Time-series identification of fatigue strain data using decomposition method

This paper presents the time-series identification a variable-amplitude (VA) strain signal on lower arm suspension component in terms of time-series component analysing and correlate to the fatigue damage properties. The identification technique was used is called classical decomposition method, to classify the strain data into trend, cyclical, seasonal and irregular components. The time history plot of a study case showed the fatigue data contains high and low amplitude events and has resulted the highest amplitude for a pave, highway and campus are 224 μe, 321 μe and 619 μe, respectively. The trend pattern of a fatigue strain data is a nonstationary series in variance and mean, where a campus data produced highest slope of 31.2×10−4 compared to the others. By observing the cyclic movement of the moving average plot, the fatigue strain data contained expansion, contraction and random background. The autocorrelation plot is weak in identifying seasonal pattern, but the autocorrelation coefficient, r1 valu...