Recorded Acoustic Emission Signals Research Articles

Dynamic rock tensile failure under hydrostatic pressure analyzed with wavelet transform of acoustic emission signals

We develop a novel experimental apparatus, integrated into a dynamic testing system, for the real-time monitoring of the failure process of deep rocks. Four groups of Brazilian disc (BD) granite specimens subjected to various hydrostatic confinements (0, 10, 20, 30 MPa) are tested under different loading rates. A strain gauge and an acoustic emission (AE) transducer are attached to each specimen to obtain the real-time failure signals during the impact. The continuous wavelet transform is utilized to analyze the recorded AE signals. It is observed that tensile crack initiation and propagation effectively absorb AE signals produced by stress waves above 50 kHz, which indicates that post-failure AE signals are predominantly generated by the cracks themselves. It is also found that the proportion of AE signals within the 100–150 kHz range increases with the loading rate, suggesting that higher loading rates encourage the emergence of smaller cracks. Moreover, higher hydrostatic stress enhances the dynamic tensile strength of the rock, thereby restraining crack initiation. When the confining stress is applied, the AE signals exceeding 100 kHz are delayed in their occurrence as compare with the failure, contrasting with those within the 50–100 kHz range that appear simultaneously with the failure. This pattern indicates that cracks generate AE signals within the 50–100 kHz range during the tensile failure, while those with frequencies above 100 kHz are produced by tensile crack propagation following the failure.

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

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

Development of an empirical model for damage degree assessment of steel specimens based on the results of acoustic emission signal flow statistical processing

The study is devoted to acoustic emission (AE) method application for monitoring the state of structural materials at the inelastic and ultimate deformation stages. The possibilities of using the standard AE signal parameters recorded at the inelastic and ultimate deformation stages to assess the damage degree of steel specimens were investigated. It was shown that such parameters as the maximum amplitude of the recorded AE signals and their AE activity did not have a clear correlation with the damage degree of products made of structural steel and alloys. This makes it difficult to apply standard methods for assessing the damage degree of structural steels. The feasibility of monitoring the state of damage of 30KhGSA alloyed steel at the inelastic and ultimate stages by the evaluation of partial activity of the high-energy AE signals weight content was presented. The Kolmogorov-Smirnov criterion was used to separate the processes of ductile and brittle fracture.

Cyclic aging monitoring of lithium-ion battery based on acoustic emission

ABSTRACT A timely and accurate understanding of the state of health (SOH) of lithium-ion batteries is essential for early detection and prediction of their potential safety risks. Acoustic emission (AE) can perform dynamic non-destructive testing of lithium-ion batteries under normal charge-discharge cycles, and detect the deformation of internal electrodes of the battery behavior. In this paper, firstly, the whole aging process of lithium-ion batteries and recorded AE signals are monitored. Secondly, the waveform and signal time interval from AE signals during battery aging in the time domain is extracted. Thirdly, the spectrum of AE signals is analyzed. The experimental results show that the total number of AE signals will change with the decline of battery capacity and SOH. The turning point in the time interval waveform of the battery AE signal can be used to identify the constant current or voltage charging mode. The AE signals of batteries mainly exist in two frequency bands, where the content of frequency band 1 is about 10kHz-120kHz, and the range of frequency band 2 is about 130kHz-180kHz. The maximum amplitude of frequency bands 1 and 2 shows their characteristics during the charging and discharging process.

