This paper aims to assess the influence of stiffeners and sensor transfer functions on the measurability of acoustic emission (AE) waves in ship structures travelling as ultrasonic-guided waves. A procedure for evaluating this influence by calculating sensor correction coefficients has been developed. After applying the obtained correction coefficients, the transmission of the ultrasonic-guided waves and their dependence on the angle of incidence and different frequency content are investigated using finite element simulations and experimental measurements. The experiments examine the propagation of 60 and 150 kHz AE signals in a 10-mm thick steel plate. By combining the results of the simulations and experimental results, the attenuation due to the presence of stiffeners turns out to be less than 5 dB and the transmission coefficient appears to have limited variation for different angles of incidence. The results of this study can be used to optimize the accuracy and coverage of AE monitoring systems.

Concern for the structural assessment of ship and offshore structures during operations, including unmanned operations. The focus shall be on methodologies translating monitoring and inspection data into operational and life-cycle management advice, with associated criteria for decision making. This shall include diagnosis and prognosis of structural health, prevention of structural degradation and failures, and structural renewal and reuse. The research and development in passive, latent and active systems including their sensors and actuators shall be addressed. Special attention is to be given to structural digital twin technology and methods including reduced order analysis, inverse modelling, and AI technology application, combined with the use of monitoring systems and inspection data, to provide real-time advice for safe operation during the structural life-cycle.

Seagoing vessels operate in harsh environments which make them especially prone to progressive degradation mechanisms such as fatigue and corrosion. Acoustic emission (AE) monitoring is gaining interest from ship operators and inspectors for its potential as an early-warning structural health monitoring technique for these types of damage. A major challenge facing the implementation of AE is dealing with the background noise. This article presents an experimental study of ultrasonic noise levels in representative environments and conditions AE monitoring. The probability of detection (PoD) is proposed as a quantitative metric for the detection of damage in the presence of operational noise. Measurements were carried out in multiple locations on board of a vessel under different operational conditions. Measurements at cruising speed on hull plates inside the engine room suggest that the ultrasonic background noise level exceeded 90 dB under 100 kHz but rapidly reduced in the higher frequencies associated with the failure mode-related AE signals. The PoD was estimated to be 94% for damage signals above 100 kHz. These results suggest that acoustic emission monitoring has the potential to perform reliably under noisy conditions. This perspective is promising to the future of a structural health monitoring system based on AE measurement.

Corrosion is a leading damage mechanisms in the degradation of marine assets. Acoustic emission (AE) monitoring has gained increasing interest as a technique for continuous monitoring of corrosion damage. This study numerically and experimentally investigates the feasibility of wall thickness loss estimation from the AE signals due to localized corrosion. The interaction of the elastic waves emitted due to the evolution of corrosion damage are influenced by the local thickness and material properties of the structure. A steel plate of (500 mm x 500 mm x 10 mm) with a localized wall thickness loss between 0 and 80% in the center of the plate was considered. The numerical investigation was conducted using a higher-order finite element model. Laboratory experiments were performed on a carbon steel specimen instrumented with 7 AE transducers (40 - 250 kHz). Corrosion damage was artificially introduced in the steel plate by progressively milling a pit in the center. At different stages of wall thickness loss, simulated AE sources were generated. The response of the structure was evaluated based on signal characteristics such as amplitude, rise-time, frequency content, and waveform. A correlation between the signal amplitudes and the wall thickness loss was observed in both experimental and numerical results. This perspective is promising for the feasibility of corrosion-induced wall thickness loss estimation based on AE measurements.

In this paper, an investigation of the characterization of fatigue damage-induced signals by means of Acoustic Emission (AE) monitoring is presented. The objective is to establish a correlation between AE signals and fatigue crack growth data. To achieve this, small-scale fatigue experiments have been performed. The test consists of cyclic loading of standardized compact test (CT) specimens at room temperature. Damage-induced ultrasound signals were continuously measured using four AE transducers. The results suggest that AE signals emitted by fatigue crack growth from the initiation moment can be detected with a satisfactory signal-to-noise ratio. A multi-parameter analysis including amplitude, counts and hit rate of AE data in correlation with crack growth data was performed. Three stages of fatigue crack growth were identified, offering a basis for further damage characterisation using AE monitoring.

