Fatigue Data Editing Algorithm for Automotive Applications

This paper presents a wavelet based algorithm to summarise a long record of fatigue signal by extracting the bumps (fatigue damaging events) to produce a bump signal. With this algorithm the input signal is decomposed using the orthogonal wavelet transform and the wavelet levels are then grouped into characteristic frequency bands. Bumps are extracted from each wavelet group at a specific trigger level, which is set automatically according to the global signal statistics comparison between the original and bump signals. The accuracy of the algorithm has been evaluated by application to two experimentally measured data sets containing tensile and compressive preloading conditions. For both data sets, the bump signals length were at minimum of 40% of their respective original signals, and almost 90% original fatigue damage was retained in the bump signals, as calculated using the strain-life models of Smith-Watson-Topper and Morrow. Based on the results, this algorithm was found to be a suitable approach to summarise a long fatigue signal for the automotive usage