Acoustic Emission Signals Research Articles

Frictional process, AE characteristics, and permeability evolution of rough fractures on Longmaxi shale with specific roughness under water injection

Studying the frictional process, acoustic emission (AE) characteristics and permeability evolution of fractures under water injection is fundamental for proposing theories and methods to control the stability of geological structures. In this work, the shear-flow test was conducted on Longmaxi shale fractures with different specific roughness to analyze the frictional deformation and mechanical properties of shale fractures during the frictional slip process, and the influence of effective normal stress and surface morphology was investigated. AE monitoring technology was also utilized to reveal the AE evolution during the frictional slip process. Based on the AF/RA values, AE signals were subjected to cluster analysis using the Gaussian Mixture Model (GMM) to distinguish and quantify the crack types and proportions during frictional slip. Combining the development of cracks and considering the ratio of worn area, a theoretical analytical expression was established to reasonably describe the friction-damage evolution, classifying the friction-damage during the frictional slip process into tensile and shear damage, respectively. Meanwhile, the permeability evolution of Longmaxi shale fractures was also studied in detail. A theoretical model capable of describing and predicting the permeability evolution was proposed under the condition that the permeability evolution and damage process of Longmaxi shale fractures correspond to each other. Finally, the interaction between asperities on shale fractures with different surface morphologies during the frictional slip process was discussed, and the influence mechanism of surface morphology on permeability evolution was revealed.

Machine Learning-Enabled Acoustic Emission Signal Processing for real-time crack growth monitoring in heterogeneous structures

Heterogeneous structures pose unique challenges for structural health monitoring, demanding advanced techniques for early detection of crack propagation. Acoustic Emission (AE) is a non-destructive testing technique that relies on the detection and analysis of stress-induced high-frequency acoustic waves. It is commonly used for identifying and monitoring the development of cracks, defects, or other structural anomalies. However, raw AE data is often noisy and contains background noise and interference, which make them not suitable for reliable real-time crack monitoring applications. This study integrates AE data with machine learning algorithms to provide a proactive and accurate system for continuous crack propagation monitoring. The performance of the proposed approach for features extraction to derive relevant information from the AE signals, data labeling for training machine learning models, and the deployment of these models for real-time monitoring, are discussed.

Acoustic emission monitoring for engineered barriers in nuclear waste disposal

To safely dispose of nuclear waste in underground facilities, engineered barrier systems are needed to seal shafts and galleries. The material used in these barriers must be adapted to the host rock parameters. Shrinking and cracking must be avoided to provide a barrier with almost zero permeability. For repositories in salt rock environments, several types of salt concrete (SC) are used. Within the project SealWasteSafe, we compared the performance of an innovative alkali-activated material (AAM) with standard SC in their hydration and hardening phase. To monitor the microstructural changes within the two materials SC and AAM, acoustic emission (AE) signals have been recorded for up to ~250 days on 340-liter-cubic specimens. The phenomenon of AE is defined as the emission of elastic waves in materials due to the release of localized internal energy. Such energy release can be caused by the nucleation of micro-fracture, e.g. in concrete while curing or when exposed to load. The occurrence of AE events gives first rough indications of microstructural changes and potentially occurring cracking and thus, provides insights for structural health monitoring (SHM). The results show, that for the first 28 days after casting, less AE activity was detected in the AAM compared to SC. After 61 days, in the AAM material, the number of AE events exceeded those observed in the SC. However, the majority of the AE detected in AAM was related to surface effects, and not to microstructural changes within the matrix. Additionally, the source location analysis indicated, that despite lower activity in SC, we observed some clustering of the events. In contrast, in AAM, the activity inside the specimen is randomly distributed over the whole volume. The monitoring results help to estimate the material’s sealing properties which are crucial to assess their applicability as sealing material for engineered barriers.

