## 1 Introduction  
Carbon fiber woven composites have been used in a wide range of applications in, for example, aerospace, automobile, and industry due to their high specific strength and stiffness and other outstanding properties [1; 2; 3; 4]. However, in spite of these advantages, damages such as matrix cracking and debonding that appear during the manufacturing and service processes are still unavoidable. Therefore, characterization and process monitoring of the damage evolution of the composites is highly necessary [5; 6; 7; 8; 9]. Up to now, some methods such as impedance spectroscopy [10], eddy current testing [11], and the residual strength technique [12] have been used to monitor the progressive damage in materials. By contrast, acoustic emission (AE) technology can provide real-time information varying with external variables such as load, time, and temperature. Therefore, it has been used more and more widely in online monitoring and the early prediction of damage [13; 14; 15]. The AE technique is based on the principle of the rapid release of elastic waves induced by local damage sources in composites and has been widely applied for structural health monitoring and damage evaluation of the composites in real time [16; 17; 18]. According to the AE signal waveform collected by sensors, some characteristic parameters like peak amplitude, energy, and duration can be obtained, which provides an indication about the damage evolution of the composites during the tensile and compressive tests [19; 20]. [PERSON] et al. [21] proposed an experimental methodology based on AE wave detection for determining delamination onset and propagation in carbon fiber composite materials under quasi-static and fatigue loading. It was found that AE energy distributionsare well correlated with the delamination length and the crack growth rate. [PERSON] et al. [22] reported a significant correlation between the damage mechanism and textile architecture of 2 \(\times\) 2 twill carbon-epoxy composites. It was found that the fiber volume fraction has barely affected characteristic strain values while the first AE event is recorded. In addition, the AE technique can also evaluate the different failure mechanisms of the composites [23; 24; 25]. [PERSON] et al. [26] studied the failure characteristics of carbon/Kevlar hybrid composites by using the AE technique under high strain-rate compressive loading. The results showed that AE signals can reflect the dynamic behaviors and failure modes in composites. The amplitude range of matrix cracking, fiber-matrix debonding, and fiber breakage is from 42 to 43, from 50 to 65, and over 94 dB, respectively. Up to now, investigation on the mechanical testing, nondestructive testing (NDT), and failure behaviors of the composites on the tensile condition have been carried out by some researchers [27]. [PERSON] et al. [28] conducted the tensile mechanical properties and fracture characteristics of carbon fiber reinforced plastic (CFRP) composites embedded with SiC powder by using the AE technique. It was found that the fracture behavior of the SiC reinforced CFRP composites could be effectively evaluated on the basis of the specific AE frequency bands. [PERSON] et al. [29] used the shear-lag model to describe the micro stress field of the damaged composites for determining the microdamage parameters. It can be found that the occurrence of multiple damage mechanisms, i.e., matrix cracking, fiber/matrix interface debonding, and fiber breakage, is the main reason for the non-linear characteristic of the composites. In the case of the analysis of AE signals, [PERSON] et al. [30] reported a result that the cluster identification creates a link between damage mode and AE parameters of the corresponding AE events by cluster analysis which was based on the k-means++ algorithm and principal component analysis (PCA) of AE signals for woven glass/epoxy composites. It was found that cluster analysis is an effective method to analyze AE signals.