Investigation of the Scale Factor Impact on the Results of Acoustic Emission Monitoring of the Steel Specimens Tension Process

The research is devoted to steel structure diagnostics by the acoustic emission (AE) method. The existing regulatory documents for AE diagnostics of metals and alloys do not take into account some critical factors, among which one is the scale factor should be highlighted. As a result, this can lead to an unreliable assessment of the danger degree of defects in structures when using standard AE diagnostic criteria. This paper presents a quantitative assessment of the scale factor impact on the AE data during the static tension test of steel specimens to failure. Experimental studies were carried out on flat specimens of various thicknesses with a side notch made of high-quality alloyed steel 30 KhGSA. It was established that AE data changed (rise in the AE signals amplitudes and AE activity) within the increase of specimen thickness. Growth in the recorded AE signals cumulative energy was registered with a greater specimen thickness. Partial correlation dependences of the mean count frequency and cumulative energy of AE signals on the specimen thickness were obtained. It was shown that such an effect occurred due to both a general increase in the deformed metal volume and greater strain intensity during the tension of thick specimens. The obtained dependences may contribute to the development of AE diagnostics of metallic materials which is invariant to the scale factor impact.

Acoustic emission signal processing for the assessment of corrosion behaviour in additively manufactured AlSi10Mg

Acoustic Emission (AE) technique is used for characterizing the corrosion behaviour of the AlSi10Mg specimens prepared from recycled feed-material using the Selective Laser Melting (SLM) Process. The corrosion behaviour is studied by exposing the specimens to a simulated salt attack for a period of 240 h. AE signals are recorded for the entire duration and are analysed in their time-frequency domain. Initially, noises are observed in the recorded AE signals. A methodology based on waveform entropy is developed to denoise the signals. The characteristics of the noise are studied to retain any useful information. The results show that some of the low amplitude signals could be misidentified as noise signals; it is rectified by studying the features of the noise such as peak frequency, frequency centroid and peak amplitude. The time-frequency characteristics of the AE signals are then studied along with the morphological features of the specimens exposed to different stages of corrosion. This provides a correlation between the corrosion behaviour such as pit formation, corrosion product formation and the corrosion crack formation and the time-frequency characteristics of AE signals. This information can identify the corrosion behaviour of the AlSi10Mg specimens intuitively.

Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals.

The paper presents the results of tests conducted to identify the damage process in specimens collected from the steel of a gas pipeline. The tests concerned specimens made of S235 steel subject to quasi-static loading—uniaxial tension until failure. Acoustic emission (AE) signals were recorded during the loading process along with force and elongation signals. Sections were collected from previously loaded specimens and subjected to microstructural examinations to determine the nature of material damage at different strain stages. The recorded AE signals were analyzed using the k-means clustering method, as well as time-frequency analysis. The results of metallographic tests and analysis of AE signals identified frequency spectra characteristic of different stages of the process of material damage.

Developing a heterogeneous ensemble learning framework to evaluate Alkali-silica reaction damage in concrete using acoustic emission signals

The monitoring and evaluation of Alkali-silica reaction (ASR) damage in concrete structures are required to ensure the serviceability and integrity of concrete infrastructures such as bridges and dams. The innovation of this paper lies in the development of an automatic ASR monitoring and evaluation approach by leveraging acoustic emission (AE) and a heterogeneous ensemble learning framework. In this paper, ASR was monitored by AE sensors attached to a concrete specimen, which was placed in a chamber with high humidity and temperature. The recorded AE signals were filtered and divided by four ASR phases according to signal strength, crack width and expansion strain. A heterogeneous ensemble network including convolutional neural networks (CNN) and random forest models was employed to learn different features from AE signals and classify the AE signals into their corresponding phases. The results suggest that the proposed model has a high performance and classifies the signals into the assigned phases with high accuracy.

Исследование развивающихся повреждений при изгибном нагружении полимерных композиционных материалов и их идентификация методом акустической эмиссии

