Non-Contact Transmission Error and NVH Diagnostics in Helical Gear Drives: A State-of-the-Art Review

Abstract

With the increasing popularity of electric cars, which run at higher motor speeds than internal combustion vehicles, new Noise, Vibration and Harshness (NVH) problems have emerged for powertrains. Quiet, vibration-free vehicles create a premium feeling of quality and increase customer satisfaction. Demand has increased alongside the need for precise and quiet transmissions. One of the mechanical sources of vibration and noise generated by helical gears, which are also used in vehicle transmissions, is transmission error (TE). Conventional measurement methods for measuring TE are based on rotary transducers and torque sensors. These methods have their limits of accuracy and applicability. Recent advances in certain non-contact techniques, such as laser Doppler vibrometry, digital image correlation and motion magnification all open up new perspectives in TE and NVH diagnostics. Nowadays, machine learning (ML) methods are coming to the fore for condition monitoring and noise prediction. This literature review provides a structured overview of experimental setups, non-contact measurement technologies, and ML applications related to TE and NVH in helical gear drives. Current research trends are identified, limitations and areas of future opportunities are explored. In line with recent trends, the integration of optical diagnostics and ML offers promising opportunities towards efficient real-time condition monitoring. In addition, the paper explores the conceptual feasibility of a proposed low-cost, camera-based, non-contact test bench that integrates optical diagnostics and machine learning for estimating transmission error, offering an alternative to high-cost laser vibrometry.