## 3 Results
### AI Development Description
#### 3.4.2 Fault Location Network  
Using real data collected from the tests, a neural network was tested to approximate the fault location. The network uses pressure measurements from the sensors to determine signal delays. Based on these pressure drop patterns, the network attempts to identify the approximate section of the system where the fault has occurred. To achieve this, it was necessary to characterize the locations of the sensors and consumers within the system to properly label the positions of elements. This labeling is essential for training the AI-based fault location system. Due to the limitations of the tests conducted, data were collected only from consumers and faults at specific positions on the ship. The ship was divided into nine areas, with the different tests carried out being labeled accordingly (Figure 8). The division into nine zones was determined based on the ship's structure and operational requirements. Specifically, the ship was divided into three vertical zones, corresponding to the ship's three main fire zones. Each of these vertical zones was further subdivided into three horizontal zones. This structured division allows for precise localization of faults and ensures that the neural network can effectively identify and differentiate between different areas of the ship. The architecture selected for the fault detection network was a classification network. Classification networks are a type of feed-forward network designed to classify inputs into target categories. The input data for the neural network consist of a series of pressure measurements from each sensor, capturing both time and pressure values as shown in Figure 9. The output is a numerical representation of a specific zone on the ship where the anomaly is detected. The same dataset used for fault detection is utilized for the anomaly localization network, but the preprocessing differs for training purposes. In the detection network, raw pressure  
Figure 8: Areas into which the vessel has been divided for fault location. values from the sensors are employed directly. Conversely, for the localization network, the data are preprocessed to compute the pressure drops per second. Different network configurations, including variations in the number of layers and neurons, as well as the sizes of input data samples, were trained to identify the optimal network performance. Additionally, various training functions were employed, such as the scaled conjugate gradient, Levenberg-Marquardt, and Bayesian regularization. In this particular study, only single-layer networks were used, with the number of neurons reaching up to 50. Different training functions were employed, including scaled conjugate gradient, Levenberg-Marquardt algorithm, and Bayesian regularization. In this case, only single-layer networks were used, and each configuration was initialized 10 times. For each network initialization, 10 retrainings were performed, retaining the layer with the best performance (minimum MSE). A schematic of the location algorithm proposed is shown in Figure 10.  
The model architecture is based on a single-layer artificial neural network, with the number of neurons specified by the parameter 'neurons'. This design choice reflects a simpler configuration where all processing occurs within a single hidden layer. The exploration for the number of neurons in this single layer ranges from 5 to 50, providing a spectrum of complexity for the model. The search spans 46 different values, allowing for the consideration of various neural architectures and their respective representation capabilities. The granularity of the search is defined by the number of different values considered for the 'neurons' parameter, covering 46 values in this case. This fine granularity enables a detailed exploration of the model's performance across a diverse set of neural network configurations.