LAUSR

This study characterized the properties of random strain loading data for using power spectral density (PSD) in frequency domain of a heavy vehicle leaf spring. This is due to missing data caused by the sensitivity of the strain gauges in capturing strain signal. Strain signal was captured from a leaf spring component for 100 s at a sampling rate of 200 Hz using strain gauge. Fatigue life prediction was computed using strain-life models: Coffin-Manson, Morrow and Smith-Watson-Topper (SWT). The fatigue strain data showed that downhill data produces the lowest fatigue life prediction at 3.42 × 10 2 cycles/block with high energy of 3.6 × 10 4 μɛ 2 .Hz -1 ; then it was followed by curve and highway data. This was supported by the root-mean-square (RMS) value at 324.24 μɛ as it is directly related towards the PSD based on the energy contained for each captured signal. The correlation of fatigue life and strain amplitude was calculated to identify the distribution of fatigue strain data of leaf spring. Thus, the fatigue strain loading data can be characterized properly based on the energy content in PSD, the statistical parameter in the form of RMS value and the correlation with strain amplitude for random strain loading of leaf spring.