Towards predictive maintenance: the case of the aeronautical industry
Maria Eddarhri, Jihad Adib, Mustapha Hain, Abdelaziz Marzak
Abstract
Periodic maintenance of aeronautical equipment is an expensive process. However, the most practiced types of maintenance in the field of aeronautics are preventive and corrective maintenance. The aeronautical wiring companies produce a huge amount of data that, with proper processing and intelligent systems, can provide useful information and data for the reduction of machine downtime, therefore the implementation of predictive maintenance whose objective is to predict the failure of systems by continuously observing their status, in order to plan and carry out maintenance actions in advance. In this paper, the benefits of artificial intelligence are presented with machine learning and data mining techniques could be used by aeronautical wiring companies, which use preventive and corrective maintenance. In addition, a study of the current situation is made, as well as a proposal of an expert system based on machine learning for the development of predictive maintenance.