## 1 Introduction  
Composite materials are widely used in aircraft structures because of their advantages of stiffness, strength, light weight and excellent fatigue and corrosion resistance [1; 2; 3]. However, the various damage mechanisms of composite structures are very difficult to predict. Structural health monitoring (SHM), which can assess the structure status in real time and supply an early warning, has been proposed to confront this challenge [4; 5]. Acoustic emission (AE) is an attractive technique for SHM which is sensitive, can detect various types of composite material damage and needs only a small number of sensors [3; 6; 7]. The exact health state can be obtained by monitoring the AE signals. Furthermore, damage information such as damage mechanisms, damage position and residual life could also be extracted when the AE signals can be accurately collected and analyzed [8]. At present, both traditional AE characteristic parameters and new signal processing methods can be used to analyze AE signals. When composite structures are exposed to an external load, AE signals will occur from matrix cracking, matrix/fiber debonding, delamination, and fiber fracture [9]. Following the theory of plate wave, the generated waves will propagate in all directions, and form different modes. The difference of these modes is due to the particle displacement: in the plane (IP) for the symmetric or extensional mode and outside to the plane (OP) for the antisymmetric or flexural mode [10]. For the extensional mode, all frequency components travel at the same velocity, but for the flexural mode, the high frequency components travel faster than the low frequency components [11]. In practical structures, the signals detected by AE sensors depend on various factors, such as the structures' properties (including the material, width, geometry, and so on), damage source orientation, damage location, signal attenuation, wave dispersion, boundary reflections and other interference phenomenon. Therefore, all these factors have to be taken into account during the AE data analysis [10]. A major issue is how to distinguish between the different damage mechanisms according to the characteristics of the AE signal. Many studies have used time features, frequency characteristics, or time-frequency methods to identify the damage mechanisms [10; 11; 12; 13; 14], but they were only applied in simple laboratory experiments. For complex structures, [PERSON] and [PERSON] established an effective finite element modeling (FEM) routine to interpret the AE signal behavior [15; 16; 17; 18; 19; 20], which can give advice about how to extend the laboratory results to a practical scenario. In order to assess the real-time damage mechanisms, [PERSON] et al. [21] designed appropriate hardware frequency filters to split the extensional and flexural modes of the damage AE signals; [PERSON] and [PERSON] [22] thought about the large data sets of the collected data during the composite SHM, and explored various innovative processing techniques to save computing time. Additionally, based on the variety of signal processing methods, a large number of studies have correlated specific AE signal features with the progression of damage. [PERSON] et al. [23] identified some damage mechanisms of CFRP through pattern recognition algorithm based on five representative AE parameters (peak amplitude, peak frequency, energy, rise time and duration). [PERSON] et al. [24] classified the damage of matrix and delamination of CFRP based on AE signals through three classification techniques. [PERSON] et al. [25] considered significant AE parameters, such as counts, energy, signal strength, absolute energy and hits, and used an artificial neural network to predict the failure strength of composite laminates. [PERSON] and [PERSON] [26] indicated that wavelet analysis was suitable to process the AE signals in three-point bending load of composites structure, but the proper wavelet type should be selected.