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This study aims to introduce a novel method for accelerating fatigue tests. Strain signals measured at automotive suspension components were extracted based on the Morlet wavelet producing damaging segments. Furthermore, the segments were clustered using the Fuzzy C-Means to remove the segments having lower energy. The process was able to shorten the strain signals up to 41.4% and it was able to retain...
This paper focuses on the analysis based on the clustering and the classification method of fatigue strain signals. Very few detailed studies have been carried out on the classification of fatigue damage, especially in the automotive field. Fatigue strain signals were observed on the coil springs of vehicles during road tests. The strain signals were then extracted using the Wavelet Transform approach...
Identifying damaging or non-damaging events in long records of fatigue data is a crux of recapitulating pristine data. In this article, a fuzzy double clustering framework (DCf) is utilized to classify the fatigue segment by exploiting two typical statistical features; kurtosis and the standard deviation. In the first stage, segments are assigned to a number of similar groups to generate multi-dimensional...
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