Polymer composite materials (PCM) reinforced with glass fibers are very important in many industries due to their unique properties (high chemical resistance and specific strength) with the economic efficiency of use. At the same time, the application of glass fabrics as reinforcing elements ensures high manufacturability. However, unlike crystalline materials, polymer composite materials are subject to the complex process of destruction, which requires the application of non-destructive control methods to get information about the nature of the resulting damage and the kinetics of their accumulation. The paper studies the deteriorations formed in the fiberglass samples molded using T-11-GVS-9 glass fabric and DION 9300 FR binder within static bending deformation accompanied by the acoustic emission (AE) method. In this work, the authors solved the problem of identifying the nature of damages in fiberglass using the Fourier spectra of the recorded AE signals. The authors used the clustering method to estimate their formation and development kinetics. Clustering was performed based on the Kohonen self-organizing map (SOM) algorithm using the values of peak frequencies of the Fourier spectra calculated for the recorded AE signals under static bending deformation of a fiberglass sample up to failure. To ensure the separability of the resulting damages according to the AE parameters, the authors used the loading rate that was ten times lower than that calculated according to the state standard. The study established that the application of frequency representation of AE signals recorded during the fiberglass destruction is effective when solving the task of identifying the nature of the resulting damages. As a result of the study, the authors found that the process of delamination formation during the bending of multilayer laminated plastics acts as a critical mechanism of destruction leading to a significant loss of the polymer composite strength properties.

Sound of a Composite Failure: An Acoustic Emission Investigation

The failure progression characteristics of adhesively bonded Carbon Fiber Reinforced Polymer (CFRP) composites are investigated using Acoustic Emission (AE) technique. Different failure progression modes such as matrix cracking, fiber breakage, delamination and through-thickness crack growth releases AE waveforms in different frequency domains. The characteristic features of these different AE waveforms are studied in Mel Scale, which is a perpetual frequency scale of average human hearing frequency. The recurring noise in the recorded waveforms has been identified more efficiently when the waveforms are analysed in Mel Scale. The recorded AE signals from the adhesively bonded CFRP under static tensile loading are stretched to match the Mel filter banks. The sampling rate of the recorded signal is adjusted from 1 MHz to 20 kHz. Following that, the Mel spectrogram and its cepstral coefficients are used for identifying the different failure modes from which the AE signals are generated. A comprehensive comparison of the AE analysis in Mel scale with conventional waveform processing techniques such as Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Wavelet Packet Transform (WPT) and Hilbert-Huang Transform (HHT) has been made. The advantages and further applications of Mel Scale over traditional waveform processing techniques in defining the failure modes in the composites are also discussed.

Testing of Railway Prestressed Concrete Sleepers Using Acoustic Emission

The article describes the tests of prestressed concrete sleepers made according to the method specified in the requirements of the European standards (EN 13230-2:2009 Railway applications – Track – Concrete sleepers and bearers – Part 2: Prestressed monoblock sleepers) and WTWiO regulations, and an additional measurement method was used – acoustic emission (AE). The purpose of the tests using the acoustic emission method was to verify the results obtained using other test methods de-scribed in the European standard. The use of this method in sleeper tests enables obtaining precise data from the sleeper load test and determination of the characteristic parameters based on the recorded AE signals. Due to the variety of existing sleepers and the development of products in this area: wooden sleepers, composite sleepers, steel sleepers (type Y), the use of the acoustic emission method in research will be a good support and will enable proper assessment of these elements of the railway road.

Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique

Carbon Fiber Reinforced Polymer (CFRP) composites, adhesively bonded in a Single Lap Shear (SLS) configuration, are tested in this study. The damage characteristics are analysed using Acoustic Emission (AE) data recorded during the test. Among the different features of the AE data, the most suitable features are selected using a methodology developed based on the Laplacian scores for feature selection. The selected features are then analysed using k-means clustering algorithm for establishing a relationship among them. Amplitude of the recorded AE signals and their Frequency Centroid (C-Freq) had a strong relationship, which is used for characterizing the damage modes. The cumulative counts of the clustered data of Amplitude vs C-Freq are used for characterizing the damage modes. A comprehensive characterization of the damage modes in each stage of loading and the final failure is made using this method.

