## 1 Introduction
### Related Work  
#### 1.1.1 Fault Detection in Pipelines  
Detecting faults in pipeline systems is a challenge that has been addressed across multiple engineering disciplines for decades. The need for swift identification and localization of ruptures or blockages within pipes has prompted significant advancements in the field. Techniques for identifying leaks in pipelines range from basic manual inspections conducted by trained technicians to the utilization of advanced satellite imagery. Conventional approaches for detecting issues in pipelines, particularly in water distribution and hydrocarbon systems, are typically grouped into three categories: biological methods, hardware-based methods, and software-based methods [1]. Biological methods focus on visual inspections, while software-based methods incorporate various computational strategies. Hardware-based methods, on the other hand, are classified based on the types of sensors and devices used for fault detection. In their work, [PERSON] and [PERSON] [2] offer a comprehensive review of the key methodologies and technologies for detecting pipeline blockages and leaks, as summarized in Table 1.  
These technologies are diverse and are used for detecting both blockages and leaks in piping systems, each with specific approaches and methods to address these issues. The choice of the appropriate technology will depend on the specific needs and conditions of the pipeline network being monitored. Similar classifications can be found in different studies related, above all, to the oil and gas sector. For example, [PERSON] et al. [3] classifies detection techniques into hardware-based and software-based methods (Table 2). \begin{table}
\begin{tabular}{c c} \hline \hline
**Blockage Detection Techniques** & **Leakage Detection Techniques** \\ \hline
**Vibration analysis**: This technique involves monitoring the vibrations in pipes to identify potential blockages. Changes in vibrations can indicate flow issues. & **Negative pressure wave-based leak detection system**: This technique is based on the detection of negative pressure waves generated by leaks in pipes. \\ \hline
**Pulse echo methodology leak detection**: This technique uses ultrasonic pulses to detect leaks in pipes. The reflection of pulses can reveal the presence of leaks. & **Fiber sensor based**: It uses fiber optic sensors to detect changes in tension or deformation caused by leaks in pipes. \\ \hline
**Acoustic reflectometry**: It relies on the emission and detection of acoustic waves to locate blockages in pipes. The reflection of waves can indicate obstructions. & **Support vector machine-based pipeline leakage detection**: This technique employs machine learning algorithms like support vector machines to identify patterns related to leaks. \\ \hline
**Transient wave blockage interaction and blockage detection**: This technique utilizes transient waves to detect blockages in pipes. Changes in wave propagation can signal blockages. & **Piezoelectric acoustic emission sensor**: This sensor detects acoustic emissions caused by leaks in pipes. \\ \hline
**Stochastic successive linear estimator**: It is based on mathematical and statistical models to estimate the presence of blockages in pipes. & **Filter diagonalization method**: This method utilizes signal processing techniques to identify noise patterns related to leaks. \\ \hline  & **Harmonic wavelet analysis**: This method focuses on decomposing acoustic signals into harmonic components to detect leaks. \\ \hline  & **Genetic algorithm in combination with the inverse** **transient method**: This method combines genetic algorithms with modeling techniques to detect and locate leaks. \\ \hline  & **Computational fluid dynamic simulation**: This method uses numerical simulations to predict and detect leaks based on fluid flow behavior in pipes. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Software and hardware detection methods.