Hybrid Time Series Forecasting Models Applied to Automotive On-Board Diagnostics Systems

摘要

A desired characteristic of the automotive diagnostics systems is to make fault predictions to prevent unexpected car breakdowns, avoiding financial losses and physical damages to the drivers. Based on that, the objective of this work is to evaluate intelligent hybrid systems to forecast real-time information from three in-vehicle sensors: engine coolant temperature, air fuel ratio (AFR) internal combustion and automobile battery voltage. Numerical results showed that, in general, combining forecasters from the residual modeling is a promising approach in the context of automotive data. In addition, the alternative combination of nonlinear with linear models suggests a hopeful proposition that can be used in other applications.