Abstract
The fault feature of rolling element bearings in early failure period is so weak and susceptible to random noise that it is very difficult to be extracted, so combined adaptive local iterative filtering decomposition (ALIFD) with Teager–Kaiser energy operator (TKEO) for rolling element bearings fault diagnosis. This experiment provides access to bearing test data for faulty bearings, and acceleration data was measured at locations near the bearings. The results show that this method can be used to accurately extract different frequency components of bearing fault vibration signals and diagnose bearing fault location.
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References
X.F. Liu, L. Bo, S.R. Qin, Rotating speed based variable window STFT. J. Vib. Shock 29(4), 27–29 (2010)
B. Li, P.L. Zhang, S.S. Mi et al., An adaptive morphological gradient lifting wavelet for detecting bearing defects. Mech. Syst. Signal Process. 29(9), 415–427 (2012)
L. Zhao, J.Z. Xia, H. Wang et al., Application of empirical mode decomposition in rolling bearing fault diagnosis. J. Mil. Transp. Univ. 18(9), 49–53 (2016)
H.H. Giv, Directional short-time Fourier transform. J. Math. Anal. Appl. 399(1), 100–107 (2013)
J.L. Chen, Z.P. Li, J. Pan et al., Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 70–71, 1–35 (2016)
Y. Lv, R. Yuan, G.B. Song, Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing. Mech. Syst. Signal Process. 81, 219–234 (2016)
L. Lin, Y. Wang, H.M. Zhou, Iterative filtering as an alternative algorithm for empirical mode decomposition. Adv. Adapt. Data Anal. 1(4), 543–560 (2009)
A. Cicona, J.F. Liu, H.M. Zhou, Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis. Appl. Comput. Harmon. Anal. 41(2), 384–411 (2016)
P.H. Rodriguez, J.B. Alonso, M.A. Ferror et al., Application of the Teager–Kaiser energy operator in bearing fault diagnosis. ISA Trans. 52, 278–284 (2013)
V.T. Tran, F. AlThobiani, A. Ball, An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Syst. Appl. 41, 4113–4122 (2014)
X.L. An, C.S. Li, F. Zhang, Application of adaptive local iterative filtering and approximate entropy to vibration signal denoising of hydropower unit. J. Vibroeng. 18(7), 4299–4311 (2016)
M. Liang, I. Soltani, An energy operator approach to joint application of amplitude and frequency demodulations for bearing fault detection. Mech. Syst. Signal Process. 24, 1473–1494 (2010)
The Case Western Reserve University Bearing Data Center Website [EB/OL]. http://www.eecs.case.edu/laboratory/bearing/. 12 May 2013
W.A. Smith, R.B. Randall, Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech. Syst. Signal Process. 64, 100–131 (2015)
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Zhao, L., Zhang, Y. & Zhu, D. Rolling Element Bearing Fault Diagnosis Based on Adaptive Local Iterative Filtering Decomposition and Teager–Kaiser Energy Operator. J Fail. Anal. and Preven. 19, 1018–1022 (2019). https://doi.org/10.1007/s11668-019-00723-w
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DOI: https://doi.org/10.1007/s11668-019-00723-w