# Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
## 3 The Proposed Methodology of ECFAR for Burst Detection and Pipeline Fault Diagnosis  
### Burst Detection Use Enhance Constant Fault Alarm Rate  
Most of these AE events can be observed as transients in the continuous AE signal within a time scale of milliseconds to seconds. Burst events result in an instant change in fluid, producing consecutively transient peaks that are overlapping and have different lengths. Transients in AE signals recorded from the complex systems are often difficult or even impossible to separate using the accustomed threshold approaches. Several reasons for this have been proposed, such as the fluctuation amplitude of the continuous signal in a period of a loading cycle and overlapping of impulses, which can be concurrently released from the different AE sources in the pipe wall material. These transients have different shapes, durations, strengths, and frequencies. Consequently, as the complexity of the collected signal increases, more advanced processing methods are used to separate these impulses. In this study, we defined the transient events--like the salient objects, which require detection--against the background signal levels. To adapt to the noise level, ECFAR algorithms were used to calculate an adaptive threshold due to the estimated noise floor. This threshold was automatically updated according to the measured acoustic emission signals. In a real measurement, the presence of noise with unknown power may cause many false alarms if the threshold value chosen is too low. Conversely, if it is set too high, fewer objects will be detected. The process utilizes a statistical analysis which assures burst and impulse detection with a constant false alarm rate. The ECFAR procedure should provide an output that is adapted to the noise floor and ensures that the number of false alarms does not depend on the noise power. The ECFAR algorithms are forced to reach a specified false alarm rate. The threshold is the value that must be exceeded to be identified as an object. The assumed noise model is a zero-mean complex-valued Gaussian random variable that is independently and identically distributed. To find an adaptive threshold for the given noise model, the noise power has to be estimated. Impulse detection is announced if the signal amplitude passes over this threshold. The threshold is established by estimating the noise level in the reference window consisting of \(N\) values. The impulse detection threshold is determined by scaling the estimated noise level with a factor to achieve a desirable probability of false alarm. The principle of the algorithm is shown in Figure 2.  
In general, the threshold is an equation of both the probability of detection as the estimated noise level and the probability of false alarm. The impulse detection threshold \(T\) is specified by  
\[T=\alpha P_{n} \tag{1}\]  
where \(P_{n}\) is the estimated noise power, and \(\alpha\) is a scaling constant called the threshold factor. From the expression, the threshold adapts to the changing data. It can depict that, with the appropriate threshold factor \(\alpha\), the required probability of false alarm can be kept at an invariable value. The entire signal is divided into a series of consecutive windows. The noise power estimate can be computed in each window as \begin{table}
\begin{tabular}{c l l} \hline \hline
**Devices** & \multicolumn{3}{c}{**Detail Characteristics**} \\ \hline \multirow{4}{*}{WDI-AST} & - & Peak sensitivity [V/ubar]: \(-25\) dB \\  & - & Directionality: \(\pm\) 1.5 dB \\  & - & Working range: from 200 to 900 kHz \\  & - & Noise level (RMS RTI) \(<\) 3 \(\mu\)V \\ \hline \multirow{4}{*}{PCI 2} & - & Input: 2 channels \\  & - & 18-bit 40 MHz ADC \\ \cline{1-1}  & - & Sensor testing: AST built-in \\ \cline{1-1}  & - & Dynamic range: \(>\)85 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the data acquisition devices. \[P_{n}=\frac{1}{N}\sum_{m=1}^{N}x_{m}^{2} \tag{2}\]  
where \(N\) is window size, and \(x_{m}\) is the sample in a window and is considered as the cells.