Analysis Of Acoustic Emission Signals Research Articles

Research of the metals elasticoplastic cyclic deformation process at reduced temperature

In the present work the test results of steel samples 20 and 45 at a temperature 20° C and -30°C when exposed to variable loads are shown. Fractographic studies of breaks were carried out using optical and electron microscopy. A comprehensive analysis of the materials destruction process, based on the analysis of acoustic emission signals, was also conducted. To estimate the damage of metals the algorithm that uses indicators of fractal dimension and informational entropy of the acoustic emission signal is proposed. The research results showed that the characteristics of fractal dimension and information entropy expand and supplement the capabilities of acoustic methods in the problems of evaluating the performance of materials with low-cycle fatigue at low temperatures.

Detection and estimation of valve leakage losses in reciprocating compressor using acoustic emission technique

Valve problems in reciprocating compressor are often resolved through parameter analysis of acoustic emission (AE) signals or intelligent system without examining the nature of signals related to its source. This study intended to explore the potential of AE signal for the measurement of valve flow rate in order to quantify the severity of valve problems. The study started with time-frequency analysis of AE signal through discrete wavelet transform, followed by valve condition classification and valve flow rate estimation for faulty valves operated from 450 to 750 rpm. The k-nearest neighbours (KNN) and support vector machine (SVM) classification algorithms are employed to classify the valve conditions before estimation of valve flow rate through regression model. The prediction accuracy of valve flow models is found between 74.5 and 98.8%. Finally, the valve leakage loss can be estimated by computing the difference of flow rate between the measured valve and its baseline (normal valve) using AE parameter.

Delamination Analysis of Carbon Fiber/Epoxy Composite Laminates Under Different Loading Rates Using Acoustic Emission

This work studies delamination behaviors of carbon fiber/epoxy composite laminates under different loading rates using acoustic emission (AE) that shows large advantage for dynamic monitoring of instantaneous defects by analyzing characteristic signal parameters. Generally, rate-dependent damage behaviors of composites poses challenges to AE signal analysis and failure mode identification during a short time. In order to study dynamic mixed-mode delamination behaviors, the single-leg three-point bending tests for carbon fiber/epoxy composite laminates with initial intralaminar and interlaminar truncation sizes under different loading rates are performed. Effects of the loading rate and layup pattern on the delamination behaviors of composites are studied by analyzing the AE responses including the amplitude and energy.

Surface quality monitoring in abrasive water jet machining of Ti6Al4V–CFRP stacks through wavelet packet analysis of acoustic emission signals

Machining such as trimming and drilling of aerospace composite structures is often required to meet the intended geometric tolerances and functional requirements. Abrasive water jet (AWJ) is a primary candidate for high speed machining of difficult-to-cut materials. The AWJ process performance is sensitive to the online faults and non-optimal process parameters, necessitating efficient techniques for online process control. In this study, acoustic emission (AE) signals are used to monitor AWJ machining of stacked titanium-CFRP. Owing to the non-stationary nature of the AE signals, this work is focused on the precision-driven predictive approach in simultaneous time-frequency domain. The AE signals were analyzed using wavelet packet transform (WPT), and an algorithm was proposed to identify and characterize these signals. Thirty-five different mother wavelets and decomposition levels up to 10 were used. The wavelet parameters (mother wavelet and decomposition) were deemed optimal when the identified signal characteristics could strongly correlate with the process parameters and kerf wall quality (surface roughness). Coiflets and Symlets were identified as the optimal wavelets with energy-entropy coefficient as the qualifying characteristic of the wavelet packet resulting in R2 > 90%. A comparative study was conducted to qualify the proposed algorithm against standard time domain analysis measures. The maximum R2 and CV (RMSD)—coefficient of variation of root mean square deviation for time domain was observed as 88.6% and 12.5% respectively as opposed to R2 = 97.12% and CV (RMSD)= 6% for the proposed WPT algorithm. Overall, an efficient algorithm was proposed in monitoring the process quality and controlling the process parameters based on the identified signal signatures.