Investigation of the Staging of Damage Accumulation in Polymer Composite Materials during Bending and Tensile Tests

The paper presents the results of the study of registered acoustic emission (AE) parameters during static deformation and damaging of polymer composite materials (PCM). Mechanical tests were done by a static tension and a static three-point bend, accompanied by an acoustic emission method. The assessment of the loading rate effect on defects formation processes was done by additional static tension test at rate equal half of recommended by the standard and static three-point bend test at rate ten times lower than that calculated by the standard. Clustering by frequency components of the recorded AE signals with a self-organizing Kohonen map was performed. The characteristics of the types of PCM structure damage by the centroids of the obtained clusters are given. Based on the clusters accumulation during mechanical tests, the stages of damage formation for static tension and static three-point bend, the loading rate effect on the process of damage formation are described.

Using of acoustic emission and video recording for monitoring the kinetics of damage upon compression of composite specimens

The results of studying fracture of a batch of fiber-reinforced polymer (FRP) specimens upon compression are considered. The kinetics of damage and fracture of structural bonds in the FRP package under the impact of compressive load has been studied using acoustic emission (AE) in combination with video recording. Correlations between fractures occurring on the micro-, meso-, and macroscopic levels of the FRP package and the AE events registered at the same time, i.e., their energy parameters, shape, and spectrum were determined. New criterion parameters including the activity of registration of AE events in energy clusters and their weight content were analyzed. The structural-phenomenological approach, implemented by dividing the entire array of acoustic emission data into energy clusters provided the possibility of control of the kinetics of the material fracture using the registration activity and the weight content of AE events in clusters of the lower, middle and upper energy levels. Comparison of the AE events recorded at the stages of loading of the tested specimens with video footage of the fracture of structural bonds in the FRP package made it possible to set up a correspondence between the occurring damage and the recorded AE signals, their parameters, shape and spectrum.

Deep and confident prediction for a laboratory earthquake

Many laboratory fault failure experiments are conducted as analogue of earthquakes, in which most of them are coupled with acoustic emission (AE) as a powerful diagnostic tool for investigating failure precursors. The purpose of this study is to predict time to the next failure in a laboratory fault failure experiment before failures happen based on the instantaneous recorded AE signals. A customized deep learning network comprising the convolutional neural network module and the recurrent neural network module is built and trained using raw AE data directly. No statistical characteristics or handmade features are extracted from raw data, avoiding any possible precursor information losses. More than 600 million AE data from a repetitive fault failure experiment are segmented as several thousand equilong sequences to form training and validation samples. The proposed network delivers satisfactory predicted results with the R2 value 0.55, much better than results using traditional earthquake catalogs method. Results of this study also demonstrate that our network does not prioritize those AE signals collected when failures impend, which is a common bias in traditional earthquake prediction methods. This study definitely holds the promise of using deep learning in earthquake prediction. Further studies are needed when analogous studies proceed to an industrial practice.

Impact Damage Ascertainment in Composite Plates Using In-Situ Acoustic Emission Signal Signature Identification

Barely visible impact damage (BVID) due to low velocity impact events in composite aircraft structures are becoming prevalent. BVID can have an adverse effect on the strength and safety of the structure. During aircraft inspections it can be extremely difficult to visually detect BVID. Moreover, it is also a challenge to ascertain if the BVID has in-fact caused internal damage to the structure or not. This paper describes a method to ascertain whether or not internal damage happened during the impact event by analyzing the high-frequency information contained in the recorded acoustic emission signal signature. Multiple 2 mm quasi-isotropic carbon fiber reinforced polymer (CFRP) composite coupons were impacted using the ASTM D7136 standard in a drop weight impact testing machine to determine the mass, height and energy parameters to obtain approximately 1” impact damage size in the coupons iteratively. For subsequent impact tests, four piezoelectric wafer active sensors (PWAS) were bonded at specific locations on each coupon to record the acoustic emission (AE) signals during the impact event using the MISTRAS micro-II digital AE system. Impact tests were conducted on these instrumented 2 mm coupons using previously calculated energies that would create either no damage or 1” impact damage in the coupons. The obtained AE waveforms and their frequency spectrums were analyzed to distinguish between different AE signatures. From the analysis of the recorded AE signals, it was verified if the structure had indeed been damaged due to the impact event or not. Using our proposed structural health monitoring technique, it could be possible to rapidly identify impact events that cause damage to the structure in real-time and distinguish them from impact events that do not cause damage to the structure. An invention disclosure describing our acoustic emission structural health monitoring technique has been filed and is in the process of becoming a provisional patent.

