Acoustic Emission Signals Research Articles

Experimental research on damage characteristics of red sandstone under the combined action of temperature, water and stress

In order to study the damage properties of red sandstone under combined action of temperature, water and stress, the uniaxial compression test was carried out for red sandstone after immersing in water for 0.5 h, 1 h and 3 h at 25 ℃, 55 ℃, 85 ℃ and 95 ℃, the mechanical properties, characteristic stress points, acoustic emission RA-AF parameters and failure forms of red sandstone were analyzed, the damage constitutive equation was established, and the damage properties of red sandstone were studied. The results showed: (1) temperature could significantly improve the saturated water content percentage of red sandstone. With the increase of temperature, the saturated water content percentage showed a trend of increase first and then decrease, and was the largest at 55 ℃; with the increase of temperature, the mechanical parameters (peak intensity, elasticity modulus, and stress at start point of expansion) decreased first and then increased, and was the smallest at 55 ℃; (2) the ratio of crack initiation stress to peak stress (σci/σf) initially increases and then decreases with rising temperatures for the same soaking duration. Conversely, it initially decreases and then increases with extended soaking time at the same temperature. The RA-AF acoustic emission signals predominantly occur in the tensile region at each stage account for a relatively large proportion, with the proportion of signals in the shear region significantly increasing as soaking time extends, corresponding well with the observed damage patterns. (3) By considering the damage due to temperature and water as Initial damage to red sandstone and the damage due to stress as load-induced damage, a damage variable based on the Weibull distribution function was established. The comprehensive damage constitutive equation derived from this can be applied to water-saturated red sandstone below 100 ℃. The findings provide theoretical reference for practical engineering applications.

Characteristics analysis and monitoring of friction stir welded dissimilar AA5083/AA6061-T6 using acoustic emission technique

The joining of aluminum alloys using fusion welding often leads to issues such as hot tears, porosity, distortion, and solidifying cracks. To address these challenges, solid-state welding processes like Friction Stir Welding (FSW) are preferable. This study investigates the FSW of dissimilar aluminum alloys AA 5083 and AA 6061-T6, employing Acoustic Emission (AE) techniques for real-time monitoring. FSW was conducted under conditions with no defects, pinhole defects, and piping defects. The resulting joints were evaluated for their mechanical properties and metallurgical characteristics. Microstructural analysis was performed using an optical microscope, revealing that the defect-free condition achieved the highest tensile strength of 256 MPa and superior hardness of 93 HV at the stir zone. Tensile failure commonly manifested within the heat-affected zone (HAZ) of AA 6061-T6, primarily due to grain enlargement and the impact of Mg2Si precipitates. Examination of the fractured regions via scanning electron microscopy revealed ductile-mode fractures featuring elongated dimples and microvoids. AE parameters such as hits, amplitude RMS, and energy were analyzed, demonstrating that defect-free welds had consistent AE signal patterns, while significant variations were observed in pinhole and piping defect conditions due to larger defect areas. The findings suggest that AE monitoring is effective in detecting and analyzing welding defects, providing valuable insights into the FSW process for dissimilar aluminum alloys.

Amplitude reconstruction of acoustic emission signals on the base of its mathematical modeling as a stochastic process

The problem of random oscillations generated by inner defect within the neighborhood of the boundary of the elastic massive body under the pre-failure stage is considered. The investigation is based on results of the invariants’ method in acoustic emission (AE) theory consisting in the stability conditions of the mean of statistic distribution parameters of signal characteristics under the same pre-failure stage. The mathematical model of arising wave process of displacement oscillations in the massive body is constructed and the correctness of its using is studied. The problem in question is reduced to the investigation of a boundary integral equation in special stochastic process classes. The task of reconstructing from AE signals and describing the nature of the random process of defect radiation at the free boundary of the body is set. Some numerical are presented.

BC-GAN: A threshold-free framework for unsupervised early fault detection in rotating machinery

Early fault detection (EFD) is a crucial component of proactive maintenance that can prevent expensive downtime, enhance safety, and optimize equipment performance and longevity. Existing limitations of the contemporary EFD approaches frequently include: (1) manually designed features relying on expert skills and knowledge, (2) the vaguely determined pre-set thresholds to distinguish between the faulty and healthy state of the testing object (however, the threshold value can vary notably from task to task), and (3) most methods, which have been originally designed for vibration data, cannot be generalised to other techniques, for example acoustic emission (AE) signals. To address these issues, a novel binary-classification generative adversarial network (BC-GAN) is designed for general EFD problems and applied to several specific datasets acquired from different run-to-failure tests of rotating components. Compared with conventional methods, unsupervised BC-GAN directly outputs the probability that an input belongs to either the “health” or “fault” state of the rotating machine without a priory threshold setting. Experimental results demonstrate the high versatility of the proposed network, which can be applied in various laboratory and industrial settings to both vibration and AE signals.

