# Review on Structural Health Evaluation with Acoustic Emission
## 3 Source Location  
Methods of AE source location came from seismology and have been refined to meet demands for accuracy, speed, and robustness against false location [5; 108; 109; 110; 111; 112]. Actually, the source location or localization problem is a general topic of interest to broad segments of science and engineering. See a comprehensive review in connection to signal processing [113]. The AE location methods also evolved with available computational capabilities, starting with table look-up and first-hit or zonal location, as described by [PERSON] in 1972 [114]. In 1991, [PERSON] [115] developed a directional sensing method with four-sensor arrays to simplify source location, allowing a source location using two sets of sensors. More recently, three-sensor arrays worked just as well, despite reducing the sensor count [116; 117]. New localization methods are adapted to AE field as reported in, e.g., [118; 112]. For typical AE applications, currently available 2-D and 3-D location algorithms satisfy their basic needs. The algorithms utilize time differences of arrival (TDOA) and seek the intersection of a set of hyperboloids assuming constant wave propagation speed, usually relying on iterative processes. However, there are more requirements developing in the SHM field for faster calculations and less sensor placements [119; 14]. In particular, smaller sized PWAS have added a new dimension to source location strategy as these allow mode discrimination and provide directional information [14] and phased array concept is also a useful addition [120]. Automation of AE detection will enable broader uses of AE technology and [PERSON] et al. [112] discussed some examples developed, focusing on fatigue problems that still menace critical structural elements. One of newer approaches relies on exact closed form solutions, which can reach the source position efficiently and accurately. This analytical method obtains the AE source as the intersection of spheres, the radii of which are related to TDOA [121; 122; 123; 124]. Some are introducing artificial intelligence concepts for source location, including Gaussian process, support vector machine and deep learning [125; 126; 127]. Earlier, neural networks and genetic algorithm were also used [128]. For AE source location on thin plates, [PERSON] [129; 130; 131] introduced the Lamb wave velocity variation due to S\({}_{0}\) and A\({}_{0}\) modes, although the dispersion behavior was used in sensor frequency selection earlier [114; 33]. Others followed his approach and showed that, for linear location, only a single sensor is needed [132; 133]. For anisotropic plates, [PERSON] et al. [117] developed a method without solving a system of nonlinear equations. This method relies on three-sensor arrays, allowing simpler orientation detection. For 2-D location, this method can also find velocity values by itself with improvements [134]. Other methods can also be used for anisotropic plates [135]. In some composite layups, complex velocity patterns develop and a generic method was devised for such cases [118]. In this best-matched point search method, a structure is represented by points defined relative to sensor network. TDOA values of an AE event are matched with those of the previously defined points, quickly yielding the position of the AE event. Another method with TDOA, named delta-T, has also been advanced since its inception and now incorporate several signal-processing methods, becoming more reliable in dealing with complex geometries [136]. These methods have a root, tracing back to the table look-up methods [114], but now are highly refined for today's AE applications. Another new approach utilizes time reversal methods. This concept originated from the need to focus on the origin through inhomogeneous media [137; 138; 139]. A time reversal method was applied to the analysis of a ribbed composite plate where Lamb wave mode conversion occurs adding an extra mode to the initial two-mode propagation [140].