Noise recognition of moving parts in a sealed cavity based on the fusion of recognition results and high-dimensional mapping

The detection and identification of noise from moving parts inside a sealed cavity is crucial for ensuring the reliability of sealed equipment. However, traditional noise recognition methods struggle to meet the stringent demands for high detection accuracy. Inspired by the idea of ensemble learning, this paper proposes a noise recognition method that combines recognition results with high-dimensional mapping to enhance the recognition of noise. Firstly, a built noise identification experimental system is used to collect signals. Then, features are filtered and extracted based on acoustic emission principles and signal properties. Ultimately, a new fusion method is devised incorporating recognition results as new features into the original dataset and designing multiple layers of single algorithms based on their individual strengths to enhance the feature extraction capabilities of the algorithm. In the first layer of the fusion algorithm, CatBoost learns from the original dataset and incorporates its recognition results into the dataset. XGBoost then trains on the new dataset as the training set. Finally, the sparse output matrix generated by XGBoost is input into a logistic regression (LR) algorithm for training and prediction. The proposed method is verified by experiments on datasets and the results show that the accuracy of this method is higher than that of a single recogniser. It also performs better than current mature stacking fusion methods and mapping-based fusion methods. This fusion approach is of great significance for improving noise recognition accuracy and for innovating fusion methods.

Combined Passive and Active Ultrasonic Stress Wave Monitoring of Concrete Structures: An Overview of Data Analysis Techniques and Their Applications and Limitations

Combined passive [or acoustic emission (AE)] and active ultrasonic stress (US) wave monitoring has been shown to provide a more holistic picture of ongoing fracture processes, damage progression, as well as slowly occurring aging and degradation mechanisms in concrete structures. Traditionally, different data analysis techniques have been used to analyze the data generated from these two monitoring approaches. For AE data analysis, for instance, signal amplitudes, hit rates, source localization, and b-value analysis have been used to detect and locate cracking. On the other hand, amplitude tracking, magnitude squared coherence (MSC), and coda wave interferometry (CWI) are examples that have been employed for US data analysis. In this presentation, we explore these data analysis techniques and show where their respective applications and limitations might be. After providing an overview of the monitoring approach and the different data analysis techniques, results and observations from select laboratory experiments, as well as an in-service structure are discussed. Finally, suggestions for further work are proposed.

Investigating the Performance and Safety of Li-Ion Cylindrical Cells Using Acoustic Emission and Machine Learning Analysis

Acoustic emission (AE) is a low-cost, non-invasive, and accessible diagnostic technique that uses a piezoelectric sensor to detect ultrasonic elastic waves generated by the rapid release of energy from a localised source. Despite the ubiquity of the cylindrical cell format, AE techniques applied to this cell type are rare in literature due to the complexity of acoustic wave propagation in cylindrical architectures alongside the challenges associated with sensor coupling. Here, we correlate the electrochemical performance of cells with their AE response, examining the differences during pristine and aged cell cycling. AE data was obtained and used to train various supervised binary classifiers in a supervised setting, differentiating pristine from aged cells. The highest accuracy was achieved by a deep neural network model. Unsupervised machine learning (ML) models, combining dimensionality reduction techniques with clustering, were also developed to group AE signals according to their form. The groups were then related to battery degradation phenomena such as electrode cracking, gas formation, and electrode expansion. There is the potential to integrate this novel ML-driven approach for widespread cylindrical cell testing in both academic and commercial settings to help improve the safety and performance of lithium-ion batteries.

Non-Contact Acoustic Emission Monitoring of Corrosion in Mooring Chain Links

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.

Influence of Grease Contamination on Acoustic Emission Monitoring of Low-speed Roller Bearings

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.

Acoustic emission signal correlation with micro-machining characteristics of Ti-6Al-4 V alloy

Acoustic emission signal correlation with micro-machining characteristics of Ti-6Al-4 V alloy

Study on compaction characteristics and mechanical model of dry crushing filling material under lateral confinement condition

The compaction characteristics and bearing capacity of dry filling materials in goaf have a significant influence on stope control and surface stability. Through acoustic emission monitoring and mechanical model analysis, a series of confined compression tests were conducted on crushed waste with varying particle sizes and Talbot coefficients. The deformation, fragmentation, and acoustic emission characteristics under corresponding working conditions were determined. The results indicate that the stress–strain curves of crushed stone with different particle sizes and Talbot coefficients exhibit similar nonlinear behavior during confined compression. However, the strain response varies with changing stress levels. By analyzing the slope change rate of the stress–strain curve, the lateral uniaxial compression process of waste rock can be divided into three deformation stages: rapid compression, stable crushing, and slow compaction. The compressive deformation characteristics of gravel differ based on particle size and Talbot coefficient. Specimens with a higher Talbot coefficient demonstrate stronger compressive resistance and weaker deformation resistance during initial compaction loading. Notably, the internal pressure structure strength is influenced by factors such as maximum particle size D, grading coefficient n, and particle size distribution continuity, rather than solely by the proportion of large particles. The evolution of acoustic emission signals and energy-time curve during waste rock confined axial compression synchronizes with the compaction process. Overall, compaction plays a critical role in maintaining the stability of goaf in dry crushed waste filling.