Influence of Compression Loading on Acoustic Emission and Light Polarization Features in TeO2 Crystal.

Monitoring the processes inside crystalline materials under their operating conditions is of great interest in optoelectronics and scientific instrumentation. Early defect detection ensures the proper functioning of multiple crystal-based devices. In this study, a combination of acoustic emission (AE) sensing and cross-polarization imaging is proposed for the fast characterization of the crystal's structure. For the experiments, tellurium dioxide (TeO2) crystal was chosen due to its wide use in acousto-optics. Studies were performed under uniaxial compression loading with a simultaneous acquisition of AE signals and four polarized optical images. An analysis of the temporal dependencies of the AE data and two-dimensional maps of the light depolarization features was carried out in order to establish quantitative criteria for irreversible damage initiation and crack-like defect formation. The obtained results reveal the polarization image patterns and the AE pulse duration alteration specific to these processes, and they open up new possibilities for non-destructively monitoring in real-time the structure of optically transparent crystals under their operating conditions.

A Method for Semi-automatic Mode Recognition in Acoustic Emission Signals

A Method for Semi-automatic Mode Recognition in Acoustic Emission Signals

Partial discharge monitoring by improved PGC-arctan algorithm

This paper proposes an improved PGC-Arctan demodulation algorithm for detecting partial discharge signals in transformers. Compared with the traditional PGC-Arctan algorithm, the improved algorithm extracts carrier frequency difference and carrier phase delay by fundamental mixing and using low-pass filtering on the interference signal, and compensates the phase modulation depth by high order frequency, reducing THD and enhancing SNR. The algorithm is theoretically analyzed, simulated and verified through experiments. In the 200 kHz acoustic emission signal detection experiment, the SNR and THD of the improved algorithm are 49.63 dB and 1.34%, respectively, which is 13.59 dB higher than the SNR of the traditional algorithm, and the THD is reduced by 76.61%, and the harmonic distortion is well suppressed. The system is used to detect weak ultrasonic signals generated during partial discharge of transformers and compare them with PZT. The results show that the system has the potential to be used to monitor the partial discharge signal in transformers.

Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber‐Reinforced Polymer Sandwich Composites Using Acoustic Emission

Acoustic emission (AE) serves as a noninvasive technique for real‐time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb‐core carbon fiber‐reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble‐supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes.

Study on mechanical properties and meso-damage mechanism of carbon-polyvinyl alcohol hybrid fiber reinforced recycled coarse aggregate concrete under the coupling action of sodium sulfate and dry-wet cycles

In order to investigate the influence of the coupling effect of sodium sulfate dry-wet (DW) cycles on the mechanical properties of carbon-polyvinyl alcohol hybrid fiber reinforced recycled coarse aggregate concrete (HFRAC), HFRAC specimens were subjected to uniaxial compression tests. The total volume fraction of fibers was kept constant at 0.4%, and various volume fractions of carbon fibers (CF) were added to the recycled coarse aggregate concrete (RAC), namely 0%, 0.1%, 0.2%, 0.3%, and 0.4%, with polyvinyl alcohol (PVA) fibers replacing a portion of CF in equivalent volume. Uniaxial compressive stress–strain curves were obtained for HFRAC specimens subjected to different numbers of DW cycles (0, 30, 60, and 120 cycles). Microscopic testing methods including acoustic emission (AE), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and nuclear magnetic resonance (NMR) were employed to analyze the microstructure, erosion product composition, and pore structure changes of the specimens. The results revealed that, for RAC specimens, the peak stress (σp) and elastic modulus (E) initially increased and then decreased, whereas the peak strain (εp) initially decreased and then increased with an increase in the number of DW cycles. After adding hybrid fibers, the σp values of CF, Hybrid 3:1, Hybrid 1:1, Hybrid 1:3, and PVA specimens increased (or decreased) by 12.0%, 11.43%, 0.9%, −9.5%, and −8.5%, respectively, compared to RAC. The Hybrid 1:3 specimen exhibited a negative hybrid effect. With an increase in the erosion age, the Hybrid 3:1 specimen demonstrated enhanced resistance to sulfate attack and achieved the highest σp value after 120 cycles. The influence of sulfate DW cycles coupling on the microscopic damage mechanism of HFRAC was analyzed using a statistical damage constitutive model. The microscopic damage evolution process was characterized by five characteristic parameters: E0, εa, εh, εb, and H. The analysis results indicated a clear correlation between the variation of these characteristic parameters and the number of DW cycles. An effective connection was established between the microscopic damage mechanism and the macroscopic nonlinear constitutive behavior. The phenomenon of relative lagging of acoustic emission signals during the uniaxial compression process was explained from the perspective of effective stress. This study provides a theoretical basis for the promotion and application of RAC under sulfate DW cycle environments.

