Paper Title
MACHINE LEARNING-BASED PREDICTIVE MAINTENANCE OF AUTOMOBILESAbstract
The rapid advancement of artificial intelligence and machine learning has created new opportunities for improving automobile reliability, safety, and maintenance efficiency. Traditional automobile maintenance practices primarily rely on scheduled servicing or corrective maintenance after a component failure occurs. These approaches may lead to unexpected breakdowns, increased maintenance costs, and vehicle downtime. To overcome these challenges, machine learning-based predictive maintenance provides an intelligent approach for monitoring vehicle health and predicting potential component failures before they occur. This study explores the application of machine learning techniques in predictive maintenance of automobiles using real-time and historical vehicle data collected from sensors and onboard diagnostic systems. Parameters such as engine temperature, vibration, oil pressure, battery voltage, fuel consumption, and other operational indicators can be analyzed to identify abnormal operating conditions and estimate the likelihood of component failure. Machine learning algorithms, including Random Forest, Support Vector Machines, Decision Trees, and Artificial Neural Networks, can be employed to detect anomalies, classify fault conditions, and predict maintenance requirements. The integration of Internet of Things (IoT) technology and cloud-based data analytics further enables continuous vehicle monitoring and timely maintenance alerts.
KEYWORDS : Machine Learning, Predictive Maintenance, Automobile Diagnostics, Fault Detection, Artificial Intelligence, IoT, Vehicle Health Monitoring, Condition-Based Maintenance.
