Machine Learning Based Predictive Maintenance for Automotive Systems

Predictive maintenance plays a central role in reducing vehicle downtime and improving operational efficiency by using data-driven methods to classify the condition of automotive engines. Rather than relying on fixed service schedules or reacting to unexpected breakdowns, this approach leverages machine learning to distinguish between healthy and failed engines based on operational data.

In this project, engine telemetry data capturing key parameters such as engine speed, fuel pressure, and coolant temperature was used to train and evaluate several machine learning models, including logistic regression, random forest, k-nearest neighbors, and a neural network. To further enhance predictive performance, ensemble strategies such as soft voting and stacking were applied. The stacking ensemble, which combines the strengths of multiple classifiers through a meta-learning approach, demonstrated particularly effective results.

This classification-based framework demonstrates how data-driven fault detection can enhance automotive maintenance operations. By identifying engine failures more reliably, machine learning enables safer transportation, reduces maintenance costs, and enhances overall vehicle dependability. Beyond individual vehicles, such approaches have broader applications in fleet management, where proactive decision-making can improve service continuity, reduce operational risks, and increase customer satisfaction.