A Novel Noise Reduction Approach of Acoustic Emission (AE) Signals in the SiC Lapping Process on Fixed Abrasive Pads.

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time-domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR.

Characterization of Acoustic Emissions from Concrete Based on Energy Activity Coefficient

Single-stage compression loading experiments were carried out on concrete specimens of various strengths to explore the characteristic parameters of the acoustic emission signal and its damage evolution law in the concrete damage process. These specimens were monitored in real time with acoustic emission and DIC instruments during the loading process, and internal pores and slices were scanned with CT scanning instruments after compression. The acoustic emission phenomenon was expressed using the energy activity coefficient, and the law relating to the phenomenon was summarized. The results show that when the peak and mean values in the first adjacent time domain grow rapidly, the specimen produces a large crack and enters the stage of rapid crack development, which can be taken as an indication of the impending damage to the specimen. The energy activity coefficient reflects the damage development intensity as follows: the smaller the energy activity coefficient, the more the cracks developed; the faster the speed, the larger the deformation. With an increase in the load level, the energy activity coefficient gradually tends to stabilize, and the specimen enters the stage of rapid crack development. However, when the energy activity coefficient suddenly increases again, the specimen is destabilized and destroyed. Therefore, the energy activity coefficient responds to the degree of congenital defects in the specimen. As the load increases, the energy activity coefficient is more stable, and the defects are smaller; in contrast, the energy activity coefficient drastically oscillating indicates that the material is very defective.

Analysis of crumb rubber content influence on damage evolution and pattern recognition of rubberised epoxy asphalt mixture using acoustic emission techniques

ABSTRACT The application of coarse crumb rubber (CR) to toughen the epoxy asphalt mixture has emerged as a viable choice. However, the evolution rule of strength and damage modes of mixtures with different crumb rubber contents remains elusive. The objective of this study is to clarify the failure mechanisms in rubberized epoxy asphalt mixture combined acoustic emission (AE) data and k-means clustering theory. Four types of epoxy asphalt mixture samples are tested with the monitor of AE sensors. Based on the temporal and frequential features of AE signals, damage evolution is evaluated by the Weibull function, b-value, and the relationship between the raise angle and average frequency at the peak loading. Moreover, the k-means algorithm is conducted to cluster the AE events and damage characteristics after pre-processing the data. The test results indicate that CR particles enhance the toughness of epoxy asphalt mixtures and the mixtures with fewer CR are susceptible to occur sudden fracture due to brittle coarse aggregates. The k-means method effectively identifies AE events into four types of damaged behaviours. The findings offer valuable insights into the strength attenuation, modification characteristics, damage evolution and damage modes of rubberized epoxy asphalt mixtures, contributing to the development of more durable and resilient asphalt materials.

Study on the keyhole oscillation mechanism of laser welding based on electro-mechano-acoustical analogy theory

The acoustic emission phenomena generated by the laser welding process are closely related to the keyhole's forced oscillation and the dynamic pressure balance at the keyhole bottom. In this study, we constructed the mechanical equation of keyhole bottom pressure balance and used the “electro-mechano-acoustical” analogy theory to convert it into an acoustic equation, the acoustic resonant frequency as well as the magnification amplification factor of the keyhole oscillation in different penetration states were derived. A monitoring platform combining acoustic sensor and high-speed photography was established to obtain acoustic emission signals and lateral keyhole images. The acoustic and image features extracted from the monitoring data validated the derived formulas. Additionally, the acoustic phenomena of different frequency-energy components were explained according to the derived formulas in turn. The results demonstrated that the derived theory is compatible with experimental comparisons, showcasing its great interpretability and applicability. The introduced analogy theory effectively establishes a bridge between keyhole forced pressure and acoustic emission vibration, aiding in the understanding of keyhole oscillation characteristics. A novel model of keyhole oscillation was proposed and verified using process monitoring data in this study, which provides a new approach for the investigation of keyhole dynamic.