Experimental study on failure mechanism of soft rock-coal bodies in abandoned mines under cyclic dynamic loading

During the construction and operation of a pumped storage power station in an abandoned mine, the soft rock-coal body structure, comprising the roof and the residual coal pillars, encounters a complex stress environment characterized by cyclic loads. The study of its failure mechanism under cyclic dynamic loading holds significant theoretical and practical importance to stay the safety and stability of the abandoned mine pumped storage power station. In this paper, we take “roof-residual coal pillar” soft rock-coal combinations with different percentages of rock as the research object, and study their mechanical properties, failure mechanism, energy evolution characteristics and acoustic emission distribution characteristics through cyclic dynamic loading experiments. The results of the experiment indicate that: (1) Both weak cyclic dynamic loading and high rock percentage enhance the deformation resistance of soft rock-coal combinations. Under low-disturbance horizontal cyclic loading, its peak strength and modulus of elasticity increase with increasing rock percentage. (2) Under low-disturbance horizontal cyclic loading, an increasing trend is observed in the average total strain energy density, dissipation energy density, and elastic energy density of the combinations as the percentage of rock increases. (3) Under low-disturbance horizontal cyclic loading, as the percentage of rock increases in the soft rock-coal combinations, the degree of failure in the rock body part progressively intensifies, while the destruction of the coal portion progressively decreases. (4) The large number of acoustic emission signals are generated at the instant of destabilization and destruction of the coal-rock combinations, mainly dominated by the signals generated by the destruction of the coal body. Acoustic emission counts and absolute energy at key point N2 decrease as the percentage of rock increases. The b value is primarily distributed in the cyclic dynamic loading stage and the failure stage, both displaying zones of sudden increase and sudden decrease in b value.

EVALUATION OF WOOD DAMAGE AND FRACTURE BEHAVIOR BASED ON ENERGY ENTROPY OF ACOUSTIC EMISSION SIGNALS

In order to assess the damage and fracture behavior of wood under continuous loading, an energy entropy and b-value associated with the acoustic emission (AE) signal were defined to quantitatively describe the release of strain energy during loading. Firstly, the acoustic emission signals of the wood in the three-point bending test were collected. This paper presents the concept of energy entropy according to the definition of information entropy. In order to further evaluate the strain energy intensity released by the damage behavior of the wood specimen, the acoustic emission b-value was defined. Finally, by jointly analysing the dynamics of these two parameters, the test process can be divided into three phases. The results show that even in the elastic phase, micro-destructive behavior occur inside the wood specimen; in the plastic phase, the wood specimen is not only subjected to macroscopic damage, but also often accompanied by fine cracks inside.

Influence of Immersion Time on the Frequency Domain Characteristics of Acoustic Emission Signals in Clayey Mineral Rocks.

The frequency domain characteristics of acoustic emission can reflect issues such as rock structure and stress conditions that are difficult to analyze in time domain parameters. Studying the influence of immersion time on the mechanical properties and acoustic emission frequency domain characteristics of muddy mineral rocks is of great significance for comprehensively analyzing rock changes under water-rock coupling conditions. In this study, uniaxial compression tests and acoustic emission tests were conducted on sandstones containing montmorillonite under dry, saturated, and different immersion time conditions, with a focus on analyzing the effect of immersion time on the dominant frequency of rock acoustic emission. The results indicated that immersion time had varying degrees of influence on compressive strength, the distribution characteristics of dominant acoustic emission frequencies, the frequency range of dominant frequencies, and precursor information of instability failure for sandstones. After initial saturation, the strength of the rock sample decreased from 53.52 MPa in the dry state to 49.51 MPa, and it stabilized after 30 days of immersion. Both dry and initially saturated rock samples exhibited three dominant frequency bands. After different immersion days, a dominant frequency band appeared between 95 kHz and 110 kHz. After 5 days of immersion, the dominant frequency band near 0 kHz gradually disappeared. After 60 days of immersion, the dominant frequency band between 35 kHz and 40 kHz gradually disappeared, and with increasing immersion time, the dominant frequency of the acoustic emission signals increased. During the loading process of dry rock samples, the dominant frequency of acoustic emission signals was mainly concentrated between 0 kHz and 310 kHz, while after saturation, the dominant frequencies were all below 180 kHz. The most significant feature before the rupture of dry rock samples was the frequent occurrence of high frequencies and sudden changes in dominant frequencies. Before rupture, the characteristics of precursor events for initially saturated and immersed samples for 5, 10, and 30 days were the appearance and rapid increase in sudden changes in dominant frequencies, as well as an enlargement of the frequency range of dominant frequencies. After 60 days of immersion, the precursor characteristics of rock sample rupture gradually disappeared, and sudden changes in dominant frequencies frequently occurred at various stages of sample loading, making it difficult to accurately predict the rupture of specimens based on these sudden changes.

