Classification of Fatigue Damaging Segments Using Artificial Neural Network / M. F. M. Yunoh ...[et al.] - M. Yunoh, M. F.; Abdullah, S.; M. Saad, M. H.; M. Nopiah, Z.; Nuawi, M. Z.; Ariffin, A. (Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM), 2018)
This paper focuses on the classification of the fatigue damaging segments datasets associated with the measurement of Variable Amplitude Loadings of strain signals from the coil springs of an automobile during road tests. The wavelet transform was used to extract high damaging segments of the fatigue strain signals. The parameters of the kurtosis, wavelet-based coefficients, and fatigue damage were then calculated for every segment. All the parameters were used as input for the classification analysis using artificial neural networks. Using the back-propagation trained artificial neural network, the corresponding fatigue damages were classified. It was observed that the classification method was able to give 100% accuracy on the classifications based on the damaging segments that were extracted from the training and the validation datasets. From this approach, it classified the level of fatigue damage for coils spring.