Fatigue time history analysis for determining the strain signal behaviour

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2 Citations (Scopus)

Abstract

This paper presents a fatigue time history analysis for determining the strain signal statistical behaviour using a wavelet and fatigue I-kaz approach. The ability of discrete wavelet transform (DWt) in the study is initiated by the high amplitude events identification and extraction based on wavelet coefficients and energy. Recently, there has been a motivation to explore a new approach by the authors, which led to the introduction of kurtosis-based analysis. An experiment has been performed on the car suspension system (coil spring), and the time history signals were collected based on the road surface in a residential area. Seven high amplitude segments, named H1-H7, were extracted based on DWT (Db4) analysis and analysed using fatigue I-kaz approach, which gave the higher values of the coefficients for DWT (Db4) and fatigue I-kaz at 2.02 × 1010 and 323.96. The discrete energy from the fatigue time history signal influenced the DWT energy coefficients and fatigue I-kaz coefficients.

Original languageEnglish
Pages (from-to)363-371
Number of pages9
JournalInternational Journal of Vehicle Systems Modelling and Testing
Volume9
Issue number3-4
Publication statusPublished - 1 Jan 2014

Fingerprint

Fatigue
Fatigue of materials
Coefficient
Energy
Discrete wavelet transforms
Kurtosis
Wavelet Coefficients
Coil
Wavelet Transform
History
Wavelets
Railroad cars
Experiment
Experiments

Keywords

  • Data scattering
  • Daubechies wavelet
  • Extraction
  • Fatigue I-kaz
  • Non-stationary

ASJC Scopus subject areas

  • Automotive Engineering
  • Modelling and Simulation

Cite this

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title = "Fatigue time history analysis for determining the strain signal behaviour",
abstract = "This paper presents a fatigue time history analysis for determining the strain signal statistical behaviour using a wavelet and fatigue I-kaz approach. The ability of discrete wavelet transform (DWt) in the study is initiated by the high amplitude events identification and extraction based on wavelet coefficients and energy. Recently, there has been a motivation to explore a new approach by the authors, which led to the introduction of kurtosis-based analysis. An experiment has been performed on the car suspension system (coil spring), and the time history signals were collected based on the road surface in a residential area. Seven high amplitude segments, named H1-H7, were extracted based on DWT (Db4) analysis and analysed using fatigue I-kaz approach, which gave the higher values of the coefficients for DWT (Db4) and fatigue I-kaz at 2.02 × 1010 and 323.96. The discrete energy from the fatigue time history signal influenced the DWT energy coefficients and fatigue I-kaz coefficients.",
keywords = "Data scattering, Daubechies wavelet, Extraction, Fatigue I-kaz, Non-stationary",
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AU - Yunoh, Mohd Faridz Mod

AU - Abdullah, Shahrum

AU - Mohd Nopiah, Zulkifli

AU - Nuawi, Mohd. Zaki

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N2 - This paper presents a fatigue time history analysis for determining the strain signal statistical behaviour using a wavelet and fatigue I-kaz approach. The ability of discrete wavelet transform (DWt) in the study is initiated by the high amplitude events identification and extraction based on wavelet coefficients and energy. Recently, there has been a motivation to explore a new approach by the authors, which led to the introduction of kurtosis-based analysis. An experiment has been performed on the car suspension system (coil spring), and the time history signals were collected based on the road surface in a residential area. Seven high amplitude segments, named H1-H7, were extracted based on DWT (Db4) analysis and analysed using fatigue I-kaz approach, which gave the higher values of the coefficients for DWT (Db4) and fatigue I-kaz at 2.02 × 1010 and 323.96. The discrete energy from the fatigue time history signal influenced the DWT energy coefficients and fatigue I-kaz coefficients.

AB - This paper presents a fatigue time history analysis for determining the strain signal statistical behaviour using a wavelet and fatigue I-kaz approach. The ability of discrete wavelet transform (DWt) in the study is initiated by the high amplitude events identification and extraction based on wavelet coefficients and energy. Recently, there has been a motivation to explore a new approach by the authors, which led to the introduction of kurtosis-based analysis. An experiment has been performed on the car suspension system (coil spring), and the time history signals were collected based on the road surface in a residential area. Seven high amplitude segments, named H1-H7, were extracted based on DWT (Db4) analysis and analysed using fatigue I-kaz approach, which gave the higher values of the coefficients for DWT (Db4) and fatigue I-kaz at 2.02 × 1010 and 323.96. The discrete energy from the fatigue time history signal influenced the DWT energy coefficients and fatigue I-kaz coefficients.

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