## 1 Introduction
### Related Work
#### 1.1.2 AI-Based Fault Detection in Pipelines  
There are some examples of using AI in pipe fault detection. [PERSON]'s presents the development of an expert system for fault detection [13]. However, everything seems to indicate that the tendency is to take advantage of the benefits that neural networks (NNs) can present for the resolution of problems of this type (analyzing data series, pattern recognition). As examples of what is claimed, both [PERSON] and [PERSON] employ neural networks to detect problems in subsea pipeline systems [14; 15]. Table 3 summarizes the information presented by [PERSON], compiling recent studies related to this field [16]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Relevant Information** \\ \hline [PERSON] and [PERSON] [17] & \begin{tabular}{c} [PERSON] and [PERSON]’s study focuses on using Bayesian learning within a neural network \\ to analyze pressure data in a water distribution system. The goal is to detect \\ anomalies or faults in the distribution system. \\ \end{tabular} \\ \hline [PERSON] et al. [18] & \begin{tabular}{c} This research employs a multi-layer perceptron neural network to analyze \\ pressure and flow data in a simulated crude oil distribution system. The neural \\ network is trained to identify and predict potential issues within the system. \\ \end{tabular} \\ \hline [PERSON] et al. [19] & \begin{tabular}{c} [PERSON] uses a Dempster–Shafer multi-layer perceptron classifier to analyze \\ pressure and flow data in a simulated crude oil distribution system. This \\ approach aids in the classification and detection of faults. \\ \end{tabular} \\ \hline [PERSON] et al. [20] & \begin{tabular}{c} In this study, [PERSON] employs a multi-layer perceptron neural network to analyze \\ to analyze experimental flow data in a water distribution system. The focus is on \\ the detection of abnormalities within the distribution network. \\ \end{tabular} \\ \hline [PERSON] et al. [21] & \begin{tabular}{c} [PERSON] et al. use a neural network to analyze pressure data in an experimental water \\ distribution system. While the specific type of neural network is not mentioned, it \\ is used to detect potential issues within the system. \\ \end{tabular} \\ \hline [PERSON] et al. [22] & \begin{tabular}{c} [PERSON] and the team utilize the backpropagation algorithm within a neural \\ network to analyze pressure data in an experimental steam plant system. This \\ aids in identifying and addressing potential faults in the system. \\ \end{tabular} \\ \hline [PERSON] et al. [23] & \begin{tabular}{c} [PERSON] and colleagues employ a neural network in the space of states to analyze \\ pressure data in an experimental water distribution system. This approach \\ enhances fault detection capabilities within the network. \\ \end{tabular} \\ \hline [PERSON] et al. [15] & \
\begin{tabular}{c} [PERSON] et al. apply a convolutional network to analyze pressure data in a simulated \\ water distribution system. The neural network is designed to enhance the \\ identification of faults within the simulated environment. \\ \end{tabular} \\ \hline [PERSON] et al. [24] &
\begin{tabular}{c} [PERSON] employs both convolutional networks and Bayesian reasoning within a \\ neural network framework to analyze pressure data in an experimental water \\ distribution system. This multifaceted approach aids in the detection and \\ classification of faults within the system. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recent developments in the use of neural networks for detection of pipe faults. Adapted from [16]. These studies collectively showcase the versatility and effectiveness of neural networks in fault detection within pipe systems, whether in water distribution, crude oil transportation, steam plant operations, or simulated environments. Neural networks prove valuable in enhancing the reliability and safety of these critical infrastructure systems.