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

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

Abstract


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.


Keywords


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

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References


S. M. Dominguez and J. D. Sorensen, “Fatigue reliability and calibration of fatigue design factors for offshore wind turbines,†Energies, vol. 5, pp. 1816-1834, 2012.

Z. Dworakowski, L. Ambrozinski and P. Packo, “Application of artificial neural networks for compounding multiple damage indices in lamb-wave-based damage detection,†Struct. Control Hlth., vol. 22, pp. 50–61, 2015.

L. Witek, M. Sikora, F. Stachowicz, and T. Trzepiecinski, “Stress and failure analysis of the crankshaft of diesel engine,†Eng. Fail. Anal., vol. 82, pp. 703-712, 2017.

S. Suman, A. Kallmeyer and J. Smith, “development of a multilaxial fatigue damage parameter and life prediction methodology for non-proportional loadingâ€. Frattura Integr. Strutt., vol. 38, pp.224-230, 2016.

T. E. Putra, S. Abdullah, D. Schramm, M. Z. Nuawi, and T. Bruckmann, “Generating strain signals under consideration of road surface profiles†Mech. Syst. and Signal Process., vol. 60-6, pp. 485-497, 2015.

A. Ananthakrishnan, I. Kozinsky, and I. Bargatin, “Limits to inertial vibration power harvesting: power spectral density approach and its applications, “arXiv, vol. 2014, pp.1-20. 2014.

T. E. Putra, S. Abdullah, D. Schramm, M. Z. Nuawi, and T. Bruckmann, “Reducing cyclic testing time for components of automotive suspension system utilising the wavelet transform and fuzzy c-mean,†Mech. Syst. and Signal Process., vol. 90, pp. 1-14, 2017.

C. S. Oh, “Application of wavelet transform in fatigue history editing,†Int. J. Fatigue, vol.23 (3), pp. 241–250, 2001.

I. Abu-Mahfouz, and A. Banerjee, “Drill wear feature identification under varying cutting conditions using vibration and cutting force signals and data mining techniques,†Procedia Comput. Sci., vol.36, pp. 556-563. 2014.

J. A. Ghani, A. Rizal, M. Z. Nuawi, R. Ramli, B. Deros and C. H. C. Haron, “Online tool wear monitoring using portable assistant (PDA), Int. J. Phys. Sci., vol.6 (16), pp. 4064-4069, 2011.

S. A. Rizzi, M. N. Behnke and P. Przekop, “The effect of a non-gaussian random loading on high-cycle fatigue of a thermally post-buckled structure, structural dynamics: Recent Advancesâ€, Proc. of the 10th Int. Conf., pp. 1-16, 2010.

H. Gao, H. Z. Huang, S. P. Zhu, Y. F. Li, and R. Yuan, “A modified nonlinear damage accumulation model for fatigue life prediction considering load interaction effects,†Scientific World Journal, vol 2014, pp. 1-7, 2014.

R. Walid and E. Abdellah, “Assessment of fatigue behavior and effects of crack growth in Aluminium alloys 6082 under various stress ratios,†Int. J. on Adv. Sc. Eng. Info.Tech., vol. 6(5), pp. 582-587,2016.

H. El Kadi, Y. Al-Assaf, “Energy-based fatigue life prediction of fiberglass/epoxy composites using modular neural networks,†Compo. Struc. vol. 57, pp. 85-89, 2002.

M. Meng and M. Meng, “Spectrum recognition with three-stage neural networkâ€. 3rd Int. Conf. on Intelligent Networks and Intelligent Systems (ICINIS), Shenyang, China.2010.

R.K. Paredes, A.M. Sison and R.P. Medina, “Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis†Int. J. on Adv. Sc. Eng. Info.Tech., vol.8(3), pp. 963-969.2018.




DOI: http://dx.doi.org/10.18517/ijaseit.8.4-2.6805

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