Neuro-fuzzy fatigue life assessment using the wavelet-based multifractality parameters


This study aims to establish a fatigue life predictive model based on multifractality of road excitations using neuro-fuzzy method to assess the durability of suspension spring. Traditional durability analysis in time domain is complicated and time-consuming due to the needs of large data amount. Thus, it is an idea to adopt an adaptive neuro-fuzzy inference system (ANFIS) for relating the performance of coil spring to the multifractal properties of road excitations, giving a meaningful fatigue life prediction. Different membership function numbers were tested to obtain the optimum membership function number. During the data training process, the checking data was used to test the trained model each Epoch of training for overfitting detection. As a result, the Morrow-based fatigue life prediction model was found to give the most suitable result with three membership functions. The SWT-based model needed five membership functions due to nonlinear properties in the SWT-based fatigue life data. Training process of Morrow-based-ANFIS was stopped at Epoch 8 given its lowest checking root-mean-square-error of 0.6953. SWT-based model recorded a higher error of 0.7940. The neuro-fuzzy models gave accurate fatigue life predictions with 96 % of the data distributed within the acceptance boundary, hence, contributing to an acceptable assessment of coil spring fatigue life based on load multifractality. This study had shown a nonlinear relationship between road multifractality and durability performance of coil spring. Multifractality had been proven an important feature to characterise various road excitations for durability prediction.

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This work was supported by the Ministry of Education Malaysia (Grant: FRGS/1/2019/TK03/UKM/01/3) and Universiti Kebangsaan Malaysia (UKM) (Grant: DIP-2019-015).

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Corresponding author

Correspondence to S. Abdullah.

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C. H. Chin graduated with masters in Mechanical Engineering from the Universiti Kebangsaan Malaysia, Malaysia in 2016. He currently studies Ph.D. in mechanical engineering at the same university. His research is focused on the durability of components in automobile applications and signal processing.

S. Abdullah is a Professor at the Department of Mechanical and Manufacturing Engineering, Universiti Kebangsaan Malaysia (UKM). He received Ph.D. from the University of Sheffield, United Kingdom, in 2005. His research focused on fatigue failure, FEA-based fatigue analysis fracture mechanics, damage mechanics, fatigue data analysis, signal analysis and mechanics of materials engineering design.

S. S. K. Singh is a lecturer at the Department of Mechanical and Manufacturing Engineering, UKM, Malaysia. He received his Ph.D. in mechanical engineering from UKM in 2016. His research interests include reliability engineering, damage mechanics, fatigue data analysis, fatigue failure, structural integrity and durability analysis.

A. K. Ariffin is a Professor at the Department of Mechanical and Manufacturing Engineering, UKM. In year 1995, he received his Ph.D. from University of Wales Swansea. His specialty is in the computational method in engineering under the area of powder mechanics, fracture mechanics, friction, corrosion, finite element/discrete element and parallels computations.

D. Schramm is a Professor and Head of the Chair of Mechatronics at the University of Duisburg-Essen. He received his Ph.D. in Engineering in 1986 at the University of Stuttgart. His researches focused on vehicle dynamics, driver assistance system, vehicle simulators, mechatronic components, and robotics machines.

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Chin, C.H., Abdullah, S., Singh, S.S.K. et al. Neuro-fuzzy fatigue life assessment using the wavelet-based multifractality parameters. J Mech Sci Technol 35, 439–447 (2021).

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  • Neuro-fuzzy
  • Fatigue life
  • Wavelet transform
  • Multifractal
  • Durability