## 2 Materials and Methods  
The development presented is focused on the implementation of an AI-based algorithm that allows, from the readings and analysis of the data obtained from the pressure sensors installed in the ship's FiFi system, the automatic detection of faults, allowing \"real\" time to achieve the following: (1) Detection of the fault: Identification is achieved by anomalous data readings in pressure sensors, and (2) Identification of the location of the detected fault: By comparing the data from the different sensors for the same event, the location of the fault within the system is identified. In the development of this work, three differentiated stages can be identified:Analysis of the Real System: In the initial phase, an in-depth analysis of the actual system was performed, and a profound understanding of its components, characteristics, and operation was gained. * Data Acquisition from the Real System: Following this, the acquisition of data from the actual system was carried out, ensuring that enough representative data were obtained. * Development of the Data-Driven Fault Detection and Localization System: Leveraging the collected and labeled data, the development of a data-driven system was initiated. This system is grounded in neural networks for the detection of anomalous pressure patterns and the pinpointing of fault locations. When selecting the most suitable technique for fault detection, the no free lunch theorem has been considered [28]. This theorem underscores that there is no universally superior algorithm applicable to all problems. Hence, the decision to employ neural networks for identifying pressure drops in a pressurized water system must be based on the method's appropriateness for the specific problem characteristics. After the extensive review of related studies, it was decided to use a system based on machine and deep learning compared to other methods, such as the use of a simulation model based on physics, as proposed in [29; 30]. Although physics-based models have important advantages [31; 32], there are limitations to their application in the proposed case. One of the most important is related to the following. The ship's firefighting system is complex, feeding salt water into various components on the vessel. Some of these components have manual openings that are not monitored by the control system, making it challenging to create a simulation model that ensures all events occurring in the system are simulated. On the other hand, the use of data-driven models also has important advantages [33; 34]. Specifically, in this case, it is intended to differentiate the pressure drop patterns in the case of openings of components that use saltwater from the FiFi system with respect to those that can occur due to rupture of a pipe in the system. The use of neural networks for pattern recognition for anomalies detection is widely [35; 36; 37]. In addition, it is proposed that with these same pressure measurements, it is possible to identify the point at which the fault has occurred. In this case, a classification neural network is proposed, a methodology that is also widely tested in other areas [38; 39]. The main challenge in the use data-driven models lies in obtaining enough data to cover all the possible scenarios that the ship may face in real life. For this purpose, it is important to note that it seeks to validate the use of a fault detection and location system with the added value that in the future it will continue to learn once implemented on board the ship. Finally, it should be noted that in numerous literature, it is concluded that important advantages can be achieved using models that combine physics and real data [40; 41; 42]. However, in the use case considered, it should be noted that it has been applied to a real ship in operation and, particularly, in a system with a high degree of monitoring of pressure signals, which implies that a significant amount of data can be relatively easily collected.