## 3 Artificial Intelligence as Enabler for Adoption of Nuclear Maritime Ships
### Monitoring and Maintenance
Therefore, AI will play a significant role in predictive maintenance, a concept that has already been successfully implemented in nuclear power plants and other critical industries [45; 46]. Figure 1 provides an overview of predictive maintenance and highlights its main challenge compared to land-based nuclear reactor systems. Unlike other industrial applications, predictive maintenance in large-scale maritime vessels faces unique constraints due to their low operational flexibility. This limitation arises from the nature of the maritime industry: the long voyages these vessels undertake may coincide with the optimal maintenance window identified by predictive maintenance systems. Consequently, decisions must be made regarding whether maintenance should be performed before or after a voyage, carefully weighing the trade and financial implications of each choice. Beyond maintenance, monitoring also plays a crucial role in the overall health management of nuclear systems [47]. AI can contribute significantly to developing critical systems for managing the health of nuclear-powered ships. Specifically, AI can be leveraged to build prognostic, fault detection, and diagnostic systems [48]. Prognostics refers to tools used to estimate the remaining useful life (RUL) of a component. It is a key aspect of predictive maintenance, enabling maintenance or component replacement to be scheduled just before failure occurs. This approach reduces maintenance costs by minimizing the need for multiple maintenance intervals and maximizing the component's operational lifespan [49]. Likewise, fault detection systems are designed to identify faults, whether in the form of abnormal operation or complete failure, and trace their origin. Complementary to fault detection, diagnostic systems determine the type of fault and pinpoint its exact location [50]. In sum, AI offers several solutions for implementing the aforementioned processes. In particular, machine learning (ML) models can be trained on historical or simulated data to identify behavioral patterns [51], which can then be used to predict the remaining useful  
Figure 1: Predictive maintenance concept in nuclear industry (**upper** path) and nuclear maritime vessels (**lower** path), where latter illustrates extra challenges needed to be taken into consideration. life (RUL) of components or detect deviations from normal operational behavior, such as fault patterns [52]. However, for nuclear systems with limited operational experience and associated datasets, an alternative approach is the adoption of rule-based AI solutions for prognostics and diagnostics [53]. Rule-based systems rely on IF/THEN rules that encode the knowledge and expertise of highly experienced professionals [53]. Given the scarcity of data on nuclear maritime vessels, developing rule-based AI monitoring systems with input from nuclear submarine experts presents a promising solution for ensuring the effective and safe operation of nuclear ships [53]. In addition, explainable AI systems may also be developed that not only perform the task of prognosis or diagnosis but also explain the reasoning behind their output [54], thus enhancing the trust towards AI systems.