## 3 Results
### Data Acquisition  
Data acquisition tests were conducted on a real ship. For this purpose, a custom datalogger was developed and integrated into the ship's control system network. The historical data gathered under controlled conditions were subsequently utilized for training the neural networks. It is important to note that the sensor information data used in this study is confidential. The data were collected under strict confidentiality agreements to protect the proprietary information of the ship's systems and ensure the privacy of the operational details. As such, specific details regarding the sensor data cannot be disclosed. Several tests were performed with the objective of collecting a substantial amount of data, categorized into two types: normal operation and fault behavior. First, data regarding the normal operation of the system (the FiFi system consuming water) were collected. To collect data corresponding to the normal operation of the system, all relevant consumers were opened, including firefighting nozzles with different flow rates, space sprinklers, and foam stations. Additionally, other shipboard systems were activated, such as bilge ejectors, the wastewater treatment plant, the waste cooling system, stabilizing fins, propellers, and transversal thrusters. These actions allowed for the collection of diverse data on the system's behavior under typical operational conditions. Figure 1 presents two examples of the pressure drop data series collected during the system's normal operation. On the other hand, efforts were made to replicate pipeline faults, such as ruptures. For this purpose, quick-opening valves were installed at various points in the system, under the assumption that their behavior would mimic the effects of a pipe section breaking. These quick-opening valves were strategically placed in different locations within the system branches, with varying pipe diameters, to simulate ruptures with different flow rates. Figure 2 illustrates the pressure drops recorded during two pipe break events. Although this approach aimed to cover a range of rupture scenarios, it is acknowledged that the data collected from these simulated breaks may be insufficient to represent all possible pipe rupture scenarios comprehensively. Nonetheless, the primary goal at this stage of development was to validate the proposed methodology. Future phases will involve extensive neural network training incorporating various fault scenarios, combining real-world data with simulated data. This approach will help address a broader spectrum of pressure drop profiles that may occur under different fault conditions. It is important to highlight that adjustments to the signal sampling frequency and the precision of pressure measurements (significant digits) were necessary to effectively characterize the pressure drops. These considerations, which may initially appear minor or insignificant, must be carefully addressed when planning data acquisition for digital twin developments. This is because certain functionalities, particularly those leveraging AI or advanced data analysis, may require high-frequency data to detect transient events, whereas routine ship operations do not typically demand high-frequency or high-precision data collection. Figure 1: Time series of pressure drop data collected during normal operation (y-axis in bars). Based on the tests conducted, the following key recommendations and limitations should be considered for the training of neural networks aimed at detecting and localizing faults using real data collected from the ship:  
* Data acquisition should be conducted at a frequency of no less than 10 Hz, with a recommended frequency of 20 Hz or higher to achieve optimal system performance. This conclusion is based on the observation that, in cases where data were recorded at lower frequencies, the collected information was insufficient to capture critical events and, therefore, could not be effectively utilized for analysis.