Large-scale low-speed rolling element bearings form crucial connections in offshore installations such as heavy-lifting cranes, single point mooring systems, and wind turbines. Due to the stochastic nature of wind and waves, the applied loads on these bearings are hardly predictable. Furthermore, the remote nature of the offshore environment requires a high standard of operational safety and reliability, reinforcing the need for advanced inspection and monitoring methods. Acoustic emission (AE) is a continuous monitoring technique for condition assessment of low-speed bearings, as it may detect the stress waves associated with developing degradation within the rolling elements. This paper presents an investigation on the influence of grease contamination on acoustic emission (AE) monitoring of low-speed bearings. It discusses several experiments that have been conducted in various regimes of particle contaminated lubrication, and proposes a strategy for condition monitoring. The presented experiments have been conducted with differing bearing geometries in multiple testing environments. The contamination particles have been both naturally developed and artificially introduced. The results total of 9 experimental cases are discussed and compared. All experiments follow the same data-acquisition principles, utilising arrays of three AE transducers sensitive in a range between 40-580 kHz. Data is recorded while operating the bearing at low speed under load. Abrasive wear of the rolling elements and crushing of the particles are concluded to be the main sources of AE due to particle contamination. Processing consists of waveform cross-correlation clustering and feature analysis. The results suggest that hard abrasive particles make significant contribution to the AE signals, which is associated with abrasive wear of the rollers and raceways. Comparisons with the contribution of softer and finer particles are presented as well.

Flexible composite marine propellers can aid the marine industry in reducing carbon emissions and underwater radiated noise pollution. The structural integrity of the blades can be assessed using structural health monitoring. One of these methods is the measurement and analysis of damage-induced acoustic emission signals. This paper experimentally investigates the feasibility of using embedded piezoelectric sensors for the measurement of acoustic emissions throughout a submerged flexible composite marine propeller blade. A full-scale glass-fibre reinforced polymer blade has been manufactured with 24 embedded sensors. While suspended in artificial seawater, acoustic emissions were simulated on the blade. The measurements show that the embedded piezoelectric sensors can measure acoustic emissions while the blade is submerged. Further, the distance from source to sensor over which the acoustic emission is measurable was investigated. For a noise level of 40 dB and a source amplitude of 70 dB between 100 and 250 kHz, an average maximum measurable distance of 124 mm was obtained. For higher frequencies, the distance drops and for lower noise levels the distance increases.

The acoustic emission (AE) technique allows monitoring damage in (reinforced) concrete in a non-destructive way by means of piezoelectric sensors attached to the material surface. This approach has disadvantages such as a decrease of the sensor coupling over time, high attenuation of AE waves in concrete, and difficulties in terms of sensor placement. Embedded AE sensors, so-called ‘smart aggregates’ (SA), can be a valuable addition or alternative to surface-mounted AE sensors. However, the embedment of sensors brings its own challenges. In this paper, the use of SA is investigated to monitor cracking of fiber reinforced concrete during a three-point bending test, and corrosion and related concrete cracking of reinforced concrete during an accelerated corrosion test. The novelty of the paper is the application of SA for passive AE monitoring during concrete degradation processes with a varying cracking behavior and crack orientation. Special emphasis is put on data filtering and localization of AE sources. The results show that, despite a higher level of wide-band noise for the SA sensors, they are able to detect and localize concrete cracking after dedicated filtering. Furthermore, the potential of SA sensors in early-stage detection of corrosion damage is demonstrated, offering enhanced possibilities for predictive maintenance of concrete structures.

This paper investigates the feasibility of detection, localisation, and monitoring of corrosion-fatigue damage in mooring chain links using remote Acoustic Emission (AE) technique in submerged conditions. A large-scale experiment was conducted on a studless R4 chain retrieved after about two decades of operation offshore. Ultrasound signals were continuously measured using fixed and movable arrays of AE transducers placed on perpendicular planes in the water tank enclosing the chain. The AE parameters extracted from the measured signals have been analysed. AE sources were successfully localised on the 3D geometry of the chain links. The results suggest that damage growth can be detected and localised using non-contact underwater AE transducers.

