Acoustic emission (AE) techniques are increasingly employed in structural
health monitoring (SHM) and non-destructive testing (NDT) to assess the integrity of
engineering structures. By capturing transient elastic waves generated by the sudden
release of strain energy within materials, AE monitoring enables the early detection of
damage. Conventional AE sensing, however, primarily relies on piezoelectric transducers.
This study experimentally investigates the potential of MXene-based nanoma-
terial sensors as an emerging alternative for AE detection. MXenes are a family of
two-dimensional inorganic compounds composed of metal carbides, nitrides, or car-
bonitrides. Among them, titanium carbide MXene (Ti3C2-MXene), first reported in
2011, exhibits exceptional properties such as high electrical conductivity, mechanical
flexibility, hydrophilicity, and excellent film-forming capability. Its rapid dynamic response makes Ti3C2-MXene a promising candidate for sensing applications in SHM.
In this work, Ti3C2-MXene nanomaterials are synthesized and processed to form
a sensing element. The fabricated Ti3C2-MXene sensors are bonded to an aluminum
plate alongside a conventional piezoceramic sensor. Controlled AE events are induced
using pencil lead break tests, and the resulting signals from both sensors are recorded
and compared. The response time and signal characteristics of the Ti3C2-MXene
sensor are analyzed relative to those of the piezoceramic sensor to evaluate its AE
detection capability.
The study presented here establishes an initial experimental foundation for
MXene-based AE sensing and highlights their potential for future development of
lightweight, flexible, and integrable SHM sensor technologies.

When performing structural tests in Aerospatiale industry, being it as part of the certification process or just to test a specimen having some particular properties, the acquisition equipment used plays a crucial role. Data sources nature depends on what type of test is about to be performed causing multiple kinds of sensors attached to the structure to be needed. It is essential then having versatile equipment able to read from different kinds of sensors. In the past, SHM testing technologies have been using elastic waves propagation as the main source of data, however, environmental and operational conditions effects over elastic waves propagation cannot be belittled.

Regarding future tests, having capable of reading elastic wave propagation dedicated sensors and EOCs, primarily temperature, dedicated sensors hardware will be required. The reading of data from piezoelectric sensors has some further requirements including high sampling rate reading due to elastic waves frequency and the capability of actively interrogating the structure in order to know the current structural health status after events have been detected while also saving temperature data.

A new evolution of the current SHM testing technologies equipment must come to light to fulfill the new requirements while maintaining all already achieved features. A solution based on a modular, multi-chassis approach combining data acquisition from software synchronized temperature sensors that support later analysis helping on EOCS compensation and piezoelectric sensors will be the new proposed paradigm.

This new presented paradigm will improve the previous capabilities presented in the EWSHM 2024 edition with active interrogation, based on pitch-catch configuration, automatization using up to 64 channels, with bandwidth up to 1MHz with configurable rates up to 250Msps. And for the passive acquisition, 125KHz bandwidth with up to 250ksps rate. The automatization enables the equipment to launch active interrogation, by customizing the interrogation signal typically using burst 3 or 5 pulse between 50 and 500KHz, from passive acquisition state when an event above configurable threshold and trigger, depending on particular noise ratio and slope, is detected.

As a first approach, this new equipment will fulfill acquisition demands on a currently running empennage test, where previous impacts where successfully detected and events are currently being captured.

In this paper, this new development which allows minimum supervision of a running test due to automatization capabilities, collecting events data that will be further analyzed once the test is completed or in parallel to the test will be presented.

By extending the service life of structures, structural health monitoring (SHM) is a key approach for reducing society’s environmental footprint. Among SHM methods, Acoustic Emission (AE) is efficient as it allows both the detection and localization of damage that may occur during a structure’s service life. Traditional wired AE systems used in non-destructive testing campaigns require a quite long preparation time, mainly because of the wires to be set and identified. Besides, a certain quantity of the power is dissipated in both wiring devices and preamplifier circuit. For SHM devices, wires represent an obstacle to the implementation of a permanent monitoring system. Furthermore, in cases such as hydrogen tank monitoring, the setting of a SHM device in ATEX environment should require low electric power devices. In this context, we design and develop a wireless sensor system capable of amplifying acoustic signals and monitoring tank structures. This offers an innovative solution for continuous, sustainable, and real-time SHM based on rectifiers for power harvesting and ambient backscattering for wireless data transmission. Compared to a conventional AE system, the designed system involves electromagnetic waves (EW), which are used for two purposes. Firstly, they provide an energy source to power a previously designed ultra-low power consumption common-source voltage preamplifier (VPA) for conditioning the acoustic signals before wireless transmission. Secondly, the EW act as a carrier for the AE signal probed by the transducer, via a designed passive circuit which consists of nonlinear resistances of diode and MOSFET, controlled by the transducer signal. The transducer data is thus transmitted via sensor’s antenna in real-time by changing antenna scattering coefficient Γ. The proposed system requires no external voltage or power supply apart from the ambient radiofrequency (RF) waves. Using a quarter‑wave antenna and a 910 MHz carrier at 1 dBm, we demonstrate reliable backscatter reception of acoustic signals at 1.5 m. The demonstration of the wireless AE preamplification prototype is done with different types of acoustic sources: continuous air flow, and electrically driven ultrasonic emitter (continuous and pulse signals), in order to simulate two potential applications of acoustic emission: leak detection and crack detection in pressurized tanks. The performance and limitations of the ongoing preamplifier are analyzed and compared with conventional wired sensors.
The authors would like to acknowledge the fundings from the Region Nouvelle Aquitaine (AAPR2024I-2023-32394910, AAPR2024A-2023-32393310), the Univ. Bordeaux RRI BEST Factory of the Future and CETIM (ADERA NRP-2024-1141).

