Using spectral kurtosis for selection of the frequency bandwidth containing the fault signature in rolling bearings
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ORIGINAL PAPER
Using spectral kurtosis for selection of the frequency bandwidth containing the fault signature in rolling bearings E. J. Osorio Santander1,2 · S. F. Silva Neto1 · L. A. Vaz1 · U. A. Monteiro1 Received: 16 December 2018 / Accepted: 1 August 2020 © Sociedade Brasileira de Engenharia Naval 2020
Abstract In the naval and offshore industry, many rotating equipment use rolling bearings and any fault can cause a high impact on the costs related to a possible equipment failure, which are far superior to the cost of replacing the bearing itself. Several reasons can shorten the life of the bearings and lead them to sudden breaks. In this context, the use of fault detection techniques in rolling bearings is extremely useful. The objective of this work is to apply the spectral kurtosis technique to select the frequency bandwidth, which contains the fault signature, for fault detection in three (3) rolling bearings. Bearing # 1 has fault in the cage, bearing # 2 has a fault in the outer race and bearing # 3 has two faults, one in the rolling element (ball) and another in the cage. Based on the difference found between the theoretical fault frequencies and the fault frequencies obtained experimentally, all faults were correctly identified. This difference does not exceed 1.83% in any case. Keywords Vibration · Rolling bearing · Fault identification · Spectral kurtosis Abbreviations α Contact angle (radial) 𝜇(n) Error signal for the autoregressive (AR) model v0 (n) Noise signal a(k) Prediction coefficients d(n) Random vibration signal Dp Rolling elements diameter Db Primitive rolling bearing diameter e(n) Error (residual) signal E(f ) Fourier transform of the error (residual) signal fBPFI Characteristic defect frequency for the inner ring * U. A. Monteiro [email protected] E. J. Osorio Santander [email protected] S. F. Silva Neto [email protected] L. A. Vaz [email protected] 1
Ocean Engineering Program (PENO), Centro de Tecnologia, Federal University of Rio de Janeiro (UFRJ), Bloco I‑108, Cidade Universitária, Ilha Do Fundão, Rio de Janeiro, RJ CEP 21945‑970, Brazil
EST‑UEA, Escola Superior de Tecnologia, Universidade Do Estado Do Amazonas, Av. Darcy Vargas 1200, Parque Dez, Manaus 69050‑020, Brazil
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fBPFO Characteristic defect frequency for the outer ring fFTF Characteristic defect frequency for the cage fBSF Characteristic defect frequency for the balls g(t) Impulse responses H (t, f) Envelope of the Short-Time Fourier Transform K(f ) Spectral kurtosis. M(f ) Fourier transform of 𝜇(n) nb Number of rolling elements p(k) Cross-correlation s(n) Vibration signal without noise v1 (n) Reference signal w ̂ k (n) Adaptive filter coefficients W(n) Time window X(t, f ) Short-time Fourier transform of x(t) y(n) Output signal for the ANC filter z(n) Output signal for the autoregressive (AR) model
1 Introduction In the naval and offshore industry, many rotating equipment use rolling bearings and any fault can cause a high impact on the costs related to a possib
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