Particle swarm optimization for model selection of aircraft maintenance predictive models

Nowadays, predictive models -especially the ones based on machine learning- are widely used to solve many big data problems. One of the main challenges within predictive models is to choose the best model for each problem. In particular, model selection and feature selection are two important issues in machine learning models as they help to achieve the best results. This paper focuses on the restriction of these two problems to σ-SVR (support vector regression) and more specifically the optimization of both problems using the particle swarm optimization algorithm. Our approach is investigated in the estimation of remaining useful life (RUL) of aircrafts which a ects their maintenance planning and which is an interesting issue in predictive maintenances. That is, the experiment consists of predicting RUL of aircraft engines using an σ-SVR optimized by PSO. Experimental results show the efficiency of the proposed approach. © 2017 Association for Computing Machinery.