Acoustic Emission Signal Characteristics Research Articles

Critical slowing down features of acoustic emission signals for predicting the failure of black sandstone under different freezing temperatures

Critical slowing down features of acoustic emission signals for predicting the failure of black sandstone under different freezing temperatures

Study on the failure and acoustic emission characteristics of coal under graded cyclic loading and unloading stress paths

To study the influence of cyclic disturbance stress on the mechanical behavior of coal during mining, a gas containing coal fluid-solid coupling servo seepage experimental system was used to conduct experimental research on the acoustic emission (AE) characteristics of gas containing coal under two stress paths of graded cyclic loading and unloading. The AE characteristics of coal damage and failure processes under different cyclic stress paths were analyzed. The research results indicate that: (1) The overall characteristics of AE signals for both graded cyclic loading and unloading paths are basically the same. With increasing of the amount of graded cyclic loading or unloading, the AE count reaches its maximum value when reaching failure, and the cumulative ringing calculation of AE increases exponentially. (2) The AE signals under the graded cyclic loading or unloading path exhibit obvious zoning characteristics. In the low and medium stress regions, the AE signal basically satisfies the Kaiser effect, while reaching the high stress region before failure, the AE signal exhibits a significant Felicity effect. (3) The concentration coefficient of AE and the intensity coefficient of the Kaiser effect have been newly defined. They are used to quantitatively characterize the extent of the Kaiser effect of AEs under graded cyclic stress. It was found that as the variation of graded cyclic stress increases, the concentration coefficient and Kaiser effect intensity coefficient both show a decreasing trend. (4) Combining the AF and RA values of AE, it was found that the coal failure signals of the two stress paths were basically similar, that is, the overall failure was mainly tensile failure, and the signals of cyclic unloading tensile failure were significantly more than those of cyclic loading. The AE signal characteristics studied in this article are of great significance for predicting coal power disasters.

Acoustic Emission Signal Feature Extraction for Bearing Faults Using ACF and GMOMEDA

Acoustic Emission Signal Feature Extraction for Bearing Faults Using ACF and GMOMEDA

Study on the characteristics of acoustic-thermal precursors of destabilization damage in coal-rock combination bodies with different proportions

The instability and failure processes of coal-rock combinations are accompanied by the release of acoustic emission (AE) and infrared radiation (IR) signals. To investigate the characteristics of AE and IR signals during the failure process in coal-rock combinations with different ratios, and to analyze the effectiveness and applicability of the monitoring methods for these two signals in various specimen ratios. In this paper, the uniaxial compression tests were conducted on seven coal-rock combinations with different ratios by using the rock mechanics loading system and the cooperative monitoring platform of infrared and acoustic emission. The results show that (1) the AE counts for failure precursors in coal-rock combinations are positively correlated with the coal-rock ratio, whereas the AE peak counts are negatively correlated. Moreover, as the coal-rock ratio decreases, the slope of the cumulative AE count curve during the peak failure stage approaches 1. (2) During the elastic and yield stages, the maximum infrared radiation temperature (MIRT) curve fluctuates, and just before peak failure, the curve displays a distinctive “V” shape. This “V” shape becomes more pronounced as the coal-rock ratio decreases. (3) In the monitoring of damage precursors of coal-rock combinations, when the ratio of coal to rock is 1:3 or less, IR monitoring is better than AE monitoring. Conversely, when the ratio of coal to rock is greater than 1:3, AE monitoring is more suitable. Consequently, the combined monitoring of AE and IR signals can increase the reliability and precision of early warning signals for coal-rock assemblage damage precursors.

Acoustic Emission characteristics and damage evolution of Concrete-Encased CFST columns under compressive load

Concrete-Encased Concrete Filled Steel Tubular (CE-CFST) is usually served as a compression member in engineering structures due to its high performance. It is crucial to reveal its compressive failure mechanism and the damage evolution law. This study used Acoustic Emission (AE) technology to monitor the compressive failure behavior of seven groups of CE-CFST columns with different diameter-width ratio, slenderness ratio and eccentricity, and then the AE signal characteristics and structural damage evolution law were discussed. Results show that the curve of AE characteristics can be used for effectively identifying the damage stages of CE-CFST structures. The failure process can be divided into six stages: initial compaction, stable growth of micro cracks, unstable propagation of macro cracks, collapsing of the outer RC structure, bulging of the core CFST structure and overall failure. The AE characteristic parameters, RA-AF value and b value are closely related to stress and crack of structure, and can be used for early warning of structural failure during the stage of unstable crack propagation. Particularly, the failure of outer concrete can be judged as b value drops to the minimum. The current study is of great significance for understanding the damage evolution process and achieving damage assessment of CE-CFST structures.

