# Preprocessing Acoustic Emission Signal of Broken Wires in Bridge Cables
## 1 Introduction
The noise signals of the three bridges were analyzed in the time domain, frequency domain and wavelet to derive the distribution of the characteristic parameters and the filtering thresholds. The content of this paper belongs to the pre-processing work before the identification of AE signals of broken wires in bridge cables. It is of great significance for the practical application of AE technology in the health monitoring of bridge cables. In [12], the failure behavior of rooms and columns with different configurations under uniaxial loading was studied using PFC2D. It pointed out that the damage process was mainly influenced by the connection angle and the number of connections. [PERSON] [13] used PFC2D to study the effects of horizontal position, vertical position, thickness, number, and confining pressure of geogrid on foundation settlement and tensile force propagation along the geogrid. Paper [14] used PFC3D to study the effect of interaction between the aqueduct and the tunnel on ground settlement. Paper [12; 13; 14] used discrete units to study and analyze the relevant content in the field of geotechnical and hydraulic engineering, which is consistent with the purpose of this paper to use AE technology for health monitoring of bridge cables elements. However, this paper focuses on signal pre-analysis processing. The proposed AE signal segmentation algorithm and the studied noise characteristic parameters can improve the efficiency and accuracy of the use of AE technology in the field of health monitoring in bridge cables. Paper [15] determined the relationship between the corroded and uncorroded ties by performing tensile experiments to determine the stress and strain forces by artificially corroding the cables and found a large change in the performance of the corroded ties by AE parameters. Paper [16] studied and analyzed the concealment of cables-anchorage, by loading defective and non-defective ties with artificial pre-stress. A comprehensive analysis of AE events in multilayer cables can show the difference between healthy and damaged cables. In [17], the propagation characteristics of the AE signal waveform within the tool were determined by the boundary element method to better understand the effect of the tool geometry on the propagated AE signal. In [18], multi-sensor monitoring of tool wear status was performed by simultaneously detecting acceleration and spindle drive current sensor signals, and the current signals were proved to be more easily processed. All the above research contents are artificial prestress loading of bridge cables or tools before studying AE signal characteristics. However, this study was conducted under actual bridge conditions without prestress control units. Moreover, this study focuses on the signal pre-processing analysis before processing AE signals due to broken wires, which has a different focus from the above literature. The rest of this paper is organized as follows. In the second section, AE signal acquisition experiments are presented. We propose a novel segmentation algorithm in this section and noise analysis methods are illustrated. In the third section, signal characteristic parameter calculation, signal segmentation algorithm effect and noise synthesis analysis effect are analyzed respectively. Finally, we draw a conclusion in the fourth section.