## 1 Introduction  
Decades of research have produced guidelines for estimating ideal service life for aircraft to limit safety risks and monetary losses due to catastrophic fatigue failure. Specifically, the United States Navy uses the safe-life approach and retires aircraft once a crack is estimated to initiate and extend to a length of 0.25 mm based on extensive and time-consuming full-scale fatigue tests [1]. While the relatively low damage threshold of the safe-life approach reduces the likelihood of failure, the high safety factors tend to lead to premature retirement and a lower return on investment for aircraft owners [2]. Rather than basing retirement time solely on estimated crack initiation and full-scale testing, structural health monitoring (SHM) and nondestructive evaluation (NDE) methods can be used to estimate the true fatigue damage in a material. While SHM is most desirable in that the entire structural health is continuously monitored, NDE methods that evaluate structural health during discrete inspection periods are more practical and often implemented. Acoustic emission (AE) has become a recognized NDE method for detecting flaws in mechanically loaded structures. When a cyclic stress is applied to a metallic structure, dislocations move and create microcracks near inclusions which coalesce to form microcracks. These defects are considered to cause disorder within the structure, and, thus, fatigue damage is considered to be synonymous with microstructural disorder. During this growing microstructural disorder, stored elastic strain energy is released partly in the form of acoustic waves that can be measured and analyzed [3]. In the past, researchers have correlated AE signals to large fatigue crack propagation in metals. A popular method for doing so has been to correlate the number times the AE voltage signal crosses a certain amplitude threshold, referred to as counts, and the crack growth rate using a power law relationship [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. In addition, other AE features have been studied as fatigue cracks grow, such as amplitude [11; 12; 14; 15], energy [14; 15], rise time [11; 15; 16; 17], and average frequency [15; 16; 17]. While methods for estimating stable crack growth rate based on AE signals are well established, estimating crack damage at the smallest possible scale is most desirable. This idea has motivated researchers to better understand wave dynamics of AE signals within single crystals [18; 19; 20; 21] and polycrystalline materials [11; 12; 15; 22; 23; 24; 25; 26; 27; 28; 29; 30] (see [31] for further discussion on past AE literature). From these studies, researchers concluded that AE activity is present during initial damage due to dislocation motion and microcracks, and various AE features can be correlated to damage. Rather than using AE features as descriptors of fatigue damage and microstructural disorder, one can measure the actual disorder or the information content of the received AE signals using information entropy. Various methods to quantify the information entropy in the AE signals have been previously proposed. [PERSON] and colleagues [32] estimated two time-domain entropies and two frequency-domain entropies from AE signals recorded during composite fatigue tests. The entropies were calculated from discrete probability distributions of the amplitude and frequency measured for one fatigue cycle every five minutes of testing. In the end, the entropy evolutions were similar to AE count trends, and entropy from AE signals was proposed to be a measure of microstructural disorder Reference [33] also focused on deriving entropy from AE amplitude distributions as a measure of fatigue damage. Results showed that measured disorder from AE amplitude distributions increased as damage also increased. [PERSON] et al. [34] introduced unsupervised pattern recognition based on time and frequency in order to distinguish clusters of AE signals and computed mutual entropy. Finally, [PERSON] and [PERSON] [35], and [PERSON], et al.