Acoustic Emission Data Research Articles

APPLICATION OF ACOUSTIC EMISSION METHOD FOR DETECTING DIFFUSION INTERLAYERS IN DIFFERENT WELDED JOINTS

The paper investigates the possibility of detecting specific structural defects of dissimilar welded joints by the method of acoustic emission (AE). Carbide and decarburized interlayers are formed in dissimilar welded joints of austenitic and pearlitic steels during welding and subsequent operation. Diffusion interlayers, as a structural defect, are usually small in size and cannot be detected by traditional non-destructive testing (NDT) methods, while the AE method is promising for detecting such defects. In the present work, we studied the AE data obtained during testing of defect-free specimens of a welded joint and specimens with diffusion interlayers. It has been established that the AE parameters for defective and defect-free welded joints have significant differences, which makes it possible to identify the presence of diffusion interlayers in dissimilar welded joints.

An improved crack probability model for silicon oxide layers using three-parameter Weibull analysis

For semiconductor dielectric layer crack detection in real-time we developed an efficient and very accurate method, which is based on acoustic emission (AE) testing and simulates the wafer probing process. The crack probability is determined using clustered and filtered AE data, that is collected by an advanced sensor-indenter system. The derived distribution function is based on two-parameter Weibull analysis, which is commonly applied for stress-induced failures of brittle materials like silicon oxide (SiO2). During indenter experiments, we observed, that the probability model for a higher number of data points is becoming inaccurate, especially at low fracture forces. Therefore, we introduce a more accurate statistical model for probing-induced SiO2 layer cracks, which is, according to a three-parameter Weibull distribution, considering a location parameter. Experimental results have shown, that the improved distribution function fits much better for predicting low-ppm failure rates of back-end-of-line (BEOL) stacks of CMOS technologies. This enables more reliable pad stack design rules and increases the reliability of complex chip designs regarding mechanical stress during test and assembly processes.

Characterizing damage evolution of CF/PEKK composites under tensile loading through multi-instrument structural health monitoring techniques

This study investigates the damage behavior of autoclave consolidated carbon fiber/Polyetherketoneketone (CF/PEKK) laminates manufactured by the automated fiber placement (AFP) lay-up process. The damage evaluation of autoclave consolidated samples is studied using a multi-instrument nondestructive monitoring approach. The effect of autoclave consolidation on the microstructure of the laminate is examined via void analysis based on density measurement, thermal analysis, and optical microscopy. The results reveal that the void content is achieved as 0.46% and there is 81.81% increase in the degree of crystallinity following the autoclave consolidation. Moreover, acoustic emission (AE), digital image correlation (DIC), and infrared thermography (IRT) results are cross-correlated to further understand the damage development. The evolution of clustered AE data during mechanical loading is used to divide the failure of the laminate into two stages, each of which signifies a different dominancy in failure modes. Scanning electron microscopy (SEM) is employed to associate damage characteristics with failure monitoring techniques.

Acoustic Emission Sensor-Assisted Process Monitoring of Air Plasma-Sprayed Titanium Deposition

AbstractAcoustic emission is a sensing technique that offers the potential benefit for its use as an in situ monitoring tool for a wide range of manufacturing processes. This work attempts to highlight the robustness of using acoustic emission (AE) data for in-line process monitoring of the air plasma spray deposition technique. As part of this study, titanium powder was deposited under various conditions of robot speed, powder feed rates and the influence of these changes were investigated in the signature obtained from the AE analysis. The post-processed AE data showed sensitivity to these changes through variation in frequencies, power spectral densities and the cumulative energy that gets transmitted to the substrate during the spraying process. The AE signal sensitivity was found to be so robust that it picked up even the differences in the substrate conditions i.e., a substrate used for coating in an as received form vs a substrate that was grit blasted before spraying showed identifiable differences in the AE signature. An attempt to convert an AE signal to energy and then analyse the spraying process in light of the cumulative energy is an investigation first of its kind in this research, hitherto not seen in the literature. In light of the extensive experimental data gathered from the in-house deposition data, the influence of the release of elastic strain energy based on the particle states and the impact on the substrate has been discussed thoroughly. The interdependency of surface preparation, feed rate and the robotic gun scanning speed has been discussed in detail as well. Through the data presented in this study, we advocate the use of AE analysis to be a vital contributor and a welcome move towards digitalisation of the thermal spray process for in-process monitoring.

