Fault Feature Extraction Method for Flexible Thin-Walled Bearings Based on MOMEDA-MCKD

Li, Weiguang; Yan, Song; Chen, Ru; Wu, Hao

doi:10.1145/3390557.3394291

Cited by 3 publications

(1 citation statement)

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“…However, most current fault feature extraction methods treat FTBs as ordinary rolling bearings for analysis, i.e. fault diagnosis is performed by analyzing the spectral characteristics of the fault signal, without taking into account the time-varying nature of the FTB [9,10].…”

Section: Introductionmentioning

confidence: 99%

TMSST-CK fault feature extraction method for flexible thin-wall bearing based on the Gini index principle

Yu¹,

Zhao²,

Yu³

2022

Meas. Sci. Technol.

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Flexible thin-wall bearing (FTB) is forced to deform with the elastic deformation of the flexspline in motion, resulting in a completely different fault characteristic frequency (FCF) from ordinary rolling bearings. However, current extraction methods treat FTB as an ordinary one and are analyzed based on spectrum, without considering the time-varying nature of FCF in FTB. In an effort to address this concern, this paper proposes a Gini index-based time-reassigned multisynchrosqueezing transform, where the window size is determined by the period impulses feature in the noise-removed signal. In addition, a frequency point selection method that contains fault characteristics of FTB is proposed by calculating the maximum correlated kurtosis in time-frequency plane. Furthermore, the instantaneous frequency is extracted by calculating the time interval between two consecutive impulses. The extracted fault frequency well agrees with the experimental result and outperforms the frequency obtained by other time-frequency analysis techniques.

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Section: Introductionmentioning

confidence: 99%