Analysis Of Acoustic Emission Signals Research Articles

Mechanical properties and damage characteristics analysis on recycled aggregate concrete with glazed hollow beads after high temperatures by acoustic emission method

This research investigates the effect of high temperature on the mechanical and acoustic emission properties of recycled aggregate concrete with glazed hollow beads (GHB-RAC). Uniaxial compression tests were conducted on GHB-RAC at varying recycled coarse aggregate (RCA) substitution rates and glazed hollow beads (GHB) contents, and acoustic emission (AE) technology was used to monitor the compression-induced damage throughout the testing process. This study delves into the mechanical properties and damage characteristics of GHB-RAC when exposed to fire. The findings reveal that AE characteristic coefficients aptly delineate the three-stage nature of axial compression damage in concrete at temperatures up to 400 °C. However, these stage-specific traits vanish at temperatures exceeding 400 °C. Additionally, through the analysis of AE signals at different temperatures, the compression damage process is elucidated based on the b-value method. The RA-AF analysis indicates that the number of shear cracks exhibits an increasing trend with increasing temperature, and an increment in GHB dosage positively influences the percentage of tensile cracking during loading, with the influence being more significant at 70 % GHB content. The acoustic emission sensing can accurately detect and localise damage. With the increase in temperature, the distribution of AE signals depicting concrete damage is more dispersed and the number of AE signals is more larger. Notably, the incidence of localized damage points diminishes with a higher GHB ratio, indicating the efficacy of GHBs in mitigating thermal damage to the concrete. Furthermore, a damage constitutive model incorporating variables such as temperature, GHB dosage, and recycled coarse aggregate replacement rate was proposed. The model employed AE energy count parameters as the damage indicator. The good fitting results suggest that the mechanics behavior of GHB-RAC under uniaxial compression can be well described by the model. Consequently, the study offers invaluable insights into the damage evolution of post-fire recycled aggregate concrete with GHB admixtures.

Characterization of the damage mechanism in CFRP composites under mode I based on comprehensive analysis of AE signals

This study utilises several methods centred on acoustic emission (AE) waveform characteristics to conduct in-depth analysis of the damage mechanism of composite materials under mode I. Combined with the peak frequency statistics and continuous wavelet transform (CWT) of AE signals, three damage modes of laminated composites during loading are identified. The cumulative energy of three damage modes separated by variational modal decomposition (VMD) is used to characterize their damage evolution process. Based on the findings, delamination and fiber–matrix debonding are proven to be the dominant damage mechanisms for mode I. The comprehensive analysis results from AE's accumulated energy, peak frequency distribution, CWT, VMD, and sentry function can accurately detect the onset time of delamination in laminates. The equivalent crack length is calculated by the load value determined at the initial time of delamination, and the strain energy release rate calculated by the load value and crack length at this time is used as the interlaminar fracture toughness value of mode I. This research method will provide a new idea for the damage tolerance analysis of mode I delamination in laminated structures.

Analysis of acoustic emission signal in the tensile process of weld-bonding joint and its damage model

ABSTRACT This paper analysed the acoustic emission signals acquired during the tensile test of DP780 weld-bonding joints, and the Gaussian equation was used to fit the cumulative hits and the load to establish relevant damage models. The process parameters were designed based on the Box-Behnken Design method, and the wavelet packet was used to decompose the collected AE signals. The results showed that, in the elastic stage, no AE signal emitted, and the AE signal was mainly concentrated in the plastic stage, the hardening stage and the failure moment of the weld nugget or the adhesive layer. At the failure instant of the adhesive layer or the weld nugget, the signal amplitude was the largest, and the failure frequency was mainly concentrated in the middle and low-frequency bands. The failure of the adhesive layer and the weld nugget could be distinguished from the energy distribution diagrams, and the established damage model could reflect the damage state of the specimen. The results proved that the acoustic emission technique can provide support for the non-destructive testing of the weld-bonding joint.