Multivariate Statistical and Correlation Analysis between Acoustic and Geotechnical Variables in Soil Compression Tests Monitored by the Acoustic Emission Technique

In this research, a series of compression tests were carried out, under oedometric conditions, on sand samples prepared with different moisture contents. In these tests, in addition to the usual measurements of the stress and deformation of the sample, a series of acoustic emission sensors were used to monitor the parameters of the acoustic signals coming from inside the sample. This is a rather novel technique with great potential, but sometimes difficult to approach due to the large amount of acoustic emission data generated. In this paper, a correlation and regression analysis has been performed to quantify the correlations between the geotechnical variables and the parameters characterizing the acoustic emissions. The results presented open an interesting horizon of possibilities since, as it has been shown, it is possible to determine the values of the geotechnical properties from the acoustic variables, by means of the regression functions obtained for each type of soil or for each practical case. At the very least, this is a complementary tool in the determination of the mechanical properties of soils subjected to compression, although it could also be useful in those situations in which the monitoring of geotechnical variables describing the tenso-deformational behavior of the soil may be difficult or impossible.

Peridynamic simulations of rock indentation

AbstractThis paper presents an ordinary state‐based peridynamic model to simulate failure in porous rocks and its application in the rock excavation processes. Rock indentation is a fundamental process for mechanical rock fracturing, which is frequently encountered in rock excavation. To model crushing occurring under high triaxial loads observed in the rock indentation experiments, peridynamic model has been extended to consider the pore‐collapse phenomenon. Rock heterogeneity is considered in the simulations by sampling the strength parameters from a probability distribution function. The model is qualitatively validated using the acoustic emission data from the indentation experiments. The developed model is able to form a crushed zone, which serves as the point of initiation for tensile cracks, which split the specimen. Quantitative validation relies on the comparison of the force‐penetration data as well as the indentation pressure‐penetration data obtained from the simulations and experiments performed on Bentheim Sandstone specimens.

Multivariate variational mode decomposition and generalized composite multiscale permutation entropy for multichannel fault diagnosis of hoisting machinery system

Due to the harsh working environment of hoisting machinery system, the fault information of the important components is significantly complex, which leads to the fault signals not being collected completely by using only single channel. To alleviate this problem, acoustic emission (AE) experiments are applied to collect multichannel AE signal of hoisting machinery system. Additionally, a new intelligent fault diagnosis method based on multivariate variational mode decomposition (MVMD) and generalized composite multiscale permutation entropy (GCMPE) is proposed to extract multichannel AE fault features and implement multichannel fault diagnosis of hoisting machinery system. Firstly, based on variational mode decomposition (VMD) and the idea of multichannel AE data processing, MVMD is proposed to process the original multichannel AE signals collected from hoisting machinery system, which can obtain adaptively several multichannel modal components containing discriminative information. Meanwhile, GCMPE is presented to extract the fault information of multichannel modal components obtained by MVMD, which can improve the feature extraction performance of the original multiscale permutation entropy. The experimental results demonstrate the effectiveness and superiority of the proposed method in multichannel fault diagnosis of hoisting machinery system compared with some traditional single-channel analysis and other multichannel analysis methods.

