Aero engines remaining useful life prediction based on enhanced adaptive guided differential evolution

Author's Department

Mathematics & Actuarial Science Department

All Authors

Sara Abdelghafar, Ali Khater, Ali Wagdy, Ashraf Darwish, Aboul Ella Hassanien

Document Type

Research Article

Publication Date



Remaining Useful Life (RUL) prediction is a key process for prognostic health management in almost all engineering real-world applications, especially which are in hazardous and challenging environments where the failures and disastrous faults cannot be avoided such as space vehicles and aircraft. This paper proposes a predictive approach based on our proposed algorithm Enhanced Adaptive Guided Differential Evolution (EAGDE) is used to optimize the parameter selection of Support Vector Machine (SVM) to give high RUL prediction accuracy. The advantages of the proposed approach (EAGDE–SVM) are verified using the popular benchmark C-MAPSS which describes the degradation of the aircraft turbofan engine datasets. The experimental study compares EAGDE–SVM with the basic SVM with randomized parameter selection and with an optimized SVM using three different optimization algorithms. Also, the EAGDE–SVM is evaluated against three popular classifier models that have been used in the comparisons of recent research. Different evaluation criteria of classification, prediction, and optimization aspects have been used, the obtained results show that the EAGDE is capable to achieve the lowest classification error rates and RUL high prediction accuracy through finding the optimum values of the SVM parameters with high stability and fast convergence rate.

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