The multifractal characteristics of acoustic emission for composite samples subject to simulated field loading condition

ABSTRACT The composite strata structure is a common geological formation in western China. The study of the failure characteristics of the composite structure is of great help to the stability evaluation of stope and the prediction of coal mine dynamic disasters. In order to study the influence of different hard rock coefficients on the instability characteristics of composite samples, the triaxial tests with progressive varying confining pressure simulating the field loading condition were conducted on eight groups of composite samples with different hard rock coefficients. The bearing capacity of different samples was tested using the RMT-150B triaxial compression equipment, while the DS-5 type eight-channel acoustic emission monitoring system was employed for simultaneous monitoring of acoustic emission parameters. Additionally, the multifractal theory in mathematics is applied to the analysis of acoustic emission signals, exploring the multifractal characteristics of the time series of acoustic emission signals. The results show that the peak strength of the composite sample decreases first and then increases as the hard rock coefficient increases, which is consistent with the trend of the multifractal spectrum parameter Δ f α of the acoustic emission time series. A significant increase in the low-frequency and high-amplitude signal ratio can be used as a precursor to early indicate the instability in composite samples. The dynamic changes of multifractal parameters at different stages of the progressive varying confining pressure can reflect the influence of axial pressure and confining pressure on the crack propagation in the composite samples, and the two parameters at the hydrostatic pressure stage can be used as a rule-of-thumb for screening the testing samples. Simultaneously, the multifractal parameters can early warn the rock failure much more ahead of time than the conventional approach associated with the warning point obtained in the acoustic emission curves, and its head of time can be reaching 0.015 failure progress.

Waveform analysis of acoustic emission signals arising from water absorption of cracked concrete

Waveform analysis of acoustic emission signals arising from water absorption of cracked concrete

Investigating a three-dimensional convolution recognition model for acoustic emission signal analysis during uniaxial compression failure of coal

Acoustic emission predicts coal sample failure, vital for early warnings and monitoring. The study emphasizes using acoustic emission to predict coal sample failure patterns and establish a discriminative model for monitoring. It employs a lightweight 3D convolutional model combining DenseNet with Group Convolution (GC) and the "squeeze-and-excitation" (SE) module. Coal samples from Xinglongzhuang Coal Mine underwent uniaxial compression, and a subset of experiments with prominent features was selected. The resulting acoustic emission parameters were then transformed into spatiotemporal image sequences for input to the model. Successfully identifying hazardous coal damage stages, all four network structures (DenseNet, DenseNet + GC, DenseNet + SE, DenseNet + GC + SE) achieved over 98.29% predictive accuracy in verification samples. The model exhibited a recall rate of over 98.02% for high-risk samples, effectively capturing spatiotemporal information. DenseNet + GC + SE showed a probability distribution focusing on different risk levels. By integrating group convolution and SE modules, this model significantly reduced both model and time complexity while preserving precision, enhancing efficiency. It effectively discerns diverse acoustic emission features through group convolution and evaluates their significance using the SE module. Compared to the traditional C3D neural network, this model greatly reduces complexity and time requirements while improving efficiency in distinguishing between various damage stages.

Effect of SiC concentration on the mechanical properties and fracture modes of SiC/Al composites via cluster analysis of acoustic emission signals

Effect of SiC concentration on the mechanical properties and fracture modes of SiC/Al composites via cluster analysis of acoustic emission signals

Enhancing Reliability and Safety in Industrial Applications: Assessing the Applicability of Energy b-Value to Composites.

This study investigates the fracture behavior of glass fiber-reinforced plastic (GFRP) under various loading conditions using acoustic emission (AE) testing. Using fracture tests and time series analysis of AE signals, parameters such as b-value, improved b-value (Ib-value), and energy b-value (be-value) were examined to understand crack initiation, growth, and structural failure. The stress-strain curve revealed distinct responses during tensile and step loading, and time series analysis highlighted variations in amplitude, AE energy, and Kaiser and Felicity effects. Under tensile loading, the Ib-value exhibited a linear decrease, while step loading introduced complexities, including the Felicity effect. The be-value, incorporating energy considerations, fluctuated, providing insights into micro-cracks and macro-cracks. Statistical analysis demonstrated a consistent decrease in the be-value, emphasizing its potential for long-term monitoring. This study provides a comprehensive technique for assessing composite material fracture behavior, enhancing understanding for critical applications in hydrogen storage vessels and pressure pipes as well as advancing reliability and safety in industrial sectors.

Damage Evolution During Rock Dynamic Compression Revealed by Wavelet Analysis of Acoustic Emission Signals

Damage Evolution During Rock Dynamic Compression Revealed by Wavelet Analysis of Acoustic Emission Signals

Identification of Destruction Processes and Assessment of Deformations in Compressed Concrete Modified with Polypropylene Fibers Exposed to Fire Temperatures Using Acoustic Emission Signal Analysis, Numerical Analysis, and Digital Image Correlation.