Damage Evolution and Fractal Characteristics of Granite under the Influence of Temperature and Loading

To study the damage characteristics and evolution law of granite under the influence of temperature and loading rate, uniaxial compression tests at different loading rates were performed for granite samples at 30 °C, 40 °C, 60 °C, and 80 °C. The damage characteristics and evolution law of granite were studied in conjunction with acoustic emission ringing, acoustic emission b values, acoustic emission signaling constellations, and fractal dimensions. The results showed the following: (1) At a low loading rate (0.005 mm/s), the peak stress of the granite increases gradually as the temperature increases. At high loading rates (0.01 mm/s and 0.02 mm/s), the peak stress of the granite first decreases, then increases as the temperature increases. The elastic modulus at different loading rates decreases first, then increases as the temperature increases. The elastic modulus of the granite is the lowest at 40 °C; (2) At the same temperature, specimens subjected to higher loading rates exhibit accelerated internal damage progression, with a precipitous decrease in the b-value occurring earlier. At a loading rate of 0.005 mm/s, the total number of acoustic emission ring-down events is at least 1.7 times greater than that at other loading rates, indicating a more complex development of internal cracks in the granite specimens. Moreover, as the temperature increases under a constant loading rate, the concentration of acoustic emission ring-downs intensifies during the unstable crack development and failure phases, accompanied by progressively larger fluctuations in the b-value; (3) Under the same temperature conditions, the acoustic emission signals in the specimens loaded at 0.005 mm/s and 0.01 mm/s propagate from one end to the other. At 0.02 mm/s, emissions appear simultaneously at both ends of the specimen and converge toward the center. The proportion of damage and energy associated with compression differs with temperature; at 30 °C and 40 °C, damage and energy are concentrated in the early phase of compression, whereas at 60 °C and 80 °C, they are more significant during the later stages; and (4) The fractal dimension of the granite specimens generally decreases, then undergoes a phase of fluctuating growth, followed by a decline. With increasing loading rates at a constant temperature, the compaction of the granite specimens decreases; the orderliness of the crack patterns gradually increases. As the temperature increases, the distribution of cracks during the compaction and failure phases becomes more orderly. During the crack initiation and development phases, the dispersion of cracks and the formation of crack networks are more pronounced. These findings provide a theoretical reference for understanding the microscale damage and failure mechanisms of granite under varying loading rates and temperatures.

Acoustic Emission during Non-Uniform Progression of Processes in Composite Failure According to the Von Mises Criterion

In the study, based on the model of acoustic emission during the destruction of a composite material by shear force according to the Von Mises criterion, the effect of non-uniformity of the destruction process on the generated acoustic emission signal is simulated. The study under the accepted modeling conditions allows us to determine the patterns of changes in the amplitude envelope of acoustic emission signals at various stages of developing processes. In theoretical and experimental studies of acoustic emission signals when searching for patterns in their parameter changes and developing methods for monitoring or diagnosing the state of composite materials, the problem lies in the interpretation of recorded information. This issue arises from the complexity and diversity of processes occurring in the material structure at micro and macro levels, and the high sensitivity of the acoustic emission method to these processes, wherein structural changes lead to observable alterations in the characteristics of acoustic emissions. Solving this problem requires both theoretical and experimental studies to understand the influence of various factors on the characteristics of the generated acoustic emission. The results of the presented study can be used to assess the condition of composite materials and structures, such as bridges, e.g., in terms of defectiveness, property dispersion, damage during operation, and other characteristics.