Study on Spectrum Characteristics and Clustering of Acoustic Emission Signals from Rock Fracture

Acoustic emission signals are relevant to the process of rock failure. In this paper, acoustic emission waveform signals during rock loading are acquired through uniaxial compression test of granite in laboratory. Short-time Fourier transform is used to analyze the acoustic emission signals during rock fracture to obtain the peak frequency. Based on the peak frequency of acoustic emission, four types of acoustic emission signals are classified by using fuzzy C-means method. The parameters of different types of acoustic emission signals are analyzed, which include ring count, duration, amplitude and energy. Meanwhile, the progressive propagation of surface cracks in rock specimens is quantitatively analyzed by digital image correlation (DIC) technology. The result shows that different types of acoustic emission signals correspond to different strength of rock fracture. Before rock fracture, high-count, long-duration and high-energy precursory characteristic signals appear intensively. The event density $$ {\text{Den}}_{t} = 1 $$ is taken as the early warning threshold of rock fracture through the quantitative analysis of acoustic emission signal. The present results of the research show the usefulness of the DIC and acoustic emission techniques in experiment of that type.

Mode I Interlaminar Fracture of Glass/Epoxy Unidirectional Laminates. Part I: Experimental Studies.

The paper presents experimental tests of unidirectional double cantilever beams made of a glass fiber reinforced (GFRP) laminate. The critical value of the strain energy release rate (c-SERR or GIC), i.e., the mode I fracture toughness of the considered material was determined with three different methods: the compliance calibration method (CC), the modified compliance calibration method (MCC), and the corrected beam theory (CBT). Due to the common difficulties in precise definition of delamination initiation force, the Acoustic Emission (AE) technique was applied as an auxiliary source of data. The failure process was monitored, as well, in order to detect and identify different damage phenomena. This was achieved through a detailed analysis of the raw AE signal subjected to fast Fourier transformation (FFT). The frequency spectra revealed three dominating frequency bands with the basic one described by the average value of 63.1 kHz, revealing intensive delamination processes. This way, not only precise values of the critical SERR, but also the information on damage evolution during propagation of delamination, was obtained.

Using ANN and SVM for the Detection of Acoustic Emission Signals Accompanying Epoxy Resin Electrical Treeing

Electrical treeing is one of the effects of partial discharges in the solid insulation of high-voltage electrical insulating systems. The process involves the formation of conductive channels inside the dielectric. Acoustic emission (AE) is a method of partial discharge detection and measurement, which belongs to the group of non-destructive methods. If electrical treeing is detected, the measurement, recording, and analysis of signals, which accompany the phenomenon, become difficult due to the low signal-to-noise ratio and possible multiple signal reflections from the boundaries of the object. That is why only selected signal parameters are used for the detection and analysis of the phenomenon. A detailed analysis of various acoustic emission signals is a complex and time-consuming process. It has inspired the search for new methods of identifying the symptoms related to partial discharge in the recorded signal. Bearing in mind that a similar signal is searched, denoting a signal with similar characteristics, the use of artificial neural networks seems pertinent. The paper presents an effort to automate the process of insulation material condition identification based on neural classifiers. An attempt was made to develop a neural classifier that enables the detection of the symptoms in the recorded acoustic emission signals, which are evidence of treeing. The performed studies assessed the efficiency with which different artificial neural networks (ANN) are able to detect treeing-related signals and the appropriate selection of such input parameters as statistical indicators or analysis windows. The feedforward network revealed the highest classification efficiency among all analyzed networks. Moreover, the use of primary component analysis helps to reduce the teaching data to one variable at a classification efficiency of up to 1%.