This article presents the results of tests conducted to identify the failure process and evaluate the deformation of axially compressed concrete specimens modified with polypropylene fibers (PP). The test specimens were previously stored at ambient temperature and subjected to fire temperatures of 300 °C, 450 °C, and 600 °C. Acoustic emission (AE) signals were recorded during loading, along with force and strain measurements. The recorded AE signals were analyzed using the k-means clustering method. The compilation of the test results made it possible to determine the classes of signals characteristic of different stages of the material failure process and to indicate the differences in the failure mechanisms of specimens stored under ambient conditions and subjected to fire temperatures. Digital image correlation (DIC) measurements were conducted during the strength tests. A numerical model of the material (FEM) was also prepared, and a comparison of the obtained results was carried out.

Tracing fracture damage evolution and identifying damage patterns in cast steel using advanced acoustic emission analysis

The application of advanced acoustic emission signal analysis techniques can facilitate the identification of fracture damage modes in ductile materials and enable the provision of fracture precursor warnings. Given the vast array of acoustic emission signal characteristic parameters, there must be a systematic and comprehensive basis for selection and a deep comparison among different fracture warning indicators. This study uses G20Mn5QT cast steel material fracture acoustic emission monitoring tests under different in-plane constraints as an example. We adopted principal component analysis and k-means++ to select acoustic emission feature parameters and cluster damage modes. Additionally, we conducted a comparative analysis of several fracture precursor warning indicators and influencing factors, including fractal dimensions, b/Ib values, and natural time analysis. Our findings show that shear and tensile fracture damage modes are present in type-I fracture tests when applying the k-means++ cluster to the rise angle-average frequency-energy parameter, but tensile fracture modes dominate. Fractal dimensions, b-values, and Ib-values show a strong trend correlation with the fracture damage evolution process. Natural time analysis can quantitatively provide the moment of fracture precursor warnings, but the sliding window length inversely affects the absolute value sizes. Through acoustic emission signal analysis, our research provides a reference for identifying and examining the evolution of fracture damage in ductile materials and engineering components.

Experimental Study on Mode I Fracture Characteristics of Granite after Low Temperature Cooling with Liquid Nitrogen

Liquid nitrogen fracturing has emerged as a promising technique in fluid fracturing, providing significant advantages for the utilization and development of geothermal energy. Similarly to hydraulic fracturing in reservoirs, liquid nitrogen fracturing entails a common challenge of fluid–rock interaction, encompassing the permeation and diffusion processes of fluids within rock pores and fractures. Geomechanical analysis plays a crucial role in evaluating the transfer and diffusion capabilities of fluids within rocks, enabling the prediction of fracturing outcomes and fracture network development. This technique is particularly advantageous for facilitating heat exchange with hot dry rocks and inducing fractures within rock formations. The primary objective of this study is to examine the effects of liquid nitrogen fracturing on hot dry rocks, focusing specifically on granite specimens. The experimental design comprises two sets of granite samples to explore the impact of liquid nitrogen cooling cycles on the mode I fracture characteristics, acoustic emission features, and rock burst tendency of granite. By examining the mechanical properties, acoustic emission features, and rock burst tendencies under different cycling conditions, the effectiveness of liquid nitrogen fracturing technology is revealed. The results indicate that: (1) The ultimate load-bearing capacity of the samples gradually decreases with an increase in the number of cycling times. (2) The analysis of acoustic emission signals reveals a progressive increase in the cumulative energy of the samples with cycling times, indicating that cycling stimulates the release of stored energy within the samples. (3) After undergoing various cycling treatments, the granite surface becomes rougher, exhibiting increased porosity and notable mineral particle detachment. These results suggest that the cyclic application of high-temperature heating and liquid nitrogen cooling promotes the formation of internal fractures in granite. This phenomenon is believed to be influenced by the inherent heterogeneity and expansion–contraction of internal particles. Furthermore, a detailed analysis of the morphological sections provides insights into the structural changes induced by liquid nitrogen fracturing in granite samples.

