A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

Paulino José García Nieto, Esperanza García-Gonzalo, Antonio Bernardo Sánchez and Marta Menéndez Fernández
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Paulino José García Nieto: Department of Mathematics, Faculty of Sciences, University of Oviedo, C/Calvo Sotelo s/n, 33007 Oviedo, Spain
Esperanza García-Gonzalo: Department of Mathematics, Faculty of Sciences, University of Oviedo, C/Calvo Sotelo s/n, 33007 Oviedo, Spain
Antonio Bernardo Sánchez: Department of Mining Technology, Topography and Structures, University of León, 24071 León, Spain
Marta Menéndez Fernández: Department of Mining Technology, Topography and Structures, University of León, 24071 León, Spain

Energies, 2016, vol. 9, issue 6, 1-19

Abstract: Remaining useful life (RUL) estimation is considered as one of the most central points in the prognostics and health management (PHM). The present paper describes a nonlinear hybrid ABC–MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS), which have been successfully adopted for regression problems, with the artificial bee colony (ABC) technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC–MARS-based model from the remaining measured parameters (input variables) for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC–MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.

Keywords: multivariate adaptive regression splines (MARS); artificial bee colony (ABC); aircraft engine; remaining useful life (RUL); prognostics; reliability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
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