Acoustic emission and machine learning algorithms for particle size analysis in gas-solid fluidized bed reactors

In this work, a combination of an acoustic emission (AE) technique and a machine learning (ML) algorithm (Random Forest (RF) and Gradient Boosting Regressor (GBR)) is developed to characterize the particle size distribution in gas-solid fluidized bed reactors. A theoretical approach to explain the generation of acoustic emission signal in gas-solid flows is presented. An AE signal is generated in gas-solid fluidized beds due to the collision and friction between fluidized particles as well as between particles and the bed inner wall. The generated AE signal is in the form of an elastic wave with frequencies >100 KHz and it propagates through the gas-solid mixture. An inversion algorithm is used to extract the information about the particle size starting from the energy of the AE signal. The advantages of this AE technique are that it is a cheap, sensitive, non-intrusive, radiation-free, suitable for on-line measurements. Combining this AE technique with ML algorithms is beneficial for applications to industrial settings, reducing the cost of signal post-processing. Experiments were conducted in a pseudo-2D flat fluidized bed with four glass bead samples, with sizes ranging from 100 μm to 710 μm. AE signals were recorded with a sampling frequency of 5 MHz. The AE signal post-processing and data preparation for the ML process are explained. For the ML process, the AE frequency, AE energy and particle collision velocity data sets were divided into training (60%), cross-validation (20%) and test sets (20%). Two ensemble ML approaches, namely Random Forest and Gradient Boosting Regressor, are applied to predict particle sizes based on the AE signal features. The combination of these two models results in a coefficient of determination (R2) value greater than 0.9504.

Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Abstract Cavitation prevents the conversion of internal dynamic and pressure energy in a water jet propulsion pump and causes erosion of flow passage components. Acoustic emission is a non-destructive test method for instability signals. Based on the incipient mechanism and development process of cavitation, three acoustic emission signal feature parameters (ring counting rate, energy release rate, Hurst index) are proposed to define the cavitation state utilizing the waveform pattern and signal self-similarity. The multi-source and multi-feature cavitation identification model of acoustic emission signals is established through cavitation simulation, multi-monitoring point acoustic emission signal testing, and feature parameter extraction, and a large number of probabilistic trainings are carried out using Bayesian network, resulting in a fast cavitation state identification method based on acoustic emission signals. The research results have important practical engineering application significance for the prediction of cavitation resistance, real-time dynamic monitoring of cavitation phenomenon and prevention of cavitation damage.

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.

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.

Multifractal Characteristics of Acoustic Emission Signals of Concrete Under Axial Tension With Different Steel Fiber Contents

Multifractal Characteristics of Acoustic Emission Signals of Concrete Under Axial Tension With Different Steel Fiber Contents

Fractal evolution characteristics of fracture meso-damage in uniaxial compression rock masses using bonded block model

With regard to deep mining in metal mines, an investigation into the failure mode of deep fractured rock masses and their corresponding acoustic emission signal characteristics is conducted via uniaxial compression tests. Subsequently, a fractal damage renormalization group mechanical model is developed to explain the behavior of those fractured rock masses. Employing the bonded block model (BBM) numerical simulation method, fracture process in synthetic rock samples is analyzed, thereby validating the efficacy of the mechanical model. The numerical simulations highlight the critical role of fractures expansion in underlying the deterioration of rock mass strength. As the peak load decreases, the fracture fractal dimension increases, leading to a significant 14.2% reduction in compressive strength accompanied by an approximate 8.7% rise in average fracture fractal dimension. A comparative analysis of tetrahedral and voronoi block synthetic rock samples reveals the tetrahedral block samples exhibit a superior ability to depict the fracture behavior of fractured rock masses. Specifically, they offer a more accurate simulation of acoustic emission characteristics and failure modes. Furthermore, variations in the fracture fractal dimension with respect to the hole defect’s position are observed, with the maximum value occurring along the vertical axis of the hole defect. This observation underscores the potential utility of visually monitoring deep rock fracture dynamics as an effective mean for quantitatively evaluating fracture damage and strength degradation in deep rock formations.