The 8AE-PD computer measurement system for registration and analysis of acoustic emission signals generated by partial discharges in oil power transformers

The 8AE-PD computer measurement system for registration and analysis of acoustic emission signals generated by partial discharges in oil power transformers

Prediction of critical rupture of plasma-sprayed yttria stabilized zirconia thermal barrier coatings under burner rig test via finite element simulation and in-situ acoustic emission technique

In the current work, the yttria stabilized zirconia (YSZ) thermal barrier coatings (TBCs) have been fabricated by atmospheric plasma spraying. The failure mechanism of the TBCs during burner rig test (BRT) has been investigated via finite element modeling (FEM) and in-situ acoustic emission (AE) technique systematically. During the BRT, the oxygen-propane flame was heated onto the surface of the coating samples with the dwell time 3 min, then the surface and the backside of the coating samples were cooled by the compressed air simultaneously for 3 min. This can be viewed as one thermal cycle. Then repeat the same heating and cooling process. The history and distribution of the temperature along the through-thickness direction of the coating samples has been obtained via FEM, and the residual stress at different stages has been also calculated. The FEM results have indicated that large tensile stress and compressive stress existed at the heating stage and cooling stage of each thermal cycle, respectively. The crack propagation tends to occur at the initial period of heating stage in the BRT process. The in-situ acquisition of the AE signals during the BRT has also been adopted. The crack propagation patterns have been obtained based on the analysis of AE signals. The filtering technique has been used to exclude the disturbance of the noise in the process of BRT in order to capture the actual and effective AE signals which represent the crack propagation and the deformation of the coating systems. Based on the characteristic waveform of effective AE signals, the Fast Fourier Transformation (FFT) and wavelet transformation has been adopted to analyze the key distribution range of the amplitude and frequency. The investigation results indicate that the acquired AE signals during BRT mainly include signals which came from the plastic deformation of the substrate and creep of each layers, propagation of the vertical crack and propagation of the interfacial cracks (horizontal crack). The detailed failure mechanisms of the TBCs during BRT have been clarified in the current work.

Damage mechanisms characterization of flax fibers–reinforced composites with interleaved natural viscoelastic layer using acoustic emission analysis

In this paper, the static and fatigue behavior of flax fiber-reinforced composites with and without an interleaved natural viscoelastic layer are investigated. Viscoelastic composite plates consist of a soft natural viscoelastic layer which is confined between two identical flax fiber reinforced composites. Different stacking sequences of specimens are tested with uniaxial tensile loading until failure. The mechanical behavior and the acoustic activity of damage sources in various configurations with and without a viscoelastic layer are compared. The analysis of acoustic emission signals and the macroscopic and microscopic observations led to the identification of the main acoustic signatures of different damage modes dominant in each type of composites (with and without a viscoelastic layer). These results allow better identification of the influence of the impact of a viscoelastic layer on the mechanical behavior of different composites. In addition, static and fatigue flexural behavior of unidirectional composites with and without viscoelastic layer are characterized in 3-point bending tests. The effects of viscoelastic layer on the stiffness, hysteresis loops, and loss factor are studied for various numbers of cycles during cyclic fatigue.

Cluster analysis of acoustic emission signals and tensile properties of carbon/glass fiber–reinforced hybrid composites

Understanding the damage and failure of carbon/glass epoxy hybrid woven composites under tensile loading based on acoustic emission signals is a challenging task in their practical uses. In this study, an approach based on fuzzy c-means algorithm is proposed to process the acoustic emission signals from tensile loading of composites monitored by combining acoustic emission technology and digital image correlation method. The results show that the acoustic emission signals from tensile loading can be divided into three clusters. The three clusters correspond to three kinds of damage modes including matrix cracking, fiber/matrix debonding, delamination, and fiber breakage. By comparing the acoustic characteristics of these classes, a correlation procedure between the clusters and the damage mechanisms observed is proposed. Meanwhile, it can be found that debonding and fiber break signals for glass fiber correspond to a lower frequency range than that for carbon fiber. Moreover, the method combining acoustic emission and digital image correlation can effectively monitor the damage process of the specimen both on the inside and outside, which can provide a reference for the health monitoring of composite structure.

