A Review of Spur Gear Fault Diagnosis: Monitoring Methods, Predictive Models, and Industrial Challenges

Abstract

Spur gears made from metal serve as central equipment in multiple mechanical configurations. They experience various defects like fatigue cracks, abrasion, and adhesion wear, pitting and scuffing. This review delivers a detailed analysis and performance review of research material from recent studies regarding spur gear failure modes, together with monitoring techniques and predictive models. Where detailed analysis through real-world examinations is conducted, wind energy applications are combined with automotive and manufacturing sector work environments to evaluate diagnostic system performance in practice. In this paper, both benefits and drawbacks across time-domain, frequency-domain, and time-frequency domain techniques are analyzed. This includes Fast Fourier Transform, empirical mode decomposition, wavelet transform, and Hilbert-Huang transform, as well as contemporary developments in machine learning diagnostic systems. The research sector identifies three main missing elements: limited availability of fault-labeled data, difficulties in maintaining operational condition generalization, and real-time system implementation. Future work and industrial use of spur gear fault diagnosis solutions need guidance to develop robust interpretive fault detection systems at a large operational scale.