Machine Learning Applications in Predictive Maintenance for Vehicles: Case Studies

Abstract

The trend in the automotive industry has shifted from wanting the connected car, which uses the internet to fulfill the infotainment needs of the driver and the passengers, to acquiring the capability to manage the massive amount of vehicle data to enable new profitable opportunities such as maintenance-as-a-service. This real-time maintenance is possible using machine learning (ML) applications to develop predictive maintenance (PdM) algorithms. This creates a new realm focusing on preventing the unscheduled broken state of expensive automotive parts such as the clutch of an automatic transmission, as the breaking of a single part can affect the behavior of the whole vehicle.

This paper aims to help move the PdM industry even further, with an up-to-date insight into new available technologies and highlight potential applications for vehicle PdM, with a list of use cases that can be studied for future development. Additionally, for each use case, the most suitable data sources are also listed. Such a list is extremely helpful to researchers and developers, especially in the vehicle maintenance field, to understand exactly which sensor has to be developed and installed, in which area it is available, and with which resolution and accuracy.