This paper employs bibliometric analysis to consolidate sustainable wear control research of spur gears used in automobile gear boxes, examining trends, publication rates, citation rates, gear fault types and vibration-based analysis. Notably, there was a 609.10% increase in publications by 2024 from 78 in 2010 and a 281% increase by 2025. Key research areas include lubrication-related and strength-based failures, comprising 53% and 47% of the research, respectively. Promising wear detection methods such as convolutional neural networks (40%), artificial neural networks (24%) and contrast enhancement algorithms (14%) are identified. This review is designed for gear designers, tribology researchers and condition-monitoring specialists, offering them insights into recent advancements and emerging techniques in detecting and controlling spur gear wear. This analysis highlights research gaps and opportunities for further exploration in gear design.
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