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Sensors 2018, 18(4), 1203; https://doi.org/10.3390/s18041203

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform

School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, China
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Received: 21 March 2018 / Revised: 11 April 2018 / Accepted: 11 April 2018 / Published: 14 April 2018
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Abstract

When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures. View Full-Text
Keywords: improved Hilbert TT transform; Hilbert TT transform; principal component analysis; rolling bearing; fault diagnosis improved Hilbert TT transform; Hilbert TT transform; principal component analysis; rolling bearing; fault diagnosis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Pang, B.; Tang, G.; Tian, T.; Zhou, C. Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform. Sensors 2018, 18, 1203.

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