An integrated risk assessment for maintenance prediction of oil wetted gearbox and bearing in marine and offshore industries using a fuzzy rule base method

This article presents an integrated risk assessment methodology for maintenance prediction of oil wetted gearbox and bearing in marine and offshore machinery with emphasis on ship cranes. Predictive maintenance uses important parameters measured in the equipment to 'feel' when breakdown is eminent. This type of maintenance intends to make interventions on machinery before harmful events may occur. This article assesses the risk levels of bearing and gearbox, which are the most sensitive components of the ship crane using fuzzy rule-based judgement for common elements and their sources. This will provide the ship crane operators with a means to predict possible impending failure without having to dismantle the crane. Furthermore, to monitor the rate of wear in gearbox and bearing of a ship crane, the ship crane reliability, and a trend to provide an operational baseline of data that will help the engineers to detect abnormal wear rates as they develop, is established. Within the scope of this research, a risk assessment model is developed for determining the risk levels of a crane's components and recommending solutions using all the diagnostic capability obtainable for effective condition monitoring of the gearbox and bearing in ship cranes.