Predictive Gearbox Oil Temperature Using Machine Learning

Gearbox is one of the most defining components for vehicles, turbines and other applications. A failure in the gearbox would ultimately cause the system to breakdown and thus results in operational failure. A gearbox failure can be attributed to several factors such as gearbox oil temperature, driving patterns, dependent engine components and other various gearbox performance. The focus of this paper is gearbox oil temperature sensor which is the one important factor that determines gearbox overheating and influence the system to take precautionary steps in switching from different types of oil to prevent risk of damaging their equipment and expensive repair. The goal of this paper is to predict the gearbox oil temperature sensor failure by adopting machine learning techniques. Various Machine learning (ML) techniques such as Support vector machine, Decision trees and Random forest etc are employed in this paper to achieve the objective. We also evaluate the performance of different ML techniques using key performance metrics. After analyzing these algorithms, we determine the classifier which provides better accuracy. This will help us in taking corrective and important preventive safety measures to minimize failure.