Mechanical behavior and damage monitoring of pultruded wood-cored GFRP sandwich components

This study presents the efficient manufacturing of rectangular wood-cored GFRP sandwich components by pultrusion process. Unlike the traditional pultruded sections by use of the roving, three types of mats (strand mat, uni-axial fabric and tri-axial fabric) were adopted to improve the web-flange junction integrity of GFRP hollow-section (HS) and sandwich section (SS) specimens. The mechanical behavior of glue-laminated Douglas-fir (DF), HS, and SS specimens under four-point bending was investigated. The test results showed that DF and HS specimens failed by tensile rupture and web buckling, respectively. The SS specimens exhibited excellent ductility and load-carrying capacity resulting from three main reasons: a) the step-by-step wrinkling of GFRP web, b) the plastic deformation capacity of the timber under the local compression parallel and perpendicular to the grain and c) the bond-slip between the timber core and the GFRP inner surface. Meanwhile, the acoustic emission (AE) system was applied to detect the damage signals. Comparisons between failure progressions and AE signal analysis suggest that cumulative AE energy was more effective than hits to characterise the damage stages. A 2-D damage location model was also developed. The predicted damage locations agreed well with the experimental failure modes.

Size Effect of the Post-Peak Cyclic Behavior of Plain Concrete in Uniaxial Tension with Acoustic Emission Technique

Abstract At present, studies about the size effect of concrete mainly focus on macroscopic mechanical behaviors like strength and fracture energy, while research on the size effect of the cyclic nonlinear properties of concrete is very rare. To further analyze the ambiguous concept of specimen size, three types of precut cylindrical concrete specimens with the same diameter but different heights, the same height but different crack depths, and the same aspect ratio but different volumes are used for the uniaxial post-peak cyclic tensile test. At the same time, the acoustic emission (AE) device is used to monitor and collect the AE signals in the loading process. According to the test results, no size effect exists in the accumulation of residual deformation, but the degradation of the unloading modulus is related to the specimen diameter. Meanwhile, the Kaiser effect is found in the analysis of AE signals. After the data analysis, a post-peak cyclic tensile model of concrete is proposed based on the size effect and damage evolution. The quantitative relationship among the model parameter, specimen aspect ratio, and damage is calculated. Finally, a good comparison is found between the predicted model and experimental result.

On-Line Registration of Damages in Composite Materials with Fiber-Optic Acoustic Emission Sensors

This paper presents the results of the analysis of acoustic emission signals registered by using fiber-optic sensors during the propagation of ultrasonic waves in a polymer composite material. Fiber-optical sensors for acoustic emission were constructed according to the scheme of an adaptive holographic interferometer. Unlike piezoelectric sensors, fiber-optic sensors are distributed type sensors. This imposes certain features on the detection of signals in plates in which fiber-optic sensors are embedded. It is established that the difference of the spectrum of acoustic emission signals is registered in different directions of wave propagation. The local maximums of the spectrum are determined by the mode of wave propagation in the plate in different directions and the location of fiber-optic sensors.

Modeling the Propagation of Elastic Ultrasonic Waves in Isotropic and Anisotropic Materials when Excited by Various Sources

This work is devoted to the investigation of the characteristics of acoustic emission waves to establish their relations with the parameters of the fracture of the structure of the material. The paper presents the results of the analysis of acoustic emission signals recorded during the propagation of ultrasonic waves in metal sheet materials using piezoelectric sensors. The specimen was a rectangular aluminum plate. The piezoelectric sensor recorded acoustic emission signals generated by the Hsu-Nielsen source. The piezoelectric sensor is located in the center of the aluminum plate. Then sources are generated with different hardness to model various kinds of cracks at each specific location. To determine the informative component of a useful acoustic emission signal, the Morlet wavelet transformation was used. When excited by a fracture pencils of different hardness, the magnitude of the wavelet differ in the energy and intensity of the spectrogram.

