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EN
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.
EN
In this work the TU (marine transport unit) needs for different types of energy are analysed in order to systematize the equipment of the TU engine room by type of energy produced. The mechanisms that provide TU with mechanical, thermal and electrical energy were identified. Research on the technical requirements and algorithms for the implementation of major power generation mechanisms showed the dependence of the process of equipment preparation and commissioning on the availability of electricity. Based on the analysis, a hypothesis was adopted on initial electricity generation and guaranteed flow of this energy, which allows guaranteed generation of all types of energy on board and thus ensure safety without emergency navigation.
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