Strain clamp condition monitoring for transmission lines based on acoustic emission signals

Abstract To ensure the reliability and safety of the power supply in the electric system, it is significant to realize the strain clamp condition monitoring for transmission lines. A strain clamp vibration test rig was designed and built to collect acoustic emission (AE) signals generated by strain clamps with an excitation frequency of 30, 50, 70, 90, and 110 Hz, respectively. The characteristics of the collected AE signals are then analyzed in the time domain and time-frequency domain. The results of the signal analysis in the time domain show that, the increasing excitation frequency leads to a higher amplitude of the AE signal, and the signal period is consistent with that of the excitation. Meanwhile, both the root-mean-square (RMS) and the average signal level (ASL) of the AE signal increase continuously with the excitation frequency, of which the growth trends are different. Additionally, the results of the signal analysis in the time-frequency domain also show that the signal spectrum is mainly concentrated in the range of 30 kHz to 200 kHz at different excitation frequencies and the increasing excitation frequency significantly leads to more high-frequency components in the AE signal.

Anisotropic mechanism of material removal and ductile-brittle transition in sapphire scratching based on acoustic emission signal

Sapphire exhibits typical anisotropy due to its unique crystal structure, and there are significant property differences in different processing areas during the machining of curved sapphire component, making it essential to investigate the anisotropy in the removal process of sapphire materials for efficient curved surface machining. In this study, the force and acoustic emission (AE) signals were monitored at different scratch stages and orientations, and the anisotropic characteristics of ductile-brittle transition (DBT) depths, material removal morphology, and subsurface crack propagation direction were analyzed. The results indicate that the anisotropy of force signals and DBT depths is mainly reflected in the differences between crystal planes. In addition, the removal morphology of materials with different crystal orientations showed smooth scratch grooves with unclear anisotropic characteristics during the ductile removal (DR) stage, while the crack propagation direction with different scratch orientations during the DBT stage showed significant anisotropic characteristics. The brittle removal (BR) stage is characterized by brittle spalling in the form of shell-like, blocky, and fishbone spalling, and there is a significant anisotropic difference in the spalling shapes on both sides of the scratch. The original AE signals, Fast Fourier Transform (FFT), and Short Time Fourier Transform (STFT) results show that the AE frequencies of different crystal planes are different, and the differences in different scratch orientations on the same crystal plane are reflected in the peak amplitude. Finally, the propagation directions of subsurface cracks with different directions are anisotropic due to different scratching crystal planes, and propagate mainly along directions of 15°, 29°, 32°, 58°, and 105°.

Acoustic emission monitoring of composite marine propeller blades using embedded piezoelectric sensors and hydrophones

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

Identification and prediction method for acoustic emission and electromagnetic radiation signals of rock burst based on deep learning

With the deep development of underground rock engineering, the threat of rock burst disasters is increasing. At present, the identification and prediction of rock burst mostly rely on the experience of field staff to determine the critical value and development trend, and there is a lack of efficient and intelligent methods for the utilization of massive data. Therefore, this paper constructs a rock burst signal recognition and prediction model based on deep learning methods to solve the above problems. In this paper, the acoustic emission (AE) and electromagnetic radiation (EMR) data of the site are first marked and input into the long-short-term memory-fully connected neural network model to realize the identification of rock burst danger signals. Then, the graph data of the AE and EMR sensor monitoring networks are constructed and input into the spatiotemporal graph convolutional network signal prediction model to predict future monitoring data. Finally, this paper uses the same dataset to compare and analyze several other commonly used deep learning models. The results show that the model constructed in this paper has the best performance in the identification and prediction of AE and EMR signals with rockburst risk. This study can provide theoretical reference for intelligent monitoring and early warning of rock burst in underground rock engineering.

Acoustic Emission Detection in Noisy Environments using Linear Prediction

In non-destructive testing, acoustic emission testing is an established technique with numerous applications in academia and the industry. In nearly all applications, the detection of relevant acoustic emission signals in continuous structure-borne sound recordings is a crucial step that forms the basis for subsequent analyses and should therefore neither miss any relevant acoustic emissions nor detect too many irrelevant events. That is because, in its most prevalent form, acoustic emission testing utilizes ultrasound signals that require large amount of storage, which might be an economically limiting factor especially for structural health monitoring of large civil infrastructures. In these monitoring applications, the signal-to-noise ratio is also expected to be worse compared to more controlled laboratory conditions, as they can be often found, for example, in materials research. We therefore propose the use of linear prediction, a time series forecasting technique, to extract relevant acoustic emissions from continuous recordings. The proposed methodology utilizes the residual between the signal predicted from a linear combination of previous time steps and the actual measurement to detect any anomalous events. Since the linear predictor can be initialized using the environmental noise only, no specific knowledge of the acoustic emission signals of interest is required beforehand and hence can automatically adapt to each measurement environment. We compare our approach with a simple amplitude-based detection as it is commonly implemented in commercially available acoustic emission systems. Especially for low signal-to-noise ratios, an increase in the area under the receiver operating characteristic curve of up to 0.7 compared to the amplitude-based detection is found.

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.