Predictive maintenance using deep learning

Abstract

[en] The goal of this study is to demonstrate if failures reported in an aircraft can be related to the environmental conditions during operation time. The current study is the first step of a long-term predictive maintenance project driven by the company DMD Solutions. First of all, the concepts of reliability and predictive maintenance are introduced. Furthermore, the fundamentals of machine learning and the state of the art are detailed. Gathering quality data was a complex process, since the available data was incomplete, noisy and unbalanced. The analysis proposes and compares several solutions. Two different approaches were carried out: the first one consisted of the prediction of failure (binary classification), and the second one, more ambitious, the prediction of the time before the next defect using time intervals (multi-class classification). Both approaches were designed using an iterative process that improved quality of both models and data at each stage of the study. The obtained results were promising and encourage further research.