This project aims at evaluating the data generated by IT diagnostic systems in order to define the feasibility of implementing a predictive maintenance solution for the automotive industry.
One of the major difficulties in automotive maintenance is the evolution of customer services, which are being enriched and based on electronic systems that tend to become increasingly complex. Moreover, the automotive product is designed to operate for 20 to 25 years with an electronic configuration that will evolve (maintenance, EE repair, etc…). Given the growing complexity of the automotive product on the one hand, and advances in the field of machine learning on the other, maintenance is becoming a major economic challenge for manufacturers committed to respecting the safety constraints of goods and people.
This research project focuses on evaluating the data generated by automotive IT diagnostic systems to determine the feasibility of implementing a preventive maintenance solution. The four-month project is being conducted by the Swiss Data Science Center, a joint center of EPFL and ETH Zurich. It is sponsored by Groupe PSA.
|Principal investigator||Olivier Verscheure|