Effects of polyurethane hardness on the propagation of acoustic signals from partial discharge

AbstractPolymeric insulation is a critical component of high voltage systems. However, exposure to high electric stress can cause partial discharges (PDs) to occur and may result in the deterioration of insulation and lead to dielectric failure. These PD events are accompanied by the production of acoustic pressure impulses in the polymer. Detection of these acoustic pressure impulses can reveal the presence of PDs and locate their source. However, analysing the detected acoustic emission (AE) signal is challenging. The acoustic pressure source's nature and the propagating medium's properties, such as density, viscosity, and elasticity, significantly affect the propagating AE signal. The effects of the hardness of the polyurethane (PU) on the propagating AE signal are reported by the authors based on results obtained from laboratory experiments. It was observed that the decay rate in the magnitude of the acoustic impulse was high in PU at all hardness levels following an exponential behaviour. The analysis of the frequency spectra indicates that the higher frequency components attenuate more strongly with distance. These laboratory results can be valuable for engineers and industries as they provide valuable insight into how the physical characteristics of a material affect the propagation characteristics of AE signals during the detection and location of PD source using the AE detection technique.

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.

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.

Research on features and localization of AE signals from the mechanical body of industrial robots based on WD-EMD

Abstract In order to reveal the propagation characteristics of acoustic emission (AE) signals in the body of industrial machinery, the characteristic frequencies and wave speeds of AE signals propagated on the interior and exterior surfaces (IS and ES) of the body were extracted. Subsequently, an algorithm utilizing characteristic frequency and time difference of arrival (TDOA) is proposed for the identification and localization of AE sources. Initially, an AE source is induced on the IS and ES of the body using the pencil-lead break method in accordance to ASTM standards, and the AE signal is captured by two piezoelectric sensors at a sampling frequency of 3 MHz. Then, to avoid the limitations of wavelet decomposition using self-selected wavelet scale and the problem that a single indicator cannot properly evaluate the correlation between independent mode functions (IMFs) with the original signal, this paper will conduct 4-layer wavelet decomposition of the original signal according to the response frequency range of the sensor, and select the wavelet details within the stable range of the response frequency of the sensor for preliminary reconstruction,and then the empirical mode decomposition (EMD) method is used to decompose the de-noised AE signal into 7 IMFs, and the AE waveform is reconstructed by the combined information of correlation coefficient and variance accounted for. Secondly, the reconstruction method combined with EMD analysis and a single index is compared with the proposed method in this paper to verify the reliability of the proposed method. In addition, the frequency domain characteristics of AE signal propagation process on the IS and ES of the body are extracted. Finally, based on the TDOA principle, the propagation speed of AE signal on the IS and ES of the body is calculated. Based on the geometric relationship between the AE source and two sensors, an algorithm for the location of the AE source is proposed. The results show that the proposed signal reconstruction method can effectively extract the features of AE signals, and the average positioning accuracy of the localization algorithm based on characteristic frequency and TDOA reaches 0.86%.

Application of Acoustic Emission Technology for Bearing Condition Monitoring on Passenger Ropeway

Rolling bearings are important stressed components of driving wheels and detour wheels on passenger ropeway, and their failure may lead to accidents and casualties. After the ropeway is installed and put into operation, the rolling bearings in the wheels are difficult to disassemble. Once disassembled, the bearing may be damaged. Due to the installation position and the complex operation environment, the health status of the rolling bearings is a blind spot for ropeway inspection and detection. Acoustic emission detection technology is used to monitor the rolling bearings of passenger ropeways. After a large number of field tests, the acoustic emission monitoring program and signal analysis method of rolling bearing on ropeway were proposed. The parameter characteristics of acoustic emission signals obtained in bearing operating were analyzed from two aspects: the ropeway operating cycle and the bearing operating cycle. Spectrum analysis of typical signals was performed, and the spectrum characteristics of typical acoustic emission signals of rolling bearing during the operation of the ropeway were obtained. On-site tests results show that using acoustic emission technology to perform condition monitoring and fault diagnosis of low-speed rolling bearings on passenger ropeways has good application prospects.

