Offline Predictive Maintenance for Automotive Engines Using Machine Learning

Predictive maintenance (PdM) is a key element in today’s automotive systems, which lowers maintenance costs and enables real-time engine problem detection. However, typical PdM systems are costly and unavailable for older vehicles and traditional automotive services because of their heavy reliance on IoT sensors and real-time data monitoring. We suggest an offline predictive maintenance approach to get over this restriction by utilizing both manually entered engine data and advanced machine learning techniques like Random Forest, decision trees, and deep neural networks. Our full evaluation of several machine learning methods (ranging from ensemble techniques, stochastic frameworks, and neural network architectures) identifies the most suitable models for offline application with high predictive accuracy. The methodical comparison of classical machine learning methods with contemporary deep learning strategies provides information on model appropriateness for resource-poor environments. The findings highlight the viability of low-cost, offline predictive maintenance technologies to provide everyone with access to advanced automotive diagnostics. This research enriches the discipline by presenting a usable mobile application interface that assists automotive service engineers to analyze engine conditions without relying on expensive IoT infrastructure.