The multimodal and dispersive character of ultrasonic guided waves (UGW) offers the potential for non-destructive evaluation of fiber-reinforced composite (FRC) materials. In this study, a methodology for in situ stiffness assessment of FRCs using UGWs is introduced. The proposed methodology involves a comparison between measured wave speeds of the fundamental symmetric and antisymmetric guided wave modes with a pre-established dataset of UGW speeds and translation of them to corresponding stiffness properties, i.e., 𝐴𝐵𝐷-components, in an inverse manner. The dispersion relations of guided waves have been calculated using the semi-analytical finite element method. First, the performance of the proposed methodology has been assessed numerically. It has been demonstrated that each of the independent 𝐴𝐵𝐷-components of the considered laminate can be approximated with an error lower than 10.4% compared to its actual value. The extensional and bending stiffness properties can be approximated within an average error of 3.6% and 9.0%, respectively. Secondly, the performance of the proposed methodology has been assessed experimentally. This experimental assessment has been performed on a glass fiber-reinforced composite plate and the results were compared to mechanical tensile and four-point bending tests on coupons cut from the plate. Larger differences between the estimated 𝐴𝐵𝐷-components according to UGW and mechanical testing were observed. These differences were partly attributed to the variation in material properties across the test plate and the averaging of properties over the measurement area.

Mooring chains are key parts of floating energy production units, such as floating wind turbines, photovoltaic islands, and floating production-storage-offloading units (FPSOs). These structures are predominantly subject to corrosion and fatigue. Detailed integrity assessment of mooring chains can be challenging due to difficult access, complex geometry, surface conditions (often with the presence of corrosion pits), and weather conditions. Additionally, the presence of marine growth on the surface of the chain links often requires the need of surface cleaning to obtain a detailed assessment of the structural integrity. This paper highlights an experimental investigation of monitoring of corrosion-induced damage in mooring chain links by means of non-contact Acoustic Emission (AE) technique. Accelerated corrosion experiments were performed on a large-scale mooring chain sample recovered from the field. A dedicated experimental set-up was designed to apply accelerated corrosion on two chain links submerged in natural sea-water. An immersion tank, i.e. an instrumented 1000x1000x1200mm3 water tank, was used to submerge the chain links in natural sea-water. The corrosion process was accelerated by means of anodic polarization. Ultrasound signals were continuously measured using two arrays of underwater AE transducers (in the frequency range between 50-450 kHz) placed on two perpendicular planes at a fixed distance from the chain links. Corrosion-induced AE sources were localized on the 3D geometry of the tested links. The variation and evolution of AE parameters extracted from the measured signals were analyzed as a function of testing time. The results of the accelerated corrosion experiment suggest that corrosion-induced ultrasound signals can be detected and monitored by means of non-contact AE. The results of this study may be used for characterization of corrosion-induced AE signals in mooring chain links.

The marine industry is increasingly considering the use of flexible composite marine propellers for their potential to reduce carbon emissions and underwater radiated noise. Given the early stage of development of flexible composite propellers, there are unknowns on their structural degradation. Structural health monitoring (SHM) can provide additional insight into the occurrence and propagation of degradation in these structures. A passive and lightweight method of SHM is the measurement and processing of acoustic emissions (AE) that are induced by different degradation mechanisms. The current research investigates the measurement of AE signals in composite marine propeller blades using embedded piezoelectric sensors. A full-scale glass-fibre polymer composite propeller blade is suspended in a tank filled with artificial seawater. The propeller blade contains 24 embedded piezoelectric sensors that were installed between laminas during manufacturing. Additionally, the tank includes an array of hydrophones for validation of the results. AE signals are simulated on the blade using underwater pencil lead breaks. The measured AE signals are assessed for their amplitude and frequency content. The results demonstrate the feasibility of measuring AE signals in composite marine propeller blades using embedded piezoelectric sensors and hydrophones.

