Structural Health Monitoring (SHM) aims to shift aircraft maintenance from a time-based to a condition-based approach. Within all the SHM techniques, Acoustic Emission (AE) allows for the monitoring of large areas by analyzing Lamb waves propagating in plate like structures. In this study, the authors proposed a Time Reversal (TR) methodology with the aim of reconstructing an original and unaltered signal from an AE event. Although the TR method has been applied in Narrow-Band (NwB) signal reconstruction, it fails when a Broad-Band (BdB) signal, such as a real AE event, is present. Therefore, a novel methodology based on the use of a Frequencies Compensation Transfer Function (FCTF), which is capable of reconstructing both NwB and real BdB signals, is presented. The study was carried out experimentally using several sensor layouts and materials with two different AE sources: (i) a Numerically Built Broadband (NBB) signal, (ii) a Pencil Lead Break (PLB). The results were validated numerically using Abaqus/CAE^TM with the implementation of absorbing boundaries to minimize edge reflections.

In the Structural Health Monitoring (SHM) field, Acoustic Emissions (AE) is the process by which acoustic signals generated during the formation of damage are captured by sensors, analyzed and used for localization within the structure. In plate like structures, these signals lead to the formation of Lamb Waves (LW), which are broadband in nature. These LW are generally captured by Piezoelectric Titanum Zirconate (PZT) sensors. As such, the captured broadband signals are of difficult interpretation in part due to several phenomena such as dispersion or attenuation suffered by the waves during their propagation. In this study, we hypothesize that the nature of the emitted signal contains information on the damage type, as if the features of the emitted signal were a 'fingerprint' of the damage. Wing or fuselage panels are some of the aeronautical structures were LW can develop during the emission of an acoustic signal. In operational service environments, the damage type and size may lead to the generation of different signal sources. This study aims at the development, through experimental techniques, of a classification algorithm based on Artificial Intelligence (AI) for determining the source of the emission in addition to their location within a structure. It is envisioned that the AI algorithms will be capable of identifying specific features within the emitted signals and thus correlate them to a database of known signals and their corresponding associated damage types. In order to create an AE signal damage database, the captured signal cannot be used since it has been affected by its propagation through the structure. As such, a Time Reversal process will be implemented in order to reconstruct the original signal. This original signal will be the one utilized by the AI algorithm in order to identify its corresponding damage source.

Acoustic Emission (AE) monitoring can be used to detect and locate structural damage such as growing fatigue cracks. The accuracy of damage location and consequently the inference of its significance for damage assessment is dependent on the wave propagation properties in terms of wave velocity, dispersion, attenuation and wave mode conversion. These behaviors are understood and accounted for in simplistic structures; however, actual structures are geometrically complex, with components comprising of different materials. One of the key challenges in such scenarios is the ability to positively identify wave modes and correctly associate their properties for damage location analysis. In this study, a novel method for wave mode identification is presented based on phase and instantaneous frequency analysis. Finite Element (FE) simulations and experiments on a representative aircraft wing structure were conducted to evaluate the performance of the technique. The results show how a phase analysis obtained from a Hilbert Transform of the wave signal in combination with variations of the instantaneous frequency of the wave signal, can be used to determine the arrival and therefore identification of the different wave modes on a complex structure. The methodology outlined in this paper was proven on an Automatic Sensor Test wave signal, Pencil Lead Breaks and Hanning windows and it was shown that the percentage difference is between 3% and 15% for the A₀ and S₀ wave speed respectively.

In Acoustic Emissions (AE) Hsu-Nielsen Pencil-Lead Breaks (PLB) are used to generate sound waves enabling the characterization of acoustic wave speed in complex structures. The broadband signal of a PLB represents a repeatable emission, which can be applied at different regions of the structure, and therefore can be used to calibrate the localization algorithms of the AE system. In recent years, the use of Finite Element Method (FEM) has flourished for modelling acoustic Lamb wave propagation, which is present in thin plate-like structures. The primary challenge faced by the AE community is the lack of a well-known mathematical function of a PLB signal that can be applied in numerical simulations. This study makes use of a Time Reversal (TR) approach to identify the emission source of the PLB on a 7075-T651 aluminum plate. An ABAQUS CAE™ model with piezoelectric actuators and sensors was developed. In order to avoid edge reflections, absorbing boundaries based on the Stiffness Reduction Method (SRM) were considered. The captured PLB signals were used as input to the FEM and was time-reversed. Furthermore, a band-limited white noise signal was used to calibrate the contribution of the broadband frequencies found in the transmitted wave packet. Preliminary results indicate that the TR approach can be used to understand the shape and function of the original transmitted signal.