Machine Learning based Engine Mount NVH Life Health Monitoring System

Refinement for NVH (Noise, Vibration, and Harshness) performance, and long-term vehicle reliability are rapidly evolving in today’s automotive industry. Engine mounts play a central role in isolating powertrain-induced vibrations; their deterioration can significantly affect cabin comfort, powertrain integrity, and customer satisfaction. Prior work in this area has primarily focused on direct mount sensors and physical inspection at service centre after failure. While effective in controlled environments, such methods are not scalable, add system complexity, and increase vehicle cost due to sudden breakdowns—challenges that restrict practical OEM deployment. This paper introduces a novel indirect health monitoring method that leverages a driver seat rail-mounted accelerometer to capture driver specific vibrational responses. By analyzing these signals using machine learning models and domain-specific analytical features, engine mount health is inferred without requiring sensors on the all 3 mounts. We developed and validated this approach using a combination of real-world vehicle data, controlled degradation cases, and extensive testing across varied operating conditions. Feature engineering, supervised learning techniques, and anomaly detection algorithms were applied to distinguish subtle variations in NVH behaviour at driver seat linked to mount degradation. The system demonstrated strong predictive accuracy, achieving reliable detection of degraded mounts under driving scenarios, without intruding on existing vehicle systems. This OEM-friendly, scalable solution enables cost-effective, real-time diagnostics and supports predictive maintenance strategies.