Fault diagnosis of offshore wind turbine gearboxes using a dynamic Bayesian network

The gearbox system is one of the most critical subassemblies in offshore wind turbine (OWT) drivetrains whose failures could lead to long downtimes and high repair costs. Therefore, it crucial accurately diagnose predict faults at an early stage development. This study develops a new dynamic Bayesian network (DBN) framework for fault diagnosis reliability analysis OWT systems by incorporating components’ degradation information condition-based maintenance (CBM) strategy. reliability, availability, mean-time between (MTBF) as well failure criticality index (FCI) each subassembly are estimated. results identified loss function bearing likely underlying cause system.