CONDITION BASED PREDICTIVE MAINTENANCE ON SHIP’S MAJOR EQUIPMENT USING AI

This project mainly aims at diagnostics and prognostics approach for Main Engine fitted onboard Ship. Keeping in mind the complexity of equipment’s fitted in ship and the frequency of defect, there is always a demands for prognostic maintenance approach rather than TBM (Time based Maintenance). Under these circumstances, the CbPM(Condition based predictive maintenance) helps in predicting the fault of a running machine and signals to perform maintenance on it only when required, thereby impacting operational readiness and logistic stability. Condition-based maintenance (CBM) is a program that banks upon three factor- data processing, maintenance decision making and data acquisition. The paper mainly focuses on implementing CBM on a mechanical system with emphasis on technology, algorithm and model for data processing. This project focuses on implementing CBM on machines rather than TBM by knowing the past defect history; maintenance schedule, periodic routine and then applying AI tool on the same for predicting the machines future condition. This will thereby help in improving the overall life of the equipment. An instance on the use of various classification algorithms is taken into picture and compared their accuracy based on the F1 score in this paper.