# Preprocessing Acoustic Emission Signal of Broken Wires in Bridge Cables
## 1 Introduction  
Bridges are an integral and important part of the road system and are of strategic importance in the transportation industry [1], so ensuring the safe condition of bridges is of great concern. As the main load-bearing component in cable-stayed bridges, bridge cables are prone to fracture or even may cause major accidents due to prolonged loading and natural erosion [2, 3]. Thus, it is especially important to conduct health monitoring of bridge cables. Nondestructive testing technology (NDT) plays an irreplaceable role in bridge health monitoring nowadays [4]. Acoustic emission (AE) testing is an emerging NDT technique for a variety of materials, including rocks, metals, and composites [5]. AE also enables long-term and highly sensitive online health monitoring of bridge cables through the analysis and post-evaluation of acoustic emission signals. In 2010, [PERSON] [6] obtained the whole process of bridge cable damage through fatigue experiments, and successfully realized the damage identification of bridge cables by using the amplitude-node localization method. The author pointed out in the paper that the sampling process would be influenced by noise, and the noise was eliminated by simulating the noise signal and using the wavelet decomposition method without specific in-depth characterization of the noise. [PERSON] [7] used the methods of waveform analysis and principal component analysis to achieve the identification of bridge cable break signals in different modes. The article pointed out that the noise has an impact on signal identification. However, the noise used in the paper was white noise signal by a simple simulation, which might not have research significance under the actual working conditions. In 2015, [PERSON] et al. [8] studied the analytical identification of acoustic emission signals under repeatedcyclic loading with three types of representative steel, reinforced concrete and prestressed concrete bridge specimens in the laboratory. The authors used wavelet transform filtering techniques to filter simple background noise in the process. In 2020, [PERSON] [9] used the method of principal element analysis to achieve the identification of wire broken signals in bridge cables. The method is based on a data-driven, establishing data model to realize the distinction between the broken wire signal and non-broken wire signal. The author proposed that the AE signal monitoring process is firstly to detect AE, and the AE signal higher than the ambient noise needs to be cut out before the signal identification analysis. However, the experiments were conducted in the ideal environment in the laboratory, ignoring the actual noise under actual bridge environments. In 2021, [PERSON] [10] constructed a convolutional neural network for the classification and identification of wire broken signals by collecting AE signals from bridge cable pulling experiments in the laboratory and converting the AE signals into time-frequency image signals by wavelet transform. In 2022, [PERSON] [11] evaluated the classification of bridge cable damage by introducing the severity index of the AE signals. From analyzing the application of AE technology on bridge cables worldwide in recent years, it can be found that researchers mostly conduct bridge cable pull-off experiments in a laboratory environment to determine the state of bridge cables through the characteristic parameters of AE signals. When they consider the existence of noise in the process of detecting AE signals, they mostly add noise signal by simulation, or just assume the simple white noise signal. In this paper, to address the above situation, we propose an AE signal segmentation algorithm for the characteristics of AE signals. The proposed algorithm can accurately segment individual AE signals in the collected continuous signal stream. In this paper, noise signals were collected from three bridges under actual working conditions rather than simulation or simple assumption.