Deep Learning Models for Predictive Maintenance in Automotive Manufacturing Equipment

This paper presents a deep learning-based predictive maintenance framework for automotive manufacturing equipment. Using sensor data—such as temperature, vibration, pressure, and voltage—the system employs LSTM and CNN-LSTM models to estimate the Remaining Useful Life (RUL) of critical components. The models are trained on time-series data to identify early signs of failure and support proactive maintenance planning. Experimental evaluation shows improved accuracy compared to traditional methods, enabling reduced downtime and maintenance costs. This approach offers a scalable, data-driven solution to enhance equipment reliability and efficiency in modern manufacturing environments.