Fracture evolution characteristics of strainburst under different gradient stress

AbstractStrainburst often occurs in the loading process of tangential stress concentration from surrounding rocks after excavation and unloading of deep rock mass. The concentrated tangential stress is relatively larger on the tunnel walls, which decreases towards the interior of surrounding rocks with a certain gradient. To explore the fracture evolution characteristics of surrounding rocks under different tangential gradient stresses and understand various phenomena in the strainburst process, a large‐scale true‐triaxial gradient and hydraulic‐pneumatic composite loading rockburst test device was adopted to carry out the strainburst tests under four different gradient stresses. Based on the macroscopic failure phenomena, distribution of rockburst debris, macro‐micro morphology of failure cross section, and monitored data of acoustic emission (AE), the fracture evolution of rockburst specimens was analyzed under different stress gradient loadings. The results indicate that the stress gradient has an obvious influence on the fracture characteristics, resulting in different rockburst phenomena. With the increase of stress gradient, the failure stress of rockburst decreases, while the dynamic failure characteristics of rockburst trends to be significant. The increase of stress gradient contributes to higher fragmentation degree of debris, more uniform distribution and better continuity. The total number of cracks in the model decreases with the rise of the stress gradient, and the proportion of shear failure increases, which is the main reason for the enhancement of the rockburst intensity.

Recognition of shear and tension signals based on acoustic emission parameters and waveform using machine learning methods

Acoustic emission (AE) technology is widely used to monitor the damage evolution of rock. Identifying AE signals is crucial to reveal the rock cracking mechanism. The current tension and shear signal discrimination methods primarily rely on AE parameters. However, the differences between AE parameters and waveform images used as inputs require further exploration. Acoustic emission data from Brazilian splitting and direct shear tests of red sandstone were collected. A decision tree was applied to classify the signals based on the AE parameters: Rise time (RT), Counts, Amplitude, Peak frequency (PF), and absolute energy. Three typical CNN-based networks (InceptionV3, ResNet50, and VGG19) were used to classify the AE signals using images of waveforms, reconfigured waveforms, and spectrograms generated by wavelet packets. The results showed that using AE parameters as input to distinguish tensile and shear signals yields an accuracy of up to 88%, increasing recognition accuracy when using PF as input. In addition, increasing the number of input parameters to the decision tree can improve average recognition accuracy, with single, double, and three parameters being 65%, 78%, and 82%, respectively. However, when the number of parameters exceeds three, the accuracy improvement is minimal, with four and five parameters being 84% and 86%, respectively. The CNN-based networks outperform the decision tree in terms of recognition accuracy. It is recommended that the VGG19 network and waveform are adopted as input to identify the tension and shear AE signals. In contrast, using the spectrogram as input enhances the stability of the model, particularly for the validation set, although the improvement is limited.

Development of methods for monitoring and predicting the strength of concrete at an early age by acoustic emission method

Introduction. The description of the main results of the research work carried out in JSC Research Center of Construction on the topic “Development of methods for monitoring and predicting the strength of concrete at an early age by acoustic emission method” is given.Aim. The aim of the work is to obtain experimental data for the development of methods for monitoring and predicting the strength of concrete at an early age by acoustic emission.Materials and methods. The research was carried out on concrete mixtures made of heavy and fine-grained concrete with mineral and chemical additives. In the process of concrete hardening the acoustic emission method was used. Acoustic emission monitoring was carried out continuously for several days.Results. According to the results of the study, the most informative parameters of acoustic emission data that correlate with the strength of concrete have been identified. Automated algorithms for concrete strength monitoring have been developed. Based on the results of measuring the propagation velocity of acoustic waves and the decrease in the attenuation coefficient of acoustic signals, as well as the output of these values to the plateau, the periods of completion of setting of concrete mixtures are determined. During the experiments optimal frequency characteristics of acoustic emission sensors were determined.Conclusions. The results of the research allow us to introduce into the practice of construction a new approach for monitoring the strength properties of concrete during its hardening using the acoustic emission method. Proposed approach can be carried out remotely, without the presence of an operator. The advantages of this approach also include its use in hard-to-reach places where the use of other testing methods is difficult.

