Predicting Overtemperature Events in Graphics Cards Using Regression Models

Graphics cards are complex electronic systems designed for high performance applications. Due to its processing power, graphics cards may operate at high temperatures, leading its components to a significant degradation level. This fact is even more present when any of the heat exchange components is not working properly. In such cases, graphics cards may operate in temperatures that are higher than the specified by the manufacturers. This work presents a methodology to detect over temperature events in graphics cards using regression models. The proposed approach was tested in real graphics cards from different manufacturers. The final model achieved promising results.