Published Paper Details:

Condition monitoring of gearboxes is a critical aspect of predictive maintenance that ensures the reliability and efficiency of mechanical systems across various industries. This review comprehensively analyses the most widely used condition monitoring techniques for gearboxes, focusing on vibration analysis, lubrication oil analysis, acoustic emission analysis, sound analysis, and thermography. Each method is evaluated based on its principles, advantages, and limitations. Special attention is given to lubrication oil analysis due to its significance in preventing gear failure through timely detection of wear particles and contaminants. The paper also discusses the integration of advanced sensor technologies and the role of artificial intelligence and machine learning in enhancing condition monitoring systems' accuracy and predictive capabilities. The comparative analysis highlights the suitability of different techniques for various industrial applications, emphasizing the need for a multi-faceted approach to gearbox health monitoring. Future trends in the field, such as the development of smart sensors and real-time monitoring systems, are explored, offering insights into the potential advancements that could further improve maintenance strategies. This review aims to provide a valuable resource for researchers and practitioners in the field of mechanical engineering, offering a foundation for future research and development in condition monitoring technologies. Condition monitoring of gearboxes is a critical aspect of predictive maintenance that ensures the reliability and efficiency of mechanical systems across various industries. This review comprehensively analyses the most widely used condition monitoring techniques for gearboxes, focusing on vibration analysis, lubrication oil analysis, acoustic emission analysis, sound analysis, and thermography. Each method is evaluated based on its principles, advantages, and limitations. Special attention is given to lubrication oil analysis due to its significance in preventing gear failure through timely detection of wear particles and contaminants. The paper also discusses the integration of advanced sensor technologies and the role of artificial intelligence and machine learning in enhancing condition monitoring systems' accuracy and predictive capabilities. The comparative analysis highlights the suitability of different techniques for various industrial applications, emphasizing the need for a multi-faceted approach to gearbox health monitoring. Future trends in the field, such as the development of smart sensors and real-time monitoring systems, are explored, offering insights into the potential advancements that could further improve maintenance strategies. This review aims to provide a valuable resource for researchers and practitioners in the field of mechanical engineering, offering a foundation for future research and development in condition monitoring technologies.