# Preprocessing Acoustic Emission Signal of Broken Wires in Bridge Cables
## 2 Materials and Methods
### Acoustic Emission Signal Segmentation Algorithm  
The common AE signal is a burst signal. However, when we carry out signal acquisition, the signals collected by the acquisition card are continuous. How to find the burst signal from the continuous signal, and how to determine the start point and end point of the burst signal are the problems that need to be solved. Inaccurate judgment of the start and end points of the AE signal may lead to inaccuracy of the characteristic parameters. The calculation of parameters such as energy, ringing count and duration of the AE signal is related to the length of the AE signal. Moreover, using the proposed algorithm to segment the signal may save effort and be more time efficient compared with manual segmentation. The acquisition card used in the experiments of this paper is a general-purpose acquisition card for AE signals. This kind of acquisition card can only acquire AE signals continuously, but does not have the function of segmenting the AE signals and calculating the subsequent characteristic parameters. Therefore, it is indispensable for the subsequent analysis of the AE signal to accurately segment an AE signal from the acquired continuous waveform stream. In this paper, we propose an AE signal segmentation algorithm using the characteristics of AE signal. This proposed pre-processing algorithm has great significance for the future AE signal identification in the health monitoring system for bridge cables.  
In general, the AE signal is segmented from the acquired continuous waveform, mainly to find the start and end points of the AE signal. The algorithm segments the signal based on the following parameter values and the specific meaning of each parameter is shown below:  
* Threshold (_Thr_):  
When we analyze the continuous waveform acquired from the acquisition card, the part of the signal without AE signals needs to be filtered out. We may set a value as the threshold. If the amplitude of the collected signal in the data stream has not exceeded the set threshold, it may be filtered out. When the amplitude of an existing signal crosses the threshold, it is considered the arrival of an impact signal. The time when the point exceeds _Thr_ is the start time of the AE signal.  
* Hit Definition Time (_HDT_):  
After starting to segment the AE signal, finding the end point of the AE signal is the next step to be carried out. When the amplitude of the acquired AE signal changes from higher than _Thr_ to lower than this value, the HDT timer is triggered. If the amplitude of AE signal remains lower than _Thr_ within _HDT_, the last point that crosses the threshold is considered to be the end point of the AE frame. Then, the process of segmenting the signal is also finished after the judgment. The next section of signal reading and judgment is carried out.  
* Hit Lockout Time (_HLT_):  
For some AE signals, a subsequent rebound wave may exist. In order to avoid misjudging the rebound wave of the AE signal for the next AE signal waveform, the parameter of hit lockout time (_HLT_) is introduced. After obtaining a frame of AE signal, regardless of whether there is a signal that exceeds the threshold or not, the signal from the acquisition card within the period of _HLT_ will be ignored. It will not trigger the _HDT_ judgment mechanism either.  
* Maximum Duration (_MD_):  
At some special moments, AE signals may be too dense to be distinguished separately. If the amplitude of the signal remains higher than _Thr_, the parameters mentioned above may not effectively segment the AE signal. To resolve this problem, we introduce the parameter maximum duration (_MD_). When the signal length exceeds _MD_, the acquired signal is mandatorily segmented. From the start time to the time when the signal exceeds MD, its frame is treated as a complete AE signal.  
By setting the above basic parameters, the flow of the algorithm is designed as shown in Figure 2.