Fatigue Life Prediction of Heavy Vehicle Leaf Spring using Statistical Approach
The strain signal of an automotive leaf spring component is analysed for fatigue life prediction and characterisation. This study aims to investigate the behaviour of fatigue strain signal based on fatigue life relative to a probability distribution function and a global statistical parameter. The captured data were measured for 150 s at a sampling rate of 200 Hz. Fatigue life prediction was then calculated using three strainlife models: the CoffinManson, Morrow and SmithWatsonTopper models. In fatigue life prediction, the highway data achieve the lowest fatigue life value, i.e. 1.74 104, given that these data consistently have a high amplitude range. The behaviour of random data was then analysed using a statistical approach through a global statistical parameter and a probability distribution function. Result show that the kurtosis value of the highway is the highest, i.e. 4.28, and probably because a vehicle experiences high-speed acceleration that contributes to short fatigue life. The findings of this study can be used in pre processing fatigue strain signal to observe or investigate the behaviour of variable amplitude loading data.
Index Terms - Fatigue life, probability, statistics, strain signal.