Srinivasa Pai, N. S. Sriram, Vijay G. S., Rolling Element Bearing Condition Classification Using Hoelder Exponents, Annual Research Journal, NMAM Institute of Technology, 3 (2013), 43-48.

[7] Z.K. Peng, F.L. Chu, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing, 18, (2004) 199–221.

DOI: 10.1016/s0888-3270(03)00075-x

[8] W.K. Ngui, M.S. Leong, L. M. Hee, and A. M. Abdelrhman, Wavelet analysis: mother wavelet selection methods, Applied mechanics and materials, (2013) 953-958.

DOI: 10.4028/www.scientific.net/amm.393.953

[9] G. G. Yen and K. C. Lin, Wavelet Packet Feature Extraction for Vibration Monitoring, IEEE Transactions on Industrial Electronics, 47, (2000) 650–667.

DOI: 10.1109/41.847906

[10] Ning Fang, P Srinivasa Pai, Nathan Edwards, Tool edge wear and wavelet packet transform analysis in high speed machining of Inconel 718. Strojniški vestnik - Journal of Mechanical Engineering 58, 3, (2012) 191-202.

DOI: 10.5545/sv-jme.2011.063

[11] F. Al- Badour, M. Sunar, L. Cheded, Vibration Analysis of Rotating Machinery Using Time-frequency Analysis and Wavelet techniques, Mechanical Systems and Signals, (2011) 1-34.

DOI: 10.1016/j.ymssp.2011.01.017

[12] Vijay G S, Kumar H. S., P. Srinivasa Pai, Sriram N. S. and Raj B. K. N. Rao, Evaluation of effectiveness of wavelet based denoising schemes using ANN and SVM for bearing condition classification, Hindawi Publishing Corporation, Computational Intelligence and Neuroscience, ( 2012) doi10. 1155/2012/582453.

DOI: 10.1155/2012/582453

[13] P. Li, F. Kong, Q. He, Y. Liu, Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis, Measurement, 46 (2013), 497-505.

DOI: 10.1016/j.measurement.2012.08.007

[14] B. Vishwash, P. Srinivasa Pai, Rounaq Ahmed, Kumar, H. S. N.S. Sriram and G.S. Vijay, Multiscale slope feature extraction for gear and bearing fault diagnosis using wavelet transform, Proceedings of International Conference on Advances in Manufacturing and Materials Engineering, ICAMME 2014, NITK, Surathkal.

DOI: 10.1016/j.mspro.2014.07.353

[15] R. X. Gao, R. Yan, Wavelets: Theory and applications for manufacturing, Springer, (2011).

[16] P. K. Kankar, Satish C. Sharma, S. P. Harsha, Fault diagnosis of ball bearings using continuous wavelet transform, Applied Soft Computing. 11, (2011), 2300-2312.

DOI: 10.1016/j.asoc.2010.08.011

[17] Case Western Reserve University bearing data center website, Available online: http: /www. eecs. case. edu/laboratory/bearing/welcome_overview. htm (accessed on 11th Feb. 2014).