Influence of groundwater on micro-cracking behaviour and failure mechanism of deep hard rock

To address the water-inrush hazards encountered in deep tunnels in the rugged terrain of Southwest China, the uniaxial compression tests and acoustic emission (AE) monitoring were conducted on granite samples taken from a deep tunnel with different durations of saturation (2 days and 30 days), and the results were compared with those of natural granite samples. The results revealed the influence of groundwater on the mechanical properties, micro-cracking behavior, and failure modes of granite, which provided guidance for the deep tunnel construction. Based on the previous work, an optimized method for extracting the dominant frequency of rock AE signals considering the effect of groundwater on hard granite was proposed, which corrected the inaccurate extraction problem in the traditional method. The optimally extracted high dominant frequency AE signals could also be used as a precursor of rock failure.

Features of Recording Energy Parameters of Acoustic Emission Signals from Materials

The acoustic emission method is used as a means of analyzing the state of materials and structures, non-destructive testing and diagnosing them during operation. An important advantage over other methods is that it reacts only to the development of truly dangerous defects and is capable of monitoring large volumes and surfaces without scanning them. The article presents the results of the development of methods and algorithms designed to analyze the energy characteristics of acoustic signals. The use of spectral methods is justified.

Failure mechanisms and acoustic responses of cylindrical lithium-ion batteries under compression loadings

To understand the crashworthiness safety of lithium-ion battery (LIB) comprehensively, acoustic emission (AE) testing technology is introduced to explore the mechanical response and failure mechanism. In this paper, the compression process and failure behavior of 18650 LIBs under plane compression, local indentation and three-point bending are experimentally investigated. The effects of indenter diameter, state of charge (SOC) and capacity of the battery on the force-voltage-temperature responses are discussed in detail. The relation between the failure behavior and acoustic response of LIB is studied in combination with AE detection technique. The results show that the deformation of the battery can be divided into three stages before the failure, including the stress on the shell, compression on the internal gap and electrolyte, and stress on the whole. The failure inside the battery is caused by fold, shear fracture, local buckling and tensile break under different loading forms. The smaller the diameter of the indenter is, the lower bearing capacity at the point of failure is. High-SOC batteries experience higher forces and displacements at the failure point. High-capacity batteries have poor bearing capacity and are more prone to thermal runaway. AE signal can be detected at special points during the compression. Combined with the acoustic response of the battery, the whole deformation process is divided into five stages. The researches will provide a guidance for the failure detection and safety evaluation of LIBs.

Research on internal leakage detection of the ball valves based on stacking ensemble learning

Abstract Natural gas is an important clean energy source that is mainly transported through pipelines. The ball valve is a crucial piece of control equipment for the pipeline transportation system for natural gas, and the failure of internal leakage of the ball valve will seriously affect the natural gas transmission and increase the risk of sudden safety accidents. In response to the problems of the limitations of a single machine learning model in the traditional ball valve internal leakage rate prediction methods and failure to qualitatively analyze unilateral and bilateral internal leakage recognition of ball valve, a study of ball valve internal leakage detection based on Stacking ensemble learning is proposed. A total of 15 time and frequency domain feature parameters were obtained by feature extraction of 125 and 96 sets of raw acoustic emission signals from the ball valve; the parameters of a single machine learning model were adjusted by Bayesian optimization grid search. An internal leakage rate prediction model and an internal leakage recognition model are constructed, and the proposed model is compared and analyzed with a single model through a field ball valve internal leakage test. The results indicate that the Stacking ensemble learning model outperforms each single machine learning model in terms of SMAPE (17.2583), RMSE (1.1009), and MAE (0.9375) for internal leakage rate prediction. The Stacking ensemble learning model outperformed the single machine learning model in terms of accuracy (1), recall (1), precision (1), FAR(0), and F1-score (1) for internal leakage recognition. Stacking ensemble learning significantly enhances the model's ability to detect internal ball valve leaks.

A Graph-Data-Based Monitoring Method of Bearing Lubrication Using Multi-Sensor

Super-precision bearing lubrication condition is essential for equipment’s overall performance. This paper investigates a monitoring method of bearing lubrication using multi-sensors based on graph data. An experiment was designed and carried out, establishing a dataset including vibration, temperature, and acoustic emission signals. Graph data were constructed based on a priori knowledge and a graph attention network was employed to conduct a study on monitoring bearing lubrication abnormalities and discuss the influence of a missing sensor on the monitoring. The results show that the designed experiments can effectively respond to the degradation process of bearing lubrication, and the graph data constructed based on a priori knowledge show a good effect in the anomaly monitoring process. In addition, the multi-sensor plays a significant role in monitoring bearing lubrication. This work will be highly beneficial for future monitoring methods of bearing lubrication status.