Innovative acoustic emission method for monitoring the quality and integrity of ferritic steel gas pipelines

Abstract This article presents a comprehensive improvement in the experimental analysis of cracking processes in smooth and sharp V-notched samples taken from gas transport pipelines, utilizing the acoustic emission (AE) method. The research aimed to establish a robust correlation between the failure mechanisms of uni-axially tensile samples and the distinct characteristics of AE signals for enhanced quality management in pipeline integrity. The study encompassed materials from two different straight pipe sections, encompassing both long-term used materials and new, unused materials. Through the application of the k-means grouping method to AE signal analysis, we achieved the identification of AE signal parameters characteristic of various stages of the material destruction process. This advancement introduces a significant improvement in monitoring and managing the operational safety of pipeline networks, offering a methodology that leverages advanced acoustic emission signal analysis. The outcomes present significant implications for the pipeline industry by proposing methods to enhance safety systems and more effectively manage the integrity and quality of gas infrastructure.

Performance evaluation of deep learning approaches for fault diagnosis of rotational mechanical systems using vibration, sound, and acoustic emission signals

The present study emphasizes an optimized deep learning algorithm for gearbox fault detection using vibration, sound, and acoustic emission signals. Statistical and acoustic features are extracted from these signals, and various neural network algorithms are explored. The supervised deep feed forward neural network (DFFNN) demonstrates excellent performance with vibration signals but limited accuracy with sound and acoustic emission signals. To address this, unsupervised algorithms are optimized and compared with vibration-based classification. The findings show that unsupervised neural networks, particularly the auto-encoder and stacked auto-encoder architectures, achieve improved classification accuracy by leveraging the unique characteristics of acoustic emission signals. The unsupervised models also effectively overcome the vanishing gradient problem via regularization, enhancing their training efficiency. The stacked auto-encoder, with multiple layers of encoders and decoders, reduces computation time by 40% and memory consumption. These optimized algorithms hold promise for automated fault detection systems. The auto-encoder and stacked auto-encoder, utilizing vibration, sound, and acoustic emission signals, offer enhanced classification accuracy and can facilitate real-time monitoring of rotating mechanical systems. However, further optimization is needed to maximize their performance. In a nutshell, the supervised DFFNN excels in utilizing vibration signals for fault detection, while the unsupervised models exploit the distinctive characteristics of acoustic emission signals. Future research will focus on refining these algorithms to enhance their effectiveness. Implementing these optimized deep learning approaches can lead to autonomous fault detection systems, eliminating the need for continuous human supervision.

Applicability of a new composite amusement ride safety device based on acoustic emission monitoring technology

Under the premise of ensuring safety, it is of great significance to realize the lightweight of the non-main load-bearing parts of amusement facilities. The purpose of this study is to study the failure process of a newly designed carbon fiber bumper by using acoustic emission technology. First of all, the design of carbon fiber anti-collision bar can effectively restrain passengers, and its weight is reduced by nearly two-thirds compared to traditional metal materials. Subsequently, the load-bearing capacity of the bumper was tested and acoustic emission monitoring was carried out. The test results show that this new type of combined structure of amusement facility has high reliability, which exceeds the safety factor of 3.5 required by the steel structure of amusement facility. In addition, Renyi entropy was used to select the best window function of short-time Fourier transform, and the frequency domain characteristics of acoustic emission signals of typical damage modes were discussed through appropriately selected windows function. A classifier based on supervised machine learning is established by combining frequency features and acoustic emission feature parameters. Furthermore, the use of classifiers helps to understand the damage behaviour of composite structures.

Effect of Acoustic Emission Sensor Location on the Detection of Grinding Wheel Deterioration in Cylindrical Grinding

The acoustic emission (AE) technique is an effective method for monitoring grinding wheels, and numerous studies have been published on applying an AE to monitor grinding wheels. However, there are few studies on the effect of the location of the AE sensor in stably acquiring the AE signals generated during deterioration in cylindrical grinding wheels. In this study, we propose a stable method for detecting the deterioration of a cubic boron nitride (cBN) grinding wheel during cylindrical grinding using AE. We compared the AE signals acquired during grinding from an AE sensor located on the hydrostatic bearing, which supports the grinding wheel shaft, with those from the tailstock spindle. Although positioning the AE sensor on the hydrostatic bearing was found to reduce the AE signal intensity, the AE signal variations were smaller at the same grinding position, and the effect of the grinding position was less than that for the tailstock spindle. Moreover, positioning an AE sensor on the hydrostatic bearing is considered to provide the characteristics of AE signals specifically focused on the changes in cBN on the grinding wheel surface allowing the surface roughness of the workpiece to be estimated during grinding.