Valves serve as indispensable control components within fluid pipeline systems, playing a vital role in ensuring operational safety and efficiency across critical industries such as nuclear power, petrochemicals, and aerospace. Among various failure modes, internal valve leakage is particularly problematic. Unlike external leaks, which are often visibly apparent, internal leakage remains concealed within the valve structure, frequently evading detection until significant energy losses, operational inefficiencies, or even safety hazards materialize. While acoustic emission (AE) technology has emerged as a promising non-destructive evaluation and structural health monitoring approach, conventional AE sensors exhibit a critical performance limitation: insufficient sensitivity for reliably detecting and quantifying micro-leakages, typically those below 300 ml/min. This sensitivity gap stems largely from a fundamental mismatch between the broadband frequency response of commercial sensors and the specific, often weak, acoustic signatures characteristic of minor leaks, leaving a substantial need for enhanced detection methodologies at the sensor level.

This research proposes a detection framework employing resonance-enhanced acoustic emission sensors (RE-AESs). The core premise involves a paradigm shift from conventional broadband sensing to frequency-targeted detection. By meticulously designing the structure of the sensor, specifically, a piezoelectric cantilever system with optimized support and lumped masses, the resonant frequency of the RE-AES is precisely tuned to align with the dominant frequency components emitted by leaking valves. This research develops and investigates two distinct, valve-specific RE-AES configurations: one is engineered for a resonant frequency band of 15–25 kHz, targeting regulating valves, while the other is optimized for the 35–45 kHz range, tailored for globe valves. Through finite element simulations and experimental validation on multiple valve specimens under pressurized conditions, the study demonstrates significantly enhanced detection sensitivity. A second-order nonlinear fitting model effectively correlates AE parameters with leakage rates, enabling precise quantification of leaks as low as 100 ml/min. Wavelet packet analysis further confirms the concentration of leakage energy within the targeted frequency bands, validating the frequency-optimized approach. This work establishes a robust foundation for reliable online micro-leakage monitoring, offering substantial potential for improving operational safety and maintenance efficiency in industrial applications.

Acoustic Emission (AE) is a widely used technique in Structural Health Monitoring (SHM) for detecting and characterizing damage in structures. AE discrete signals originate from structural changes (crack growth, fiber breaking, or corrosion processes…) making them higly sensitive to damage. However, the interpretation of these signals remains challenging due to their complexity, the presence of noise, and often the overlapping nature of different source mechanisms. Also, propagation in the structure between the source and the sensors may have a larger impact on the acquired signals than the source itself. If this is not properly taken into account, dense arrays of sensors are needed, preventing the application of the method. While simple detection may be sufficient in some cases, complex scenarios, such as the indentification and classification of corrosion sources, require a comprehensive signal processing pipeline.

In this study, we present a complete AE processing framework tailored for complex damage monitoring. The pipeline consists of five main stages: signal acquisition, event localization, feature extraction, selection of the most relevant features and unsupervised classification. Event localization provides spatial information, which helps distinguish between different damage mechanisms and is needed to compensate for propagation effects. Feature extraction focuses on capturing time-domain, frequency-domain and statistical characteristics of the AE signals, which are then filtered and selected to maximize the distinction between different source types. Unsupervised classification techniques allow the automatic grouping of signals without a priori labeling. In practice, clustering techniques not only enable the discrimination of different source mechanisms but also support the estimation of the appropriate number of clusters based on data-driven criteria. This is particularly valuable in scenarios like corrosion where the number of sources (e.g. bubble formation and explosion, passivation film breakdown…) is not known in advance.

To illustrate the methodology, we discuss its application to corrosion monitoring, which represents a particularly challenging case due tor the presence of multiple simultaneous mechanisms, some of them in common in the different corrosion reactions, and its gradual evolution over time. The methodology is applied to datasets of increasing complexity to assess performances both in controled and realistic conditions. Expected corrosion natures are confirmed by physicochemical analysis.

Finally, we discuss potential extensions of AE monitoring using fiber Bragg grating (FBG) sensors in embedded configurations. FBGs are particularly suited for harsh environments, including high temperatures, strong radiation fields, and intense electromagnetic interference, where conventional AE sensors may not operate reliably. Such systems could enable distributed, continuous, and low-intrusive SHM, combining the sensititivity of AE with the robustness and multiplexing capabilities of optical fibers.