PhD in Intelligent Fault Diagnosis and Prognosis Solution for Rotating Machinery in Cranfield United Kingdom | Cranfield University

This doctoral research aims to create, assess, and showcase an intelligent diagnostic and prognostic system for rotating equipment to improve operational safety, dependability, maintenance efficiency, and system availability. An extensive experimental platform will facilitate thorough investigations for validation purposes. Given the critical role of rotating machinery across industrial sectors, implementing Condition-based Maintenance (CBM) becomes crucial - an approach that forecasts maintenance needs through performance data analysis. Diagnostic and prognostic capabilities form the core of CBM strategies. Effective fault detection and prediction in rotating machinery can significantly decrease operational interruptions and maintenance expenses. Various methodologies including vibration monitoring, current signature evaluation, acoustic emission studies, and lubricant condition assessment have been employed for machinery health monitoring, enabling detection and prediction of diverse mechanical faults such as bearing wear, shaft cracks, gearbox issues, belt drive problems, reciprocating mechanism failures, mechanical rubbing, and malfunctions in motors, pumps, compressors, and fans.