Investigation on reinforcement of aligned steel fiber on flexural behavior of cement-based composites using acoustic emission signal analysis

This study is aimed at exploring the mechanism of the reinforcement of aligned steel fibers on the mechanical properties of cement-based composite by acoustic emission (AE) signal analysis. Flexural tests were carried out and the results shown that the flexural strength of aligned steel fiber reinforced cement-based composite (ASFRC) increases by around 50% over that of conventional steel fiber reinforced cement-based composite (SFRC) with randomly distributed steel fibers inside. In the flexural tests, the interior deterioration of the specimens was monitored by AE. AE signal analysis results show that, compared to the conventional SFRC, the ASFRC specimens with aligned steel fibers have larger number of signals originated by the steel fiber pullout on the fracture sections, which indicates that there are more steel fibers working. It is also found that in the specimens with aligned steel fibers the local damage of matrix around the fibers in the fracture section caused by the pullout of steel fibers is less than that in the specimens with randomly distributed steel fibers. The difference in the local crushing of matrix and the number of steel fibers (AE signals) in fractured sections between the ASFRC and SFRC specimens may be the main reasons that ASFRC is superior to SFRC in mechanical performance.

The analysis of acoustic emission signals detected during the loading of cement-based materials

The article deals with the experimental investigation of changes in the internal structure of materials subjected to loading. The main aim of the experiments was to identify crack formation and their propagation within the elastic part of the stress-strain diagram and define its significance and influence on the overall failure behaviour of specimens under loading. The specimens' overall behaviour was monitored using the non-destructive testing method of acoustic emission (AE). Three different cement-based composites, which differed mainly in the maximum value of the expected static modulus of elasticity, were manufactured for the purpose of experiments. Based on the results, it can be stated that the increased number of micro-cracks in the specimens' internal structure is reflected in an increased number of AE events. The specimens with a lower value of the static modulus of elasticity showed higher AE activity during all the periods of loading being investigated. In addition, the AE energy released due to the formation of micro-cracks was the highest in specimens with a lower value of the modulus of elasticity.

USING CLUSTERING METHODS FOR PROCESSING ACOUSTIC-EMISSION INFORMATION

The analysis of the developed methods of cluster analysis of acoustic emission signals (AE), performed during the welding process and in the process of strength testing of composite samples and structures, was performed. Clustering in digital form, informative parameters of AE signals, front edge rise rate, as well as dynamic and two-stage clustering are considered. The examples of the use of the developed cluster analysis methods in the control of welding defects, composite samples and elements of aircraft structures during static and cyclic testing are shown. The advantages and disadvantages of various clustering methods for processing AE information are determined.

The study of acoustic emission waves generated from different types of sources

Abstract This paper presents the results in the analysis of acoustic emission signals that registered during the propagation of acoustic waves in the thin metal plate. The sources are excited by the fracture of mechanical pencil lead (Hsu-Nielsen) with different hardness to model various kinds of cracks at each specific location. To research and identify different types of fracture in the sample, the analysis was performed based on the method of the wavelet decomposition in this research. These results can be used to establish relation between the characteristics of acoustic signals and the parameter of the fracture of structure in the material.

Entropy Method for Structural Health Monitoring Based on Statistical Cause and Effect Analysis of Acoustic Emission and Vibration Signals

Acoustic emission (AE) and vibration signal are significant criteria of damage identification in structural health monitoring (SHM) engineering. Multi-disciplinary knowledge and synergistic parameter effects are technical challenges for damage assessment modelling. This study proposes a structural damage cause-and-effect analysis method based on parameter information entropy. Monitoring data is used to form a time-domain feature wave (TFW). The structural strength degradation factor (DF) would be used to define structural damage information entropy (SDIE) vector. The structural damage cause and effect model is developed in a probability sense. A fatigue index is adopted for damage assessment, and a causal strength index is proposed to locate the most likely damage cause. A sandstone-truss structure experiment was conducted to show that the proposed method is effective for damage evaluation and the experimental results provide strong support. This is a statistical damage identification method based on causal logic uncertainty, meaning a complicated mechanics calculation can be avoided.