The simulation study of three typical time frequency analysis methods

Li, Yifeng; Zhang, Lihui; Li, Baohui; Xu, Yan; Wu, Shih Chi; Wei, Xiucheng; Liu, Xiaoyan; Lin, Rong; Wang, Quan

doi:10.1051/bioconf/20170802007

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…The Hilbert spectrogram showed the processing effect of HHT on nonlinear and nonstationary signals and possessed a unique advantage. This method improves the time domain resolution as much as possible without sacrificing the frequency domain resolution (Li et al, 2017). The Hilbert spectrogram displayed the distribution of signal energy in time and frequency and helped to obtain a clear understanding of the distribution situation of the low-and high-frequency parts in the signal.…”

Section: Accelerating the Fatigue Analysismentioning

confidence: 99%

Accelerating the fatigue analysis based on strain signal using Hilbert–Huang transform

Nasir

Singh

Abdullah

et al. 2019

IJSI

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Purpose The purpose of this paper is to present the application of Hilbert–Huang transform (HHT) for fatigue damage feature characterisation in the time–frequency domain based on strain signals obtained from the automotive coil springs. Design/methodology/approach HHT was employed to detect the temporary changes in frequency characteristics of the vibration response of the signals. The extraction successfully reduced the length of the original signal to 40 per cent, whereas the fatigue damage was retained. The analysis process for this work is divided into three stages: signal characterisation with the application of fatigue data editing (FDE) for fatigue life assessment, empirical mode decomposition with Hilbert transform, an energy–time–frequency distribution analysis of each intrinsic mode function (IMF). Findings The edited signal had a time length of 72.5 s, which was 40 per cent lower than the original signal. Both signals were retained statistically with close mean, root-mean-square and kurtosis value. FDE improved the fatigue life, and the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential. HHT helped to remove unnecessary noise in the recorded signals. EMD produced sets of IMFs that indicated the differences between the original signal and mean of the signal to produce new components. The low-frequency energy was expected to cause large damage, whereas the high-frequency energy will cause small damage. Originality/value HHT and EMD can be used in the strain data signal analysis of the automotive component of a suspension system. This is to improve the fatigue life, where the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential.

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Section: Accelerating the Fatigue Analysismentioning

confidence: 99%