Diagnosis of damage evolution process for asphalt mixtures using pattern recognition with acoustic emission signals

The fractures in asphalt materials evolving from microcracks directly affect the performance of asphalt pavements. Acoustic Emission (AE) technique plays a crucial role in dynamically monitoring the internal structural changes of stressed materials. This study attempted to characterize the fracture mechanism of asphalt mixtures by identifying the characteristics of recoded AE data. Firstly, the three-point bending tests and AE tests were simultaneously conducted to investigate the damage evolution process of asphalt mixtures through the time characteristics of AE parameters. Secondly, the cusp catastrophe model based on AE energy rate was employed to explore the mutational characteristics of AE process and to evaluate the damage state of asphalt mixtures. Finally, the k-means method was applied to distinguish the patterns of AE signals associating with the specific damage behaviors of asphalt mixtures during the flexural failure process. The results show that abrupt change of AE energy rate and cumulative AE energy could reflect the evolutionary characteristics of damage in asphalt mixtures. The mutational analysis of AE energy rate reveals that the damage state of asphalt mixtures subjected to the continuous external forces would change between instability and stability, which could be served as a complementary approach for further understanding the periodic variation characteristics of damage evolution process based on cumulative AE energy. The k-means method could effectively classify the recorded AE signals of asphalt mixtures in three-point bending tests into three distinctive clusters. The potential mechanisms for the clustered signals corresponding to different damage behaviors of asphalt mixtures could be characterized by the parametric characteristics and the wavelet power spectrum of AE signals.

A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.

Static Tests of Wing Box of Composite Aircraft Wing Using Acoustic Emission and Strain Gaging

Static tests of a two-spar T800 CFRP wing box have been carried out. The object was tested when loaded using the methods of acoustic emission (AE) and strain gaging. Strain in the material was determined in real time in the areas where strain gages were bonded (the panels of wing box, the walls of side members, and the shelves of stringers). The elements of the wing box have been established in which nonlinear strain change is observed and residual deformations are recorded after unloading. The AE method has been used to localize the sources of signals. The coordinates of the sources corresponded to the location of the third rib. Clustering recorded AE signals based on their digitized form has made it possible to group the signals and assign them to sources that correspond to structure failure. It is shown that in the process of loading the wing box, an increase in the structural coefficient of AE signals corresponding to CFRP delamination in the location zone is observed.

Wear monitoring of journal bearings with acoustic emission under different operating conditions

It has been shown that Acoustic Emission (AE) can be used to classify different friction states and identify defects in journal bearings. In addition, it has been demonstrated in experimental setups that AE can be used to estimate the wear volume of sliding lubricated metallic contacts. The aim of our work is to monitor the wear during the operation of a journal bearing. This work deals with the development of a prognosis system for a detection of the wear and its volume by means of AE before an actual fault occurs. For this purpose, a journal bearing test bench was developed, where the oil temperature for the lubrication, load and rotational speed can be set. Experiments were carried out under different operating conditions and lasted up to 20 hours each. To establish a correlation between wear and AE, the wear volume is determined by comparing recorded roundness profiles of the inner race before and after each experiment at multiple positions. The method used is validated by measuring the roughness of the inner race surface, thus confirming the position of the wear. The analysis of the recorded AE signals shows an amplitude modulation as well as bursts. The frequency of the amplitude modulation correlates with the rotational speed even at higher velocities. Bursts in the AE signal are directly related to wear. Various features, e.g. root mean square value and properties of the measured bursts, of the AE signal and their relevance for the prediction of the wear are analyzed. A quantitative estimation of the volume proves to be challenging, while a qualitative estimation of occurrence and severity of wear can be reliably detected.