Machine Learning based Engine Mount NVH Life Health Monitoring System Tata Motors Passenger Vehicle, Limited

Format:

Book

Conference/Event

Author/Creator:

Iqbal, Shoaib, author.

Contributor:

Dusane, Mangesh

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Noise, Vibration, and Harshness performance refinement and long-term vehicle reliability are rapidly evolving in today's automotive industry and becoming a basic need considering comfort. 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.This paper introduces a novel indirect health monitoring method that leverages a driver seat rail-mounted accelerometer to capture driver specific vibrational responses. By analysing these signals using machine-learning models placed by AIML ECU and domain-specific analytical features, engine mount health is inferred without requiring sensors on the all three 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 engine Noise Vibration and Harness behaviour at driver seat linked to mount degradation. The system demonstrated excellent predictive accuracy with reliable detection of degraded mounts without intruding on existing vehicle systems. This OEM-friendly, scalable solution enables cost-effective, real-time diagnostics and supports predictive maintenance

Notes:

Vendor supplied data

Publisher Number:

2026-26-0359

Access Restriction:

Restricted for use by site license