Experimental study on fracture and acoustic emission properties of internally cured concrete with super absorbent polymer subjected to high temperature

The three-point bending experiment was conducted on notched beams containing five different SAP contents to investigate the influence of high temperature on the fracture performance of Superabsorbent Polymer (SAP) concrete. Acoustic Emission (AE) technology was employed to monitor real-time crack propagation, and scanning electron microscopy (SEM) was utilized to analyze the micro-pore structure. The experimental results reveal the following findings. After exposure to high temperatures, the peak strength of SAP concrete continuously decreases. Compared to specimens at 20 °C, the strength loss after exposure to temperatures of 200 °C, 400 °C, and 600 °C is approximately 11%, 55%, and 87%, respectively. However, the introduction of SAP can reduce the proportion of strength loss in the concrete. Additionally, SAP improves the toughness of the concrete, with an increase in ductility coefficient ranging from 4.0 to 6.8 times as the loading temperature increases. Similar to the strength variation pattern, the initial fracture toughness of SAP concrete exhibits a significant decrease with increasing heating temperature. The loss in initiation toughness after exposure to temperatures of 200 °C, 400 °C, and 600 °C is approximately 11%, 42%, and 85%, respectively, while the loss in unstable fracture toughness is 11%, 46%, and 74%. Based on the experimental results, this study proposes two temperature-dependent fitting models for the peak strength and fracture toughness of SAP concrete, which are compared and validated with the experimental results. SEM analysis reveals a loose and porous internal structure in SAP concrete. Through the analysis of AE signals at different temperatures, the fracture process is elucidated based on the b-value method. Moreover, RA-AF analysis indicates that an increase in temperature reduces the proportion of tensile-type cracks in specimens with the same SAP content. From the temperature loaded results spanning 20 °C–600 °C, the proportion of tensile cracks decreases from 80% to 50%, indicating a transition in the failure mode from tension to shear. However, this trend is not significantly affected by the temperature increase, suggesting that the failure mode of the specimens is primarily determined by the internal pore structure of the concrete rather than the temperature load. The research findings provide experimental evidence for the application of SAP concrete in engineering projects.

Improved fault diagnosis method of electric gate valve in nuclear power plant

The vibration signal (VS) analysis and acoustic emission signal (AES) analysis methods are the common effective diagnostic methods in valve fault diagnosis (FD)(Classify fault types). However, these two methods are disadvantaged by the surrounding environment and noise signals produced during acquisition. To obtain an improved FD effect of an electric gate valve (EGV) in nuclear power plant, the two previous methods are ameliorated by a noise reduction processing, then the signal feature extraction is performed. The FD and degree evaluation are carried out for the common fault types of the EGV taken as object. In this paper, three-phase unbalanced faults (Acquisition of VS), packing damage faults (Acquisition of VS) and internal leakage faults (Acquisition of AES) are taken as examples. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm composed with Fast Fourier Transform (FFT) and the SAEU3H series digital acoustic emission detection system (SAEU3H SDAEDS) are used to noise reduction and extraction of parameters of the VS and AES, respectively. Then, the extracted feature parameters are brought into the Bi-directional Long Short-Term Memory (Bi-LSTM) deep neural network for FD and fault degree evaluation (Assess the severity of failure occurrences). The experimental results show that the method adopted in this paper has high FD accuracy and low fault evaluation error.

Cluster analysis of acoustic emission signals for the damage pattern recognition of polymer concrete

Polymer concrete (PC) is a composite material that consists of mineral fillers (gravel, sand) and a polymer binder whose role is to replace the cement hydrate binders. Its properties such as mechanical strength, adhesion, etc. are improved to a great extent by polymer replacement. In this study, acoustic emission (AE) is used as a nondestructive tool to monitor the mechanical behavior of various PC samples subjected to three-point bending. In order to identify the acoustic signatures related to micro-cracks created within the polymer matrix and the different interfaces, experiments were first performed on manufactured dual-phase PC samples. Parameters related to the created micro-structural changes were then extracted based on different physical and signal processing considerations before being clustered using the time and frequency characteristics of the collected signals. After having carried out the same clustering approach on the three-phase PC (epoxy, sand, gravel), the present work shows that a new clustering of the AE signals is possible based on the wavelets of the AE hits processed as images. High classification accuracy was then obtained using the time–frequency characteristics of AE hits and a transfer learning technique.

Clustering analysis of acoustic emission signals in the monitoring of stone monuments: case of the freeze‒thaw deterioration of tuffs

Acoustic emission (AE) technology is a promising technique for monitoring cultural monuments due to its characteristic ability to reflect status changes and perceive the development process of deterioration and damage even before their visual appearance. This study was established on the motivation of providing basic data and a methodology that can improve the signal processing, characteristics analysis and classification for the AE technique in the long-term in-situ monitoring of deterioration processes, starting from the freeze‒thaw deterioration of tuff monuments at the Chengde site. AE monitoring was carried out with an indoor freeze–thaw deterioration experiment. As a result, a set of procedures and related methodology is proposed based on the hit-based AE waveform parameters for denoising and classification of monitored AE signals by applying hierarchical cluster analysis, k-means clustering, distribution statistics, etc. The clustering results show that some signals may indicate deterioration and signals with certain characteristics are more likely to occur at a particular deterioration phase. Signals characterized by the significant absolute energy (ABE) are presumed to be related to the propagation of cracks to the outer layer. Signals characterized by a higher indirect parameter RA (Rise time divided by peak amplitude) value may connect with the opening/closing of microcracks in the earlier phase of deterioration prior to the exposure of visible surface cracks. The peak frequency (PF) is likely to decrease as the deterioration proceeds.

