## 1 Introduction
### Related Work
#### 1.1.1 Fault Detection in Pipelines
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Hardware-Based Method** & **Software-Based Method** \\ \hline
**Distributed optical fiber sensor method**: Distributed optical fiber sensors use optical fibers as sensing elements. They can measure various physical parameters like temperature, strain, and vibration along the length of the fiber, making them suitable for structural health monitoring and environmental sensing. & **Neural network method**: Artificial neural networks are trained using historical data from pipeline operations. These networks learn to recognize patterns and correlations within the data. Once trained, neural networks can analyze real-time sensor data and identify abnormal patterns that may indicate pipeline faults. For instance, a neural network can detect changes in flow rates, temperature, or pressure that deviate from expected behavior, suggesting the presence of a fault. \\ \hline
**Dynamic pressure transmitter method**: This method uses dynamic pressure transmitters to measure pressure changes in real-time. It is commonly employed in industries like oil and gas for monitoring pipelines and wellbore pressures. & **Piezoelectric acoustic emission sensor**: The piezoelectric acoustic emission sensor is a device used for detecting and analyzing acoustic emissions in pipelines. These sensors are sensitive to acoustic signals generated by pipeline defects such as cracks or leaks. When a fault occurs, the sensor converts acoustic waves into electrical signals. These signals can be analyzed to pinpoint the location and severity of the fault. This method is valuable for early fault detection and structural integrity monitoring in pipelines. \\ \hline
**Tracer method**: Tracer methods involve introducing a substance or marker into a system and tracking its movement to gather information. This is used in various applications, including hydrology, environmental monitoring, and industrial processes. & **Statistical method**: Statistical methods involve the use of statistical analysis to detect anomalies in pipeline data. By establishing statistical norms for various pipeline parameters, such as pressure or flow rates, any data that fall outside these norms can be flagged as a potential fault. Statistical process control charts and outlier detection techniques are often used to identify such anomalies. Unusual data points or trends in the statistics can indicate a pipeline fault. \\ \hline
**GPR**: GPR uses radar pulses to image the subsurface of the ground. It is commonly used in geophysical and engineering applications for locating objects or anomalies underground. & **Harmonic wavelet analysis**: Harmonic wavelet analysis is employed in pipeline fault detection to extract and analyze harmonic components from sensor data. By identifying specific harmonic patterns in the data, it can reveal irregularities or resonances in the pipeline, often linked to defects. This analysis is particularly useful when investigating faults that result in characteristic harmonic responses. \\ \hline
**SmartBall method**: The SmartBall method typically involves a free-swimming tool equipped with sensors that can be used for inspecting pipelines. It is used in oil and gas industry to detect leaks and anomalies. & **Harmonic analysis**: Harmonic analysis is applied in pipeline fault detection to decompose sensor data into harmonic components. It helps identify the fundamental frequencies and harmonics associated with pipeline conditions. When faults cause changes in harmonic patterns, this method can signal abnormalities in pressure, flow, or other parameters. It aids in the recognition of deviations from normal pipeline operation. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._* The use of software-based methods has the important advantage that it will require minimal or no investment in additional equipment (including additional sensors or other equipment).