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.