Corrosion-fatigue is considered to be one of the main degradation mechanisms affecting the structural integrity of offshore support structures. This paper presents a feasibility assessment for the detection and monitoring of corrosion-fatigue damage using non-contact acoustic emission (AE). An accelerated corrosion-fatigue experiment was conducted on a S420NL dog-bone specimen. A corrosion-fatigue cell was designed and fabricated to simultaneously apply accelerated corrosion and cyclic loads on the specimen submerged in artificial seawater. A three-electrode electrochemical configuration under potentiostatic control was used to accelerate corrosion. The ultrasound signals were continuously measured using underwater AE transducers (in the frequency range of 50–450 kHz) placed at a fixed distance from the tested coupon. The results of the accelerated corrosion-fatigue experiment suggest that corrosion-fatigue-induced ultrasound signals can be detected with a satisfactory signal-to-noise ratio using non-contact AE sensors. The mean energy of the corrosion-fatigue-induced ultrasound signals was one order of magnitude higher than that of the corrosion-induced signals. The trends of the AE parameters extracted from the AE signals were analysed as functions of the load cycles. The results revealed high potential for the identification and monitoring of corrosion-fatigue damage using the non-contact AE technique.

Naval vessels are valuable assets that are expensive to build and maintain. Predictive maintenance is crucial to enhance efficiency and operability and to extend the service life of these structures. Fatigue is regarded as one of the main damage modes affecting the structural integrity of ship structures. Fatigue cracks can develop at the intersections of stiffeners and other structural elements, without being detected until they substantially grow, introducing a degree of uncertainty in the estimation of the fatigue damage. Acoustic emission (AE) is a passive ultrasound method for the detection and localization of different types of damage. AE is sensitive to crack propagation and can cover large areas, making it suitable for structural health monitoring of ship hulls. However, for accurate fatigue crack detection via AE it is crucial to understand how ultrasound waves interact with structural members i.e. stiffeners and longitudinal frames. Dispersion, scattering, reflection, and multimodality have so far hindered the application of AE in this context. This study aims to assess ultrasound wave behavior in ship structures in the presence of structural elements such as stiffeners and longitudinal/transversal frames. It investigates the ultrasound wave transmission dependence on source frequency and angle of incidence using spectral finite element (SEM) simulations and experimental measurements. The experimental setup includes a 10mm thick steel plate with a stiffener and a longitudinal beam (Figure 1). Pairs of sensors are placed on each side of the stiffener to record the incoming and transmitted waves, and to determine the transmission coefficient. By combining the results of both simulation and experiments, an expression for the transmission coefficient as a function of different frequencies and angles is presented. The results of this study can be used to maximize the coverage of AE monitoring systems and enhance their sensitivity for detection of fatigue cracks on ship structures.

By analyzing the failure mechanisms, crashworthiness characteristics of FW composite tubes subjected to two modes of progressive damage and catastrophic failure are investigated using acoustic emission technique and numerical method. The AE signals of ±45° composite tubes were classified using hierarchical and wavelet transform methods, and based on the realistic and three-dimensional geometrical architecture of tubular structures, the microstructural finite element model was developed using Catia and ABAQUS software. Then deformation patterns and the impression of each mechanism on the crashworthiness characteristics were assessed. Results indicated that fiber breakage and fiber/matrix debonding could likely control the higher percentage of damage. By changing the type of modes from progressive damage to catastrophic failure, the percentage of matrix cracking increases, the fiber/matrix separation decreases, and the failure behavior become dominated by local buckling. Comparing the FE simulation with experimental results, we found the proposed 3D model can reasonably predict the pre-crushing, post-crushing, and material densification.

This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.

Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring technologies for composite tubes experiencing progressive damage were investigated. The challenges associated with quantitative failure monitoring were addressed, and the Genetic K-means algorithm, hierarchical clustering, and artificial neural network (ANN) methods were employed along with other three alternative methods. The impact characteristics and damage mechanisms of composite tubes under axial compressive load were assessed using Acoustic Emission (AE) monitoring and machine learning.Various failure modes such as matrix cracking, delamination, debonding, and fiber breakage were induced by layer bending. An increase in fibers/matrix separation and fiber breakage was observed with altered failure modes, while matrix cracking decreased Signal classification was achieved using hierarchical and K-means genetic clustering methods, providing insights into failure mode frequency ranges and corresponding amplitude ranges. The ANN model, trained with labeled data, demonstrated high accuracy in classifying data and identifying specific failure mechanisms. Comparative analysis revealed that the Random Forest model consistently outperformed the ANN and Support Vector Machine (SVM) models, exhibiting superior predictive accuracy and classification using ACC, MCC and F1-Score metrics. Moreover, our evaluation emphasized the Random Forest model's higher true positive rates and lower false positive rates. Overall, this study contributes to the understanding of model selection, performance assessment in machine learning, and failure detection in composite structures.

Offshore support structures and mooring systems are predominantly subject to corrosion and fatigue. These structures are typically covered with marine growth of various types. Conventional inspection methods for assessment of the structural integrity require access to the cleaned surface of these structures; however, the cleaning process is highly undesirable from the technical, economical, and environmental points of view. This paper highlights research on feasibility assessment of detection and localization of corrosion damage under marine growth using acoustic emission (AE). Experiments were conducted on two carbon steel plates, one baseline sample and one covered with artificially fabricated marine growth. The results of accelerated corrosion experiments suggest that corrosion-induced ultrasound signals can be detected with satisfactory signal-to-noise ratio using non-contact AE sensors. Ultrasound waves passing through marine growth showed around 12 dB drop in amplitude when compared to the base plate. A localization algorithm for corrosion induced-ultrasound signals was successfully implemented.

The aim of this research is to investigate the failure mechanisms of the filament-wound composite tubes under axial compressional loading by using an acoustic emission approach. First, the mechanical properties of ±45°C composite tubes were obtained experimentally. Then, failure due to the buckling phenomenon and crashworthiness characteristics were studied utilizing numerical simulation and experimental methods. Tubes were next simulated in ABAQUS software, and a continuum damage mechanics model was implemented in a progressive framework to assess the failure modes. From the macroscale view, results showed that the damage behavior of composite tubes turned out to be dominated by local buckling followed by a post-buckling field, which is generated by longitudinal cracks along the winding direction. On the micro-scale, the acoustic emission-based procedure based on the wavelet packet transform method was adopted. The hierarchical modeled assessment resulted in the identity of four clusters of AE signals. In GFRP tubes, the fiber breakage and fiber/matrix separation could mostly control the higher percentage of damage and cause to increase the energy absorption. Finally, by comparing the results obtained from micro and macro scales, the local buckling failure mode was attributed to the low content of fiber/matrix debonding in the structure.

Fiber-reinforced composite materials are widely used in the aviation, civil, and shipbuilding industries. Especially the latter two industries are typically dealing with thicker composites. At the same time, in these industries the need for structural health monitoring, to assess degradation and failure, is becoming more prevalent. Acoustic emission (AE) measurement and analysis for damage source localization and characterization can be a useful method for the assessment of structural integrity for these structures. In the case of composite panels, acoustic emissions can propagate in the form of elastic guided waves. The location of the AE source exposes regions in a structure that are subject to degradation. Typical acoustic emission source localization methods assume that the recorded AE signals consist of a single dominant fundamental wave mode. However, with thicker composites, the acoustic emissions may propagate in a multitude of modes. This will complicate the signal processing operations for accurate source localization. This research assesses experimentally how guided wave multimodality influences acoustic emission localization. An acoustic emission source is excited in a thick glass fiber-reinforced plastic (GFRP) panel. Measurements from this excitation are first assessed for their content of higher modes. Source localization is carried out based on dispersion compensation through time-distance domain migration. Different possibilities and combinations of wave modes are considered. The localization error is assessed for each option. The results highlight the added complexity of multimodality and show how the inclusion of multiple modes into the procedure can improve the accuracy of source localization.