Study on stage characteristics of hydraulic concrete fracture under uniaxial compression using acoustic emission

ABSTRACT Acoustic emission (AE) can monitor and evaluate the cracking situations of hydraulic structures for it is real-time and non-destructive. However, a current challenge of this approach is to accurately identify the various stages during the cracking process. The innovation of this paper lies in the application of AE data in analyzing the stage characteristics during the whole process of hydraulic concrete fracture. First, the slope changing of the cumulative ringing count curve was used to determine the cracking stages of hydraulic concrete. Second, the function of inter-event times (denoted as F-function) was used to further investigate the AE characteristics proximity to the final fracture. Last, the spatial-temporal-energy evolution of cracks was studied. The results show that: (1) The whole process of hydraulic concrete failure shows an obvious stage characteristic from the perspective of AE feature and it can be distinguished to five stages by the slope change of the cumulative ringing count curve; (2) The F-function can be used to identify the last two cracking stages, and it is capable of early warning the macroscopic cracking in ahead of b-value; (3) The spatial-temporal-energy distribution of cracks helps to better understanding the generation and evolution for cracks inside hydraulic concrete.

Transfer learning for predictive quality in laser-induced plasma micro-machining

In laser-induced plasma micro-machining (LIPMM), a focused, ultrashort pulsed laser beam creates a highly localized plasma zone within a transparent liquid dielectric. When the beam intensity is greater than the breakdown threshold in the dielectric media, plasma is formed which is then used to ablate the workpiece. This paper aims to facilitate in-situ process monitoring and quality prediction for LIPMM by developing a deep learning model to (1) understand the relationship between acoustic emission data and quality of micro-machining with LIPMM, (2) transfer such understanding across different process parameters, and (3) predict quality accurately by fine-tuning models with a smaller dataset. Experiments and results show that the relationship learned from one process parameter can be transferred to other parameters, requiring lesser data and lesser computational time for training the model. We investigate the feasibility of transfer learning and compare the performance of various transfer learning models: different input features, different CNN structures, and the same structure with different fine-tuned layers. The findings provide insights into how to design effective transfer learning models for manufacturing applications.

Data analysis techniques for the visualization and classification of historical vehicle engines’ health status using data-driven solutions

In the field of cultural heritage, the use of non-destructive techniques to determine the state of conservation of an artifact is of the utmost importance, to avoid damage to the object itself. In this paper, we present a data pipeline and several machine learning techniques for the visualization, analysis and characterization of engines in historical vehicles. The paper investigates the use of vibro-acoustic signals acquired from the engines in different states of conservation and working conditions to train machine learning solutions. Data are classified according to their state of health and the presence of anomalies. The t-SNE algorithm is used for dimensionality reduction for data visualization. The machine learning algorithms tested showed encouraging performance in associating acoustic emission data with the engine signature, the type of anomaly and the working conditions. Nevertheless, a larger dataset would allow us to improve and strengthen the results.

Application of Acoustic Emission as Damage Assessment Technique for Performance Evaluation of Concrete Structures: A Review

A significant increase in existing inspection efforts and the creation of more resilient structures using built-in sensors and systems is essential for accurate and reliable assessment and monitoring of structural conditions in order to minimize the risk of catastrophic failure. The acoustic emission (AE) technique could immensely benefit the damage assessment, real-time checking of structural integrity under operating conditions, and identification of the operational risk factors along with the causes of initiation and development of flaws. Therefore, this article presents a comprehensive overview of AE as a potential damage assessment and localization technique to determine the performance and behavior of reinforced concrete (RC) members along with an overview of the latest research in computational AE damage modeling and machine learning tools to optimize the accurate interpretation of voluminous AE data. The primary aim of this review is to identify specific gaps in the literature pertaining to concrete structures that need immediate attention of the researchers working in this domain. The identification of damage mechanisms based on AE events and parameters covered in this review would enable researchers to identify damage sources nondestructively in situ and produce large data sets for detailed statistical analyses of the effects of small-scale local damages in large-scale concrete structures. This review will also be helpful for practicing engineers to choose between different available AE parameters based on the limitations and advantages of these parameters for specific inspection and maintenance requirements of various types of RC structural elements, loading conditions, and failure.

