Genetic Algorithm-Based Fatigue Data Editing Technique

Abstract:

Durability testing is an essential process node in automotive component design analysis. The test associated with loading history can be accelerated if the fatigue data editing approach is considered for simplifying the given history. Even though they have been proven, unfortunately, the existing editing techniques involve complex mechanisms (e.g. abrupt detection, Fourier transformation and wavelet analysis), which are complicated in nature and which demand high computational costs. Therefore, this paper is a proposal of a simple technique that makes use of the rule-based fatigue segment classifier when deciding which parts of the history need to be removed. Rules representing the new labelling practice have been generated based on the classification data mining framework. In the context of this study, a rule set represents a group of undiscovered relationship between time domain statistical parameters and damage level. A dataset consisting of an equal length of fatigue segments trained using a multi-objective approach called the Elitist Non-dominated Sorting in Genetic Algorithm (NSGA-II) for seeking several optimal sets of rules (i.e., classifiers) by maximizing predictive accuracy and comprehensibility. The number of attributes underlying the rule set is referred to for final classifier selection where the fitter solution serves as the proposed editing technique. Comparison results on strain-stress cycle properties for the edited history and the full-length version shows that the proposed technique is suitable for fatigue data editing. Moreover, it has an additional benefit that no prior requirement on the frequency or time-frequency analysis is needed, providing the damage level of fatigue segments rapidly and the discovering of linguistic knowledge as a novelty.

• Periodical: Applied Mechanics and Materials (Volume 663)
• Pages: 431-436
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.663.431
• Online since: October 2014
• Authors: Mohd Haniff Osman*, Zulkifli Mohd Nopiah, Shahrum Abdullah, Izamarlina Asshaari
• Keywords: Fatigue Data Editing, Genetic Algorithm (GA), NSGA-II, Rule Mining, Segmentation

References

[1] R.I. Stephens, P.M. Dindinger, J.E. Gunger, Fatigue damage editing for accelerated durability testing using strain range and SWT parameter criteria, Int. J. Fatigue. 19 (1997) 599-606.

DOI: 10.1016/s0142-1123(97)00079-0

[2] S. Abdullah, C.K.E. Nidzwan, M.Z. Nuawi, A study of fatigue data editing using the short-time Fourier transform (STFT), Am. J. Appl. Sci. 6 (2009) 565-575.

DOI: 10.3844/ajas.2009.565.575

[3] Z.M. Nopiah, M.N. Baharin, S. Abdullah, M.I. Khairir, The detection of abrupt changes for variable amplitude fatigue loading by using running damage extraction method, J. Appl. Sci. 10 (2010) 544-550.

DOI: 10.3923/jas.2010.544.550

[4] K.Y. Fung, C.K. Kwong, K.W.M. Siu, K.M. Yu, A multi-objective genetic algorithm approach to rule mining for affective product design, Expert Syst. Appl. 39 (2012) 7411-7419.

DOI: 10.1016/j.eswa.2012.01.065

[5] T.E. Putra, S. Abdullah, M.Z. Nuawi, Z.M. Nopiah, Wavelet coefficient extraction algorithm for extracting fatigue features in variable amplitude fatigue loading, J. Appl. Sc. 10 (2010) 277-283.

DOI: 10.3923/jas.2010.277.283

[6] S. Baek, S. Cho, W. Joo, Fatigue life prediction based on the rainflow cycle counting method for the end beam of a freight car bogie, Int. J. Automot. Technol. 9 (2008) 95-101.

DOI: 10.1007/s12239-008-0012-y

[7] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2002) 182-197.

DOI: 10.1109/4235.996017

[8] M.H. Osman, Z.M. Nopiah, S. Abdullah, Feature selection for fatigue segment classification system using elitist non-dominated sorting in genetic algorithm, Appl. Mech. Mater. 165 (2012) 232-236.

DOI: 10.4028/www.scientific.net/amm.165.232

[9] B. Sreejith, A.K. Verma, A. Srividya, Fault diagnosis of rolling element bearing using time-domain features and neural networks, in ICIIS 2008, (2008) 1-6.

DOI: 10.1109/iciinfs.2008.4798444

[10] D.S. Guru, Y.H. Sharath, S. Manjunath, Texture features and KNN in classification of flower images, Int. J. Computer Applications. 1 (2010) 21-29.

[11] V. García, J.S. Sánchez, R.A. Mollineda, On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowl-Based. Syst. 25 (2012) 13-21.

DOI: 10.1016/j.knosys.2011.06.013