Comparison between shock pulse method and vibration analysis methods on early fault detection of rolling element bearing

Document Type : Research Article

Authors

1 Professor, School of Mech. Eng., Sharif University of Technology, Tehran, Iran

2 Ph.D. Candidate, Department of Mech. Eng., University of Zanjan, Zanjan, Iran

3 Assistant Professor, Department of Mech. Eng., University of Zanjan, Zanjan, Iran

4 Ph.D. Candidate, National Centre for Advanced Tribology, University of Southampton, Southampton, UK

Abstract

In this paper, the vibration analysis methods and shock pulse method (SPM) are compared in order to detect the unhealthy condition as well as fault type in the early stages of rolling element bearing (REB) degradation. To analyze vibration signals, three weak signature detection methods based on continuous wavelet transform (CWT), empirical mode decomposition (EMD) and envelope technique are employed. A set of accelerated life tests on REBs was designed and performed in CM lab of Sharif university of technology. Seven tests were conducted and vibration signals, as well as shock pulse signals, were recorded regularly. The trend of vibration level and shock pulse level are compared for early detection of the unhealthy condition in REBs. In addition, the extracted spectrums from SPM, CWT, EMD, and envelope techniques are studied to detect bearing characteristics frequencies (BCFs) to diagnostics. Results show that SPM has better performance on early fault detection of REBs rather than vibration analysis techniques.

Highlights

- REB vibration and shock pulse during degradation are studied and compared.

- A set of accelerated life tests are planned and employed.

- SPM is found having better performance in detection of unhealthy condition and fault type.

 

Keywords

Main Subjects


[1] Y. Lei, J. Lin, Z. He, Y. Zi, Application of an improved kurtogram method for fault diagnosis of rolling element bearings, Mechanical systems and signal processing, 25 (2011) 1738-1749.
[2] Y. Lei, Z. He, Y. Zi, Q. Hu, Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs, Mechanical systems and signal processing, 21 (2007) 2280-2294.
[3] N. Tandon, K. Kumar, Detection of defects at different locations in ball bearings by vibration and shock pulse monitoring, Noise & Vibration Worldwide, 34 (2003) 9-16.
[4] N. Tandon, G. Yadava, a.K. Ramakrishna, A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings, Mechanical systems and signal processing, 21 (2007) 244-256.
[5] R. Yang, J. Kang, Bearing fault detection of wind turbine using vibration and SPM, Vibroengineering Procedia, 10 (2016) 173-178.
[6] M. Behzad, A. Davoodabadi, H.A. Arghand, Prognostics of rolling element bearings using shock pulse method and vibration method records and employing feedforward neural-network, Amirkabir Journal of Mechanical Engineering, 53 (2021) 2557-2576.
[7] M. Behzad, A. Davoodabadi, H.A. Arghand, Using Shock Pulse Method for Early Fault Detection of Rolling Element Bearings and Comparing with Vibration Envelope Technique, Amirkabir Journal of Mechanical Engineering, (2019).
[8] Y. Wang, G. Xu, L. Liang, K. Jiang, Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis, Mechanical Systems and Signal Processing, 54 (2015) 259-276.
[9] X. Chiementin, F. Bolaers, O. Cousinard, L. Rasolofondraibe, Early detection of rolling bearing defect by demodulation of vibration signal using adapted wavelet, Journal of Vibration and Control, 14 (2008) 1675-1690.
[10] W.T. Peter, D. Wang, The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection: Part 2 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement—Parts 1 and 2”, Mechanical Systems and Signal Processing, 40 (2013) 520-544.
[11] F. Hemmati, W. Orfali, M.S. Gadala, Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation, Applied acoustics, 104 (2016) 101-118.
[12] P. Gupta, M. Pradhan, Fault detection analysis in rolling element bearing: A review, Materials Today: Proceedings, 4 (2017) 2085-2094.
[13] X. Gu, S. Yang, Y. Liu, F. Deng, B. Ren, Compound faults detection of the rolling element bearing based on the optimal complex Morlet wavelet filter, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 232 (2018) 1786-1801.
[14] A. Rohani Bastami, A. Aasi, H.A. Arghand, Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43 (2019) 233-245.
[15] R. Yan, R.X. Gao, Base wavelet selection for bearing vibration signal analysis, International Journal of Wavelets, Multiresolution and Information Processing, 7 (2009) 411-426.
[16] H. Qiu, J. Lee, J. Lin, G. Yu, Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Journal of sound and vibration, 289 (2006) 1066-1090.
[17] W. Su, F. Wang, H. Zhu, Z. Zhang, Z. Guo, Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement, Mechanical systems and signal processing, 24 (2010) 1458-1472.
[18] Y. Jiang, B. Tang, Y. Qin, W. Liu, Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD, Renewable energy, 36 (2011) 2146-2153.
[19] M. El Morsy, G. Achtenova, Application of optimal morlet wavelet filter for bearing fault diagnosis, SAE International Journal of Passenger Cars-Mechanical Systems, 8 (2015) 817-824.
[20] Y. Qin, J. Xing, Y. Mao, Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis, Measurement Science and Technology, 27 (2016) 085003.
[21] C. Junsheng, Y. Dejie, Y. Yu, The application of energy operator demodulation approach based on EMD in machinery fault diagnosis, Mechanical systems and signal processing, 21 (2007) 668-677.
[22] X. Fan, M.J. Zuo, Machine fault feature extraction based on intrinsic mode functions, Measurement Science and Technology, 19 (2008) 045105.
[23] H. Li, Y. Zhang, H. Zheng, Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings, Journal of mechanical science and technology, 23 (2009) 291.
[24] W.-C. Tsao, Y.-F. Li, M.-C. Pan, Resonant-frequency band choice for bearing fault diagnosis based on EMD and envelope analysis, in:  2010 8th World Congress on Intelligent Control and Automation, IEEE, 2010, pp. 1289-1294.
[25] X. Chiementin, B. Kilundu, J.-P. Dron, P. Dehombreux, K. Debray, Effect of cascade methods on vibration defects detection, Journal of Vibration and Control, 17 (2011) 567-577.
[26] G. Georgoulas, T. Loutas, C.D. Stylios, V. Kostopoulos, Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition, Mechanical Systems and Signal Processing, 41 (2013) 510-525.
[27] Y. Lei, J. Lin, Z. He, M.J. Zuo, A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mechanical systems and signal processing, 35 (2013) 108-126.
[28] J. DybaƂa, R. Zimroz, Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal, Applied Acoustics, 77 (2014) 195-203.
[29] J.B. Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello, F. Fnaiech, Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals, Applied Acoustics, 89 (2015) 16-27.
[30] M. Rezaee, A. Taraghi Osguei, Improving empirical mode decomposition for vibration signal analysis, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 231 (2017) 2223-2234.
[31] K. Yu, T.R. Lin, J.W. Tan, A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering, Applied Acoustics, 121 (2017) 33-45.
[33] N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454 (1998) 903-995.