Investigation of concrete crack kinematics through probability density field of the location of acoustic emission events

Monitoring or identifying structural cracks is crucial for assessing the health of existing concrete structures. Key information about structural cracking encompasses the location of the crack and its kinematics, which include movements perpendicular and parallel to the crack face. Acoustic emission (AE) is a sensitive technique for detecting the location of internal concrete cracking. However, the state-of-the-art AE monitoring methods offer limited information on crack kinematics, restricting the use of AE in crack assessment. To bridge this gap, this paper uses a recently proposed AE data analysis method that quantifies the spatial distribution of AE events along a crack probabilistically. This method uses a parameter referred to as the probability density of AE events (pdAE). By combining pdAE and crack kinematics measured by digital image correlation in a series of real-scale concrete beam tests, this paper investigates the relationship between AE events and crack kinematics. The analysed cracks are generated by a combination of bending moment and shear forces, as commonly observed in real structural concrete members. We find that the amount of AE events is not only related to crack width (the crack movement perpendicular to the crack face), as most literature suggests, but also to the complete crack kinematics throughout the loading history of the member. We then provide a physical explanation for the observed relationships between concrete crack kinematics and the quantity of AE events.

On monitoring fretting fatigue damage in solid railway axles by acoustic emission with unsupervised machine learning and comparison to non-destructive testing techniques

Railway axles are safety-critical components of the rolling stock and the consequences of possible in-service failures can have dramatic effects. Although this element is traditionally designed against such failures, the initiation and propagation of service cracks are still occasionally observed, requiring an effective application of non-destructive testing and structural health monitoring approaches. This paper investigates the application of structural health monitoring by acoustic emission to the case of solid railway axles subject to fretting fatigue damage. A full-scale test was performed on a specimen in which artificial notches were suitably manufactured in order to cause the initiation and evolution of fretting fatigue damage up to the stage of relevant propagating fatigue cracks. During the test, both periodical phased array ultrasonic inspections and continuous acquisition of acoustic emission data have been carried out. Moreover, at the end of the test, the specimen was inspected, analyzed and evaluated by visual inspection and magnetic particles testing, while acoustic emission raw data were post-processed by a special unsupervised machine learning algorithm based on an Artificial Neural Network. It is demonstrated that the proposed methodology is very effective to detect the onset of crack initiation in a non-invasive and safe way.

Comparison of Acoustic Emission Data Acquired During Tensile Deformation of Maraging Steel M250 Welded Specimens

Safety and reliability are primary concerns in launch vehicle performance due to the involved costs and risk. Pressure vessels are one of the significant subsystems of launch vehicles. In order to have minimal weight, high strength material viz. maraging steel M250 grade is used in realizing the pressure vessel casing hardware. Despite the best efforts in design methodology, quality evaluation in production and effective structural integrity assessment is still a farfetched goal. The evolution of such a system requires, first, identification of an appropriate technique and next its adoption to meet the challenges posed by advanced materials like maraging steels. In fact, a quick survey of the available Non-Destructive Evaluation (NDE) techniques suggests Acoustic Emission (AE) as an effective structural integrity assessment tool capable of identifying any impending failure or degradation at an earlier stage. Experience shows that the longitudinal welds in the pressure vessels are quite vulnerable to failure due to the fact that they experience the maximum stress (i.e. hoop stress). Loading welded tensile samples are quite synonymous to the hoop stress experienced by longitudinal welds. An attempt is made to compare the Acoustic Emission data acquired during tensile deformation of maraging steel welded specimens. A total of 16 welded specimen’s with known defects were studied for their tensile behaviour is in connection with Acoustic Emission data. The lowest failure load was 70.5 kN and the highest being 84.8 kN. AE activity graphs viz. cumulative AE activity, hit rate, energy rate, count rate, AE amplitude history, AE count history, AE energy history, amplitude-count correlation and hit amplitude distribution have been investigated and salient features with respect to the data have been critically studied and relevant correlations are arrived at.