Material recognition for fault diagnosis in machine tools using improved Mel Frequency Cepstral Coefficients

Identification of material type in structural cracks is crucial for fault diagnosis and structural integrity assessment in multi-component operation systems. In the current literature, acoustic emission (AE) signal analysis has been extensively explored as an effective method for crack monitoring and fatigue damage assessment. Most existing research on crack diagnosis is primarily focused on determining the crack formation in structures using the AE signals corresponding to known materials. However, the material recognition at any given cracking conditions using AE signals has lagged behind, hindering fault detection and damage repair in complex and multi-material structures. This research investigates the feasibility of the synergistic use of Mel Frequency Cepstral Coefficients (MFCC) and K-nearest neighbor (KNN) algorithms for identifying the material type in any arbitrary cracking processes. In the proposed method, MFCC and first-order differential MFCC parameters are extracted from the real-time AE signals. Material-specific cracking characteristics are identified and tuned for material recognition using a KNN classifier. The proposed method is validated using the AE signals of different types of materials acquired under uniaxial tension, and its effectiveness in identifying tool breakage is evaluated through case studies in the milling process. The result indicates that the recognition accuracy of up to 96.21 % can be achieved in tool breakage detection in milling operations by extracting material-specific fracture characteristics.

Evaluation of cracking damage in electrode materials of a LMO/Al-Lix lithium-ion battery through analysis of acoustic emission signals

Damage due to the internal degradation of electrode materials in lithium-ion batteries (LIBs) during charge-discharge cycles can cause capacity fading and safety issues. Therefore, it is essential to assess damage to LIBs using nondestructive methods. In this study, the relationship between the damage mechanism and acoustic emission (AE) activity of a commercial lithium manganese oxide (LMO)/Al-Lix alloy battery is systematically investigated. Microstructural observations of the electrodes revealed that the damage mechanisms of the LMO cathode and Al-Lix anode during accelerated charge-discharge were identical to those of microcracking. Energy, amplitude, duration, rise time, and peak frequency were considered as possible AE parameters for real-time damage detection. The generation of AE hits was prominent during the later stages of the charging and discharging processes. The AE signals produced by damage to the Al-Lix alloy anode during charging had a lower amplitude (0–60 dB) and energy (0–10 aJ). In comparison, those produced by damage to the LMO cathode during charging had a relatively high amplitude (100 dB) and energy (120 aJ). Additionally, the energy levels and amplitudes of the AE signals obtained during discharging decreased with an increasing number of cycles, indicating that they can be used as indicators for evaluating the damage progression of the primary, secondary, and tertiary cracks in LMOs. The AE technique offers the possibility of monitoring and evaluating the damage to the anode and cathode of a commercial full cell separately, as well as predicting the remaining capacity of the battery by monitoring the AE hits.

Torsional behavior of glass fiber-reinforced polymer composite: polymer matrix effect and acoustic emission signal analysis

Torsional behavior of glass fiber-reinforced polymer composite: polymer matrix effect and acoustic emission signal analysis

Assessment of fracture process of engineered cementitious composite (ECC) by time-frequency analysis of acoustic emission signals

Engineered cementitious composite (ECC), also known as strain-hardening cementitious composite, exhibits high ductility and high toughness. The high ductility and high toughness of ECC are associated with finely-tuned matrix cracking, fiber rupture, and fiber-matrix debonding. This study investigates the fracture process of ECC through a time-frequency analysis of acoustic emission (AE) signals. The frequency characteristics of individual AE hits were used to evaluate different types of damage throughout the fracture process. The evolution of damage was assessed, and the AE energy was calculated. The effects of matrix flaw and fiber content on the fracture process were investigated. The test results revealed that different damage stages featured different AE frequency characteristics, which were used to classify damage types. ECC showed high energy absorption along with the high toughness. This research enhances the understanding of the fracture process of ECC and advances the capability of assessing the damages.