Acoustic Emission Signals Research Articles

Damage and failure assessment of banana/ramie/epoxy composites using acoustic emission monitoring

In recent years, Composite materials that include natural fibers have played a significant role in aerospace, automotive industries, and other engineering applications. Due to its enhancing properties, the usage of composites has owing to an increase in day-to-day life. This research aims to develop a sustainable alternative material pronounced for environmental sustainability for surpassing synthetic fibers. Therefore, real-time monitoring and detection of the damage mechanisms in natural fiber composites are indispensable. In this regard, the acoustic emission (AE) technique lends itself to identifying the microscopic damage mechanisms in composite materials. Compression tests were performed in banana/ramie/epoxy composite specimens, and following consequences, the AE parameters (frequency, amplitude, counts, duration, energy rise time, etc.) were recorded. The novelty of this article is to detect the damage evolution and failure mechanisms of banana/ramie/epoxy composites under quasi-static compression with AE signal analysis for the first time. Findings showed that the stacking of hybrid reinforcing banana and ramie fiber improves the stress distribution, leading to enhanced load-carrying capacity (30.56 %), energy absorption (26.78 %), and stiffness (35.24 the compressive strength reached the maximum strength of the banana/ramie/epoxy composites%).This increase represents a substantial 30 % increase in AE energy absorption prior to failure, suggesting that the composite specimens experienced significant damage or extensive failure mechanisms Further, the AE parameters also showed that the AE energy under compression is relatively low and that frequency disperses between 70 and 150 kHz.

Failure behaviour of various rocks coated new thin spray-on liner with different curing time under uniaxial compression: Insights from AE and DIC

Thin spray-on liners (TSL) is increasingly popular in underground mine support operations. In this study, a new type of TSL material is proposed, and its macro-micro physical and mechanical properties are characterized. A systematic investigation on the deformability behaviour of six rocks (i.e., coal rock, soft red sandstone, marble, hard sandstone, granite, and limestone) covered TSL materials with different curing ages (i.e., 0-day, 3-day, 7-day, 14-day, 21-day, and 28-day) under uniaxial compression circumstance is performed using acoustic emission (AE) and digital image correlation (DIC) methods. The results show that TSL improves the peak strain of each rock sample, except hard sandstone. The impact of TSL on the peak compressive strength of coal rock, soft red sandstone, and marble is nearly negligible, while it diminishes the strength of hard sandstone, granite, and limestone. As the TSL ages, the peak strength and elastic modulus of limestone initially increase and then decrease, reaching the minimum at 14-day of age, with values 89.81 MPa and 23.13 GPa respectively. However, for other rock samples, the effect of TSL age is not significant. The AE signal produced inside the rock diminishes during the elastic-plastic stage, while the ratio of shear cracks increases upon failure of each rock specimen. The macroscopic failure modes of coal rock and limestone exhibit prominent axial splitting characteristics, whereas marble and hard sandstone undergo mixed failure processes involving shear, tension, and shear-tension. The test data highly correspond to the macroscopic fracture characteristics. The research findings can serve as guidance for TSL support in both soft and hard rock scenarios.

FE-based machine learning model for predictive damage assessment in bonded composite joints via acoustic emission

This study systematically evaluated the mechanical performance of bonded composite single-lap joints with varying overlap lengths under tensile load. By integrating Acoustic Emission (AE) signals with Finite Element (FE) modeling, this study established a predictive machine learning model to enhance the understanding and prediction of failure mechanisms. Firstly, AE signal features were extracted and utilized as inputs for a hierarchical clustering model to classify AE signals into matrix cracking, fiber breakage, and adhesive debonding. AE signals classified as adhesive debonding were further analyzed by accumulating counts, amplitude, and energy. A cohesive zone-based Finite Element (FE) model was developed to simulate progressive debonding damage in the adhesive layer. After calibration with experimental results, various physical quantities such as stress, the damage initiation indicator, and the stiffness degradation indicator were extracted for correlation analysis. The experiments and simulations unravel the missing relationship between the AE and FE data. A strong correlation was observed between the cumulative count and cumulative amplitude with the damage initiation indicator, and between cumulative energy with the damage propagation indicator. Finally, based on the disclosed relationship between AE and FE data, a Support Vector Regression (SVR) model was established to predict the damage indicator. The developed model accurately predicted testing datasets within low absolute error ranges, underscoring its high predictive accuracy. Comprehensive stress analysis proposed a quantitative damage initiation indicator of 0.32 for structural fractures and a damage indicator of 0.12 for allowable stress levels. Consequently, combining AE techniques with FE modeling provided an effective tool for evaluating damage evolution in bonded composite joints.

Investigation on mechanical property and failure mechanism of the carbon/glass hybrid laminate bolted joints using acoustic emission

In this study, tensile tests and acoustic emission (AE) monitoring were performed on single lap specimens of carbon/glass hybrid laminates with different carbon fiber layer angles. The results indicated that the carbon fiber layer angle not only affects the bearing strength and bearing stiffness of hybrid laminates, but also influences the macroscopic extension direction of damage. The study also found that carbon fiber and glass fiber damage exhibit different centroid frequencies. Building on these findings, the analysis identified the primary fiber type involved in fiber damage to different hybrid fiber laminates. Additionally, it was noted that the friction generated between the bolt and the laminate under load triggers AE signals with a high centroid frequency.

Study on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression in high-performance concrete with full aeolian sand

The issues of environmental degradation due to natural sand overextraction are becoming increasingly severe. In desert regions, utilizing aeolian sand as a substitute for natural sand in construction projects facilitates the resource economization and the effective management of desertification. The concrete industry is expected to significantly contribute to this objective, as high-performance concrete with full aeolian sand (FA-HPC) has demonstrated excellent mechanical properties. However, current researches have yet to establish a rational damage evolution law for uniaxial compression in FA-HPC from the perspective of damage characteristics. In this study, uniaxial compression tests were conducted on FA-HPC with different mixture proportions. Simultaneously, the stress-strain behavior was examined, and acoustic emission (AE) signals were collected and localized. Based on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression, regression analyses of the stress-strain and AE cumulative ringing count for FA-HPC were performed. Damage evolution model for effective stress and damage variable were established and validated with experimental data to confirm their applicability. The scale and shape parameters of the model were predicted using the mixture proportions as independent variables. The results show that the rationality of the damage evolution law assumption was validated through AE source localization. The goodness of fit values for damage variable and effective stress equation range from 0.87 to 0.98, with the ascending segment and the vicinity of the peak point closely matching the experimental data, indicating that the Weibull distribution function-based stochastic damage model for uniaxial compression is applicable for FA-HPC. The predicted values of the damage evolution model parameters based on the mixture proportions align well with the measured values, achieving a goodness of fit of 0.99, thereby guiding the study of FA-HPC's compressive characteristics prior to the formation of primary cracks. Notably, there remains a need to propose corrective equations to enhance the correlation between regression curves and experimental data in the late failure stages of FA-HPC in future research, achieving precise predictions by considering the interactions of multiple factors.

Variable frequency selective structure (CFRP/Cu/CFRP) micro blind hole laser high-quality drilling method guided by acoustic emission technology

The laminated structure of carbon fiber reinforced plastic (CFRP)-Cu-CFRP is an important structure for aerospace radome. The blind holes manufacturing is core process, but there are technological challenges in achieving the exposure of a 10 μm thick copper foil at the bottom of the blind hole. The exact position of the 10 μm thick copper foil within the material’s internal space is unknown, and the uneven heat accumulation and conduction can easily cause burning and damage to the ultra-thin copper foil. To date, there is no publicly accessible literature that documents the execution of this specific process. In this paper, novel variable inner diameter spiral scanning guided by Cu-CFRP interface accurately extracted using cosine similarity (CS) algorithm is proposed to obtain a blind hole with near-zero ablation damage. The similarity of acoustic emission signals generated by laser interaction with Cu and CFRP, as well as thermal accumulation and conduction behavior under different scanning radius, are described. The high-quality blind holes have been successfully drilled, with a large area copper foil exposed at the bottom, while the surface of the copper foil remaining intact without any damage or heat affected zone. Compared with traditional drilling method, the average exposed copper foil area at the bottom of the blind holes is increased by 238.4 % (from 0.464 mm2 to 1.570 mm2). And the average taper of blind hole is reduced by 41.7 % (from 0.470 to 0.274). This method can effectively increase the area of exposed copper foil and reduces the taper of blind holes, improve the welding strength and conductivity of wires in aerospace radomes, thereby enhancing the stealth performance and operational reliability of fighter jets.

Study of Acoustic Emission Signal Noise Attenuation Based on Unsupervised Skip Neural Network.

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.

Fatigue mechanical properties and Kaiser effect characteristics of the saturated weakly cemented sandstone under different loading rate conditions

Weakly cemented sandstone (WCS) is a unique rock type widely distributed on the surface. Environmental factors such as groundwater and stress variations easily influence its fatigue mechanical properties and fracture characteristics. To design and evaluate the long-term stability of surrounding rock support in tunnel excavation and underground resource mining projects, investigating the fatigue mechanical properties and acoustic emission (AE) response characteristics of saturated WCS under different loading rates is of great practical and theoretical significance. This study employed experimental techniques such as X-ray diffraction (XRD), nuclear magnetic resonance (NMR), and natural water absorption tests to investigate the mineral composition, pore size, and connectivity characteristics of WCS. The multi-level cyclic loading-unloading tests (MCLU) combined with the AE system were conducted on dry and saturated WCS specimens at different loading rates. The results reveal that the deformation modulus of these specimens initially increases and then decreases under cyclic loading conditions. Water significantly influences the fatigue strength and deformation resistance of sandstone. As the loading rate increases, the range of RA values broadens, accompanied by a marked increase in the number of AE signals with high RA values. Saturated sandstone specimens are more prone to developing macroscopic shear fracture surfaces. Water has a more substantial effect on the stress distribution ranges corresponding to the response of the Kaiser effect in WCS than loading rates. The capacity of the Kaiser effect to indicate the extent of rock damage is intricately linked to the progression of internal micro-cracks. When internal damage surpasses the critical value of the Kaiser effect memory damage, the accelerated propagation of shear cracks becomes pivotal in the internal damage of the sandstone. It seems that the presence of water within the interior of the rock may facilitate the dissolution of K-feldspar in WCS, which could result in the formation of kaolinite, which will be further transformed into illite. The hydration expansion of illite may further exacerbate the deterioration effect of the mechanical properties of WCS.

Feasibility assessment of non-contact acoustic emission monitoring of corrosion-fatigue damage in submerged steel structures

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.

New evaluation method for defects propagation in toroidal roller bearings using dominant frequency of acoustic emission

ABSTRACT The increased use of roller bearings in recent years has made it vital to develop techniques for Condition Monitoring (CM) of these valuable assets. Toroidal Roller Bearings (TRB) are one of the recent inventions that has revolutionised some industries because of its unique design. In this study, newly produced TRBs were tested using Acoustic Emission (AE), vibration and temperature, on a specially designed test rig. The AE signal was recorded, processed, and analysed using FFT-based methods to transform from the time domain to the frequency domain, and to specify the Dominant Frequency (DF) of each hit in the signal. The result of the analysis was a layout on the time domain of the DFs of all AE hits along with RMSAE and other parameters, this layout was called DF map. The test was divided into three phases, and the DF bands were divided horizontally into three band groups. The aim is to establish a correlation between the phases and DF bands. Knowing the type of defects that are typical for each phase in the test provided insights to what are the DFs of that type. Promising results can be achieved using this measuring method on other types of bearings.

Identification of Stability Domains for Flow Parameters in Fused Filament Fabrication Using Acoustic Emission

In Fused Filament Fabrication (FFF), the state of material flow significantly influences printing outcomes. However, online monitoring of these micro-physical processes within the extruder remains challenging. The flow state is affected by multiple parameters, with temperature and volumetric flow rate (VFR) being the most critical. The study explores the stable extrusion of flow with a highly sensitive acoustic emission (AE) sensor so that AE signals generated by the friction in the annular region can reflect the flow state more effectively. Nevertheless, the large volume and broad frequency range of the data present processing challenges. This study proposes a method that initially selects short impact signals and then uses the Fast Kurtogram (FK) to identify the frequency with the highest kurtosis for signal filtration. The results indicate that this approach significantly enhances processing speed and improves feature extraction capabilities. By correlating AE characteristics under various parameters with the quality of extruded raster beads, AE can monitor the real-time state of material flow. This study offers a concise and efficient method for monitoring the state of raster beads and demonstrates the potential of online monitoring of the flow states. Conflict of Interest Statement The authors declare no conflicts of interest.

Insight into wave propagation in polyimide films and resistive grid sandwich structures towards a hybrid monitoring of hypervelocity impact

Micro-Meteoroid and Orbital Debris pose a significant threat to the safe operation of orbiting spacecraft, potentially leading to mission failure in space exploration. Quantitative characterization of hypervelocity impact (HVI) is crucial to ensure the safety and successful completion of on-orbit missions. Firstly, this study designed a three-layer sandwich structure of polyimide film with orthogonally laid resistive wires, combined with piezoelectric and resistive wire sensors, for the simultaneous acquisition of acoustic emission (AE) signals generated by HVI and measurement of perforation dimensions. Secondly, a semi-analytical finite element (SAFE) analysis of wave dispersion properties in the periodic sandwich structure is conducted with Bloch’s theorem, together with a hybrid model based on three-dimensional smoothed particle hydrodynamics and finite element methods (SPH-FEM) to comprehensively understand the AE waves and damage characteristics induced by HVI. The resulting anisotropic wave propagation characteristics with SAFE and SPH-FEM are closely matched. Thirdly, a time delay-multiplication (TDM) imaging algorithm considering wave velocity anisotropy is proposed for accurate real-time “visualization” of HVI locations. Lastly, correlations are established between projectile and perforation dimensions. The proposed algorithm for HVI multi-parameter quantification and damage detection helps evaluate the space HVI environment and HVI-induced damage to spacecraft.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Acoustic emission and multiscale computation-guided tensile damage identification in woven composite laminates at cryogenic temperatures as low as 20 K

For laminated composite structures as key components of storage tanks serving at cryogenic temperature, it is crucial to identify the damage mechanisms for evaluating their mechanical properties and guiding structural design. In this work, the cryogenic tensile damage behavior of a thin-walled woven composite laminate was investigated through the acoustic emission (AE) monitoring and multiscale finite element (FE) computation at typical low temperatures of 153 K, 77 K and 20 K. We first established temperature-dependent constitutive laws for the microscale and mesoscale constituents of such composites based on experimental data, followed by the development of a hierarchical computational framework for modeling multiscale damage characteristics at different low temperatures. A fiber-optic acoustic emission measurement system was constructed to provide online monitoring of tensile damage of the woven composite laminates at cryogenic temperatures as low as 20 K. The comparations were made between the predicted cryogenic damage characteristics and in-situ AE signal analysis, assisted by scanning electron microscope (SEM) observations. The computed cryogenic damage evolution closely matched the AE signal identification results. The results indicate that fiber breakage and matrix cracking are the dominant cryogenic damage modes, and that the different low temperatures exert significant effects on the properties of the epoxy matrix, yarns and composite laminates. The combination of the AE monitoring system and the computational scheme provides a valuable tool for evaluating structural integrity and guiding the microstructural design of composite laminates used in cryogenic environments.

Damage source localisation in complex geometries using acoustic emission and acousto-ultrasonic techniques: an experimental study on clear aligners

Passive non-destructive evaluation tools such as acoustic emission (AE) testing and acousto-ultrasonics (AU) approach present a complex problem in damage localisation in complex and nonhomogeneous geometries. A novel AU-guided AE frequency interpretation approach is proposed in this research work which aims at overcoming this limitation. For the experimental evaluation, the damage sources from a geometrically complex clear dental aligners are tested under cyclic compression load and their origins are evaluated. Despite the rapid worldwide diffusion of the clear aligners, their mechanical behaviour is poorly investigated. In this work, the frequency characteristics of the artificially simulated stress wave, generated from different dental positions of the clear aligners, are studied using the AU approach. These frequency characteristics are then used to analyse the AE signals generated by these aligners when subjected to cyclic compressive loading. In addition, the time domain characteristics of the AE signals are studied using their Time of Arrival (ToA). The Akaike Information Criterion (AIC) is used to estimate the ToA. These frequency and time domain characteristics of the AE signals are used to estimate the local damage origin in the clear dental aligners. This will help in identifying localised damage sources during the usage period of the aligners. Experimental results revealed significant damages in the left maxillary premolar and right maxillary third molar of the aligners.

A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

Investigating the simulation test and acoustic emission characteristics of structural-control type rockbursts in deep underground environments

Deep underground projects are in a complex in-situ stress environment, and rock burst disasters induced by the hard and brittle failure of the rock mass are prone to occur around the tunnel openings. In this study, we aim to investigate the impacts of geological weak surfaces in rock masses on rockbursts and the spatiotemporal evolution of microcracks during the development of rockbursts. To achieve this objective, two sets of cubic specimens containing a circular through-hole were designed. One set of specimens had prefabricated unfilled cracks around the openings. Subsequently, the rockburst development process within the deep tunnel was replicated by subjecting both groups of specimens to identical gradient loading using a true triaxial test system. During the test, a micro camera was used to record the failure of the borehole wall in real-time, and an acoustic emission monitoring system was utilized to capture the stress waves released during structural damage. Finally, a multi-level synergistic analysis of the rockburst damage mechanism was carried out based on the macro-imaging data and micro-acoustic emission data. The results show that failure in high-stress environments within tunnels mainly includes microcrack initiation, particle ejection, crack propagation, local cracking, slabbing spalling, damage penetration, and rockburst damage. The time–frequency domain information of acoustic emission signals is highly perceptive and representative of the structural damage state of the specimens. The mutation characteristics observed in time-domain parameters, such as acoustic emission amplitude, event rate, ringing, and cumulative energy, correspond to drastic alterations in the internal structure of the rock. The distribution characteristics of frequency-domain parameters, such as acoustic emission peak frequency, dominant frequency energy, and frequency centroid, reflect different crack scales and damage modes. The diffusion of frequency centroid indicates that the damage and failure patterns within the rock are evolving towards a more complex direction. The energy magnitude of acoustic emissions represents the damage intensity during the development of rockburst. The root causes of induced structural-control type rockbursts in deep hard brittle rock masses are the combined effects of localized high-stress concentrations and large-scale discontinuous geological structures, such as natural joints, fissures, and structural planes. The naturally occurring large-scale through-type geological weak surfaces within the rock mass reduce the strength of the surrounding rock, alter the location of stress concentration, and change the initial damage characteristics. Moreover, they promote the evolution of rockbursts, thereby increasing their destructive intensity.

Unsupervised weathering identification of grottoes sandstone via statistical features of acoustic emission signals and graph neural network

Weathering features of sandstone heritage can be recognized by using artificial intelligence (AI) based surrogate models, and most models perform classification tasks for types based on precise labels. But there are lack of prior validated knowledge of the weathering or untagged historical data for complex weathering conditions in many cases. To this aim, a unsupervised graph neural network (GNN) based on the statistical features of the acoustic emission (AE) signals is constructed. Firstly, taking unweathered sandstone as a reference, we define 4 weathering levels of sandstone ranging from I to IV based on pore indicators. We selected 11 statistical features that are high correlated with pore of sandstone. Then, this GNN is constructed and trained by 2880 sets of statistical measured AE signals. Compared with AEs, LOF and IF models, GNN achieves the best identification performance among the four evaluation criteria. Each iteration of the GNN network is fitting the feature information of the signals and their neighbors. By data dimensionality reduction techniques, when the GNN stops iterating, it will be easy to distinguish unweathered AE signals from weathered one by comparing the reconstruction error of each signal. Furthermore, when the nearest neighbor’s k gradually increases, the AUC of GNN also gradually increases and then tend to stable when k equals to 50–100. While the hidden layers of the network aggregates less information about the neighborhood features of the signals and cannot distinguish significantly between unweathered and weathered signals when the value of k is small. As the depth of the network deepens, the feature values between signals become more and more similar, their reconstruction errors in the output layer of the network to become more similar, making it difficult to distinguish unweathered AE signals from weathered AE signals via GNN. Meanwhile, GNN adopts more AE features and considers the similarity between each features. This can greatly eliminate various errors caused by wave velocity measurement, greatly improving the robustness of AE detection. Hence, the GNN model presented addresses the limitations of relying solely on P-wave velocity measurements to assess the degree of sandstone weathering at stone cultural heritage.

Acoustic emission-based weld crack leakage monitoring via FGI and MCCF-CondenseNet convolutional neural network

Online monitoring of weld crack leakage in pressure pipelines of nuclear power ship based on acoustic emission (AE) technology is of great significance for maintaining the safe and stable operation of the system. However, most of the current leakage studies are conducted through artificially designed pipeline hole types, which deviate from the actual crack morphology and are weakly online, with low identification accuracy and slow monitoring speed. Therefore, a convolutional network of FGI and multi-scale channel information cross fusion based on AE technology is proposed in this paper. First, the FBank feature of the AE signal of pipeline weld leakage are extracted. On this basis, the Gini Index (GI) preference feature is used to filter the useless information in the FBank feature. Then, a multi-scale channel information cross fusion module is designed to improve the feature learning ability of the network through the interaction and fusion of different channel information. Finally, the superiority of the proposed FGI feature extraction method and the effectiveness of the proposed multi-scale channel information cross fusion CondenseNet (MCCF-CondenseNet) convolutional neural network are verified by the pipeline leakage AE monitoring experiments under three crack morphologies. The results show that the identification accuracy of the proposed method is as high as 96.42 %, and the identification speed is significantly faster than other state-of-the-art approaches under the premise of ensuring the identification accuracy. This work provides a new method for the online leakage monitoring of nuclear power pressure pipelines, and has important supporting significance for the online leakage monitoring of other large and complex equipment.