Using Monet Wavelet Coefficients to Cluster Variable Amplitude Fatigue Features

This paper presents clustering of fatigue features resulted from the segmentation of the SAESUS time series data. The segmentation process was based on the Monet wavelet coefficient amplitude level which produces 49 segments that each has an overall fatigue damage. Observation of the fatigue damage and the wavelet coefficients was made on each segment. In the end of the process, the segments were clustered into three clusters in order to identify any improvements in the data scattering for fatigue data clustering prospects. This algorithm produced a more reliable and suitable method of segment by segment analysis for fatigue strain signal segmentation. According to the findings, the higher Monet wavelet coefficient presented damaging segment, otherwise, it was undamaging segment. It indicated that the relationship between the Morlet wavelet coefficient and the fatigue damage was strong and parallel.