Acoustic Emission Spectra Research Articles

Phenomenological study of acoustic emissions generated by gear meshing in planetary gearboxes

Abstract This study explores the frequency content of the envelope spectrum of acoustic emission (AE) signals from planetary gearboxes. Since AE signals contain continuous AE and burst-type AE, it is important to determine how different types of amplitude modulation affect these components. Phenomenological simulations of the AE signals were conducted to analyze the influence of the modulations. This approach quantitatively determines how the different harmonics in the envelope spectrum change depending on the level and type of modulation. The modulations under analysis are sinusoidal and single square wave. The simulations indicate that sidebands at the planet gear pass frequency around the gear mesh components are mainly attributed to the modulation of AE bursts, while harmonics at the planet gear pass frequency are primarily attributed to the modulation of the continuous AE. An application case to a wind turbine gearbox is also presented. The study highlights the influence of normal modulations or fault-related modulations in the AE envelope spectra from planetary gearboxes. It provides meaningful insights for establishing AE technology as a consistent condition monitoring technique for planetary gearboxes. The main practical value of the research is providing guidelines for the correct interpretation of AE envelope spectra in planetary gearboxes, which is crucial for avoiding gear failures.

Formation of local structural order in the aluminum melt before crystallization

The results of an experimental study of acoustic emission spectra (AE) in the frequency range of 20–200 kHz, which occur when the temperature of the aluminum melt decreases from 860 to 660°C. The paper discusses the relationship between acoustic signals and the processes of structural transformations in the melt as the temperature.

Acoustic emission spectrum characteristics of structural coal destruction in negative pressure environment

Abstract The uniaxial compression experiments under a low-pressure environment were performed by using structural coal samples. The frequency domain response characteristics of coal mass failure under loading in a low-pressure environment were acquired by FFT transformation and wavelet packet decomposition. The results show: As the loading stress of coal increases, the AE spectrum becomes more abundant, and the whole AE spectrum shows a left-shift trend. When the gas pressure increases, the acoustic emission signals change from low-frequency high-energy to high-frequency low-energy, the frequency band gradually narrates, and the spectrum changes from complex multi-peak shape to single-peak shape. As stress increases, the proportion of energy in the band 0-4.38 kHz gradually increases, while that in other bands gradually decreases. The energy response to stress changes in the two frequency bands of 2.92-4.38 kHz and 4.38-5.84 kHz is the most obvious. When the pressure changes, the energy in three frequency bands of 2.92-4.38 kHz, 4.38-5.84 kHz, and 7.3-8.76 kHz present an evident response trend with the pressure change, and the response trend (increase) of the latter two is exactly opposite (decrease) to that of the former. This phenomenon indicates that 2.92-4.38 kHz and 4.38-5.84 kHz are the characteristic frequency bands of the coal fracture process. The findings of this research offer crucial foundational data to support the monitoring and early warning of coal and gas outburst hazards.

Effect of cyclic loading level on mechanical response and microcracking behavior of saturated sandstones: Correlation with water weakening phenomenon

Rocks frequently endure water environments and diverse cyclic loading from natural and anthropogenic sources across various damage stages. Understanding the mechanical and fatigue behavior of saturated rocks under different cyclic loading levels is crucial for the intelligent design and long-term stability of the slope. Here, we report several monotonic and cyclic compression tests conducted on red and cyan sandstones under dry and water-saturated conditions, coupled with acoustic emission (AE) monitoring. We analyzed key mechanical parameters such as peak stress and peak strain, as well as AE parameters including count and energy, and dominant frequency in AE spectra. Our findings reveal that elevated cyclic loading levels significantly reduce peak stress and increase peak strain in both dry and water-saturated rocks, with more pronounced effects observed under water-saturated condition. The cumulative AE count correlates positively with increasing cyclic loading levels but decreases due to water saturation. Under cyclic loading with varying levels, dry rocks often experience shear cracking and exhibit AE waveform with high dominant frequency, while saturated rocks tend toward tensile cracking and AE waveform with low dominant frequency. Higher cyclic loading intensifies the occurrence of tensile cracking and AE waveform with low dominant frequency, which are constrained during the elastic deformation stage but become more pronounced beyond the crack initiation threshold. Moreover, cyclic loading enhances the water weakening effects in saturated rocks, closely associated with ongoing crack propagation within the rocks.

Identifying steady and pulsatile two-phase flow regimes with pressure drop signals and acoustic emissions

Identifying two-phