## 1 Introduction  
Acoustic emission (AE) technology has the potential for on-line structural health monitoring; a desired procedure for evaluating material degradation in aircrafts. Acoustic emissions are stress waves that propagate through a material as a result of applied stresses. When a material is subjected to cyclic fatigue loading, AE signals may be generated frequently with cracking within the material. These waves can be detected by piezoelectric sensors when placed on the surface of the material. The characteristics of the AE signal are determined by the mechanism that generated the signal, and the means by which it travels through the material and the sensor that transforms the emission into the signal [1]. The most commonly used AE feature for fatigue is the AE counts, which is defined as the number of times that an AE signal amplitude exceeds a predefined subjective threshold value. Other common characteristics used to describe features of an AE hit include the signal's peak amplitude, risetime, and duration. The amplitude is defined as the maximum voltage value of the signal often reported in decibels, the risetime is the time between the first count and the count with peak amplitude, and the duration is the time between the first and last AE count. These features are illustrated in Figure 1, which shows a typical waveform from an AE hit with the associated features. Besides relating features from the AE signals to fatigue crack growth, some researchers supplement the AE signals with other crack growth indicators. For example, surface temperature mapping known as thermography has been used to enhance detection of crack initiation and growth. The driving factor for this development is that in addition to the release and propagation of stress waves that are detectable by AE sensors during fatigue crack growth, small amounts of thermal energy are also dissipated. Recent promising researches [2; 3; 4; 5], built on the previous pioneering works on infrared tI thermography and AE signals, have also quantified fatigue crack growth. This paper, however, uses features of AE signals alone to describe fatigue crack growth behavior. Mechanical structures typically operate under a wide range of loading scenarios. Previously proposed fatigue life models based on the AE signal properties reported by [PERSON] and [PERSON], [6]; [PERSON] [7]; and later by [PERSON] & [PERSON] [8] have not been validated with respect to different loading conditions. These studies were also focused only on studying aluminum alloys. Different material and loading features, such as frequency and loading ratio, may also affect model predictions and can be verified through specially designed experiments. Several attempts have been made to relate different AE parameters such as AE count, energy, and amplitude to fatigue crack growth, stress intensity factor range (\(\Delta K\)), maximum stress intensity factor (\(K_{max}\)) and crack growth rates [9; 10; 11; 12; 13]. These studies showed that the relationship between the stress intensity factor range and the number of AE counts can be captured by an equation with a form similar to the Paris-Erdogan equation [14; 15; 16]. A leading general model that relates the AE count rate to the crack growth rate was proposed by [PERSON] [7], and has the following form:  
\[\frac{dc}{dN}=\alpha_{2}\bigg{(}\frac{da}{dN}\bigg{)}^{\alpha_{1}} \tag{1}\]  
where, \(c\) is the AE count, \(a\) represents crack size, \(N\) is the load cycle, \((da/dN)\) is the crack growth rate, \((dc/dN)\) is the AE count rate, and \(\alpha_{1}\) and \(\alpha_{2}\) are the model parameters. [PERSON] and [PERSON] [11] used a variation of Equation (1) in the form of the linear regression in Equation (2):  
\[\log\bigg{(}\frac{da}{dN}\bigg{)}=\alpha_{1}\log\bigg{(}\frac{dc}{dN}\bigg{)}+ \alpha_{2}+\varepsilon \tag{2}\]  
where, the error term, \(\varepsilon\), in Equation (2) accounts for the difference between the model prediction and the observed AE count rate.