Enhanced predictive diagnostics for naval equipment: integrating MYT decomposition for advanced process monitoring

Abstract

The competitiveness in maritime operations demands maintenance strategies that ensure high reliability and availability at minimal cost. While predictive diagnostics have shown promise in detecting deviations from optimal operating conditions, current methodologies often fail to effectively isolate and identify the contributing process variables. This study introduces an enhanced predictive diagnostic approach that integrates MYT (Mason, Young, Tracy) decomposition with traditional statistical monitoring techniques, such as Hotelling's T² control charts. By applying this methodology to the auxiliary systems of a 284-meter LNG tanker, we identified that the key variables driving process anomalies were Superheated Steam in Boiler 1 (Tn/h) and Superheated Steam in Boiler 2 (Tn/h). These findings underscore the ability of the proposed method to detect deviations before critical failures occur, providing ship operators with actionable insights to enable precise maintenance scheduling, reduce operational costs, and prevent unscheduled downtime. The demonstrated integration of MYT decomposition into predictive maintenance protocols highlights its potential to optimize monitoring accuracy and decision-making in complex naval systems.