Predicting microseismic, acoustic emission and electromagnetic radiation data using neural networks

Microseism, acoustic emission and electromagnetic radiation (M-A-E) data are usually used for predicting rockburst hazards. However, it is a great challenge to realize the prediction of M-A-E data. In this study, with the aid of a deep learning algorithm, a new method for the prediction of M-A-E data is proposed. In this method, an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data, and then the M-A-E data can be predicted. The predicted results are highly correlated with the real data collected in the field. Through field verification, the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

Fracture and energy evolution of rock specimens with a circular hole under multilevel cyclic loading

Underground structures gradually face increasing multilevel cyclic loading as construction progresses. In this paper, the fracture and energy evolution of rock specimens with a circular hole under multilevel cyclic loading were investigated using an experimental-theoretical approach. A series of multilevel cyclic loading tests were conducted on two types of rock (sandstone and granite) using the MTS 815 rock mechanics test system. Digital image correlation (DIC) and acoustic emission (AE) techniques were employed to monitor the fracture behavior of the rock with deformation and acoustic emission data recorded respectively. The total energy density, elastic energy density, and dissipated energy density of specimens are obtained by theoretical calculation. The results indicate that, despite the similarity in cracking sequence and fracture evolution of rock specimens with a circular hole between multilevel cyclic loading and uniaxial compression tests, there are differences in the AE activity under these two loading conditions. Additionally, the cracks around the hole under multilevel cyclic loading display a distinct “open-close-reopen” behavior. The energy density of rock specimens with a circular hole grows nonlinearly with stress. The elastic and dissipative energy density exhibits a linear relationship with the total energy density before rock failure. The fitting value of the elastic and dissipative energy density can be regarded as the energy storage coefficient and the energy dissipation coefficient, which could be further used to quantify the energy storage and dissipation capacities of rock. Under multilevel cyclic loading, the propagation of remote fractures and their coalescence with the shear fractures is the primary resource of AE energy, with only a few supplies given by micro-fissure closure and primary fractures. The dissipation energy in the rock structure can be described by the growth of AE energy. The findings indicate that there are distinct stages of energy release during the fracture evolution of cracks around the circular hole. These stages can be predicted by changes in AE energy or macroscopic energy, revealing the evolution stages of cracks and the modes of failure.

Comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation signals of rock burst based on deep learning

Microseismic, acoustic emission, and electromagnetic radiation monitoring methods are often used to monitor rock burst disasters in coal mines. In the process of coal mining, the time series characteristics and amplitude characteristics of microseismic, acoustic emission, and electromagnetic radiation data are mainly used to identify rockburst risk, but the results of risk identification through the three monitoring methods are quite different. Consequently, the accurate and comprehensive early warning of rock burst risk is still an urgent problem to be solved. The development of deep learning provides a new means for intelligent early warning of rock burst risk. In this paper, a comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation (MS-AE-EMR) signals of rock bursts was proposed based on a deep learning algorithm. This method uses long short-term memory recurrent neural networks (LSTM-RNNs) to intelligently identify the MS-AE-EMR precursor signal of rock burst risk, predicts the MS-AE-EMR signal by a convolution neural network (CNN), analyses the MS-AE-EMR precursor signal of rock burst risk through the data analysis method and obtains the risk coefficient of rock burst. Moreover, by using the MS-AE-EMR original signal and risk coefficient, it trains the multi-input CNN and inputs the predicted signal into the trained multi-input CNN to obtain the predicted risk coefficient of rock burst. Analysing the risk coefficient completes the comprehensive early warning of the MS-AE-EMR signal of rock burst. After field verification, the RNN-based comprehensive early warning method of the MS-AE-EMR signal can respond positively to rock burst risk and capture the information in advance. Therefore, this method is of great significance for accurate monitoring and early warning of rock burst in coal mines.