Gearbox Fault Diagnosis: A Comparative Study of Machine Learning and Deep Learning Approaches

Gearbox fault diagnosis is a critical aspect of ensuring the reliability and performance of machinery in various industrial applications. This study presents a comprehensive survey employing a systematic literature review methodology, focusing on the comparative analysis of machine learning (ML) and deep learning (DL) approaches for gearbox fault diagnosis. The study systematically reviews and compares different methods employed in fault diagnosis scenarios, encompassing diverse maintenance strategies and fault analysis techniques. Examining diverse fault diagnosis methods, maintenance strategies, and fault analysis techniques, the survey systematically evaluates the effectiveness of ML and DL models, in identifying and diagnosing gearbox faults. The study addresses contemporary challenges in this domain, such as varying operating conditions and fault types. By outlining these complexities, the study aims to guide researchers and industry professionals toward informed decision-making in selecting appropriate methodologies for gearbox fault diagnosis. The comprehensive overview provided serves as a valuable resource, facilitating advancements in the development of robust diagnostic fault diagnosis techniques. It recognizes and addresses several contemporary challenges faced in Gearbox Fault Diagnosis, offering insights that can aid researchers and industry practitioners in navigating and overcoming these challenges.