International Journal on Advanced Science, Engineering and Information Technology, Vol. 8 (2018) No. 4-2: Special Issue on Empowering the Nation via 4IR (The Fourth Industrial Revolution)., pages: 1480-1485, Chief Editor: Khairuddin Omar | Editorial Boards : Shahnorbanun Sahran Hassan, Nor Samsiah Sani, Heuiseok Lim & Danial Hoosyar, DOI:10.18517/ijaseit.8.4-2.6805

Artificial Neural Network Classification for Fatigue Feature Extraction Parameters Based on Road Surface Response

Mohd Faridz Mohd Yunoh, Shahrum Abdullah, S. S. K. Singh


The aim of this paper is for classification for fatigue feature extraction parameters based on road surface response using the artificial neural network (ANN) technique. It is important for classification of the fatigue damage of automotive suspension as it is considers the random strain loading from the road surface contributed from complex variable amplitude loadings. In this study, the proposed method captured that strain signal collected from the car coil spring during the road test. Hence, the prediction of fatigue life need to assess based on actual loading to ensure the prediction results are accurate. The high amplitude segments were extracted from the strain signals using the discrete wavelet transform. This approach provides an advantage in assessing the signals containing random loads for both discontinuities and smooth components for the areas that contain high fatigue damage in the strain signal. From this, three significant fatigue features extraction parameters such as kurtosis, wavelet energy coefficients and fatigue damage were classified based on the similarities using ANN classification technique. This is important in analysing the clustering and classification were used in detection of fatigue damage according to the response of the various road surface condition. The results show the classifications using ANN give the accuracy based on the coefficient of determinant, R= 0.85 for all data. From on the accuracy of the ANN, it can be concluded that the discrete wavelet transform as a pre-processing method to extract the features from the signal for classification level of fatigue damage for the coil spring according to the response of loading based on road surface.


Artificial neural network; Fatigue damage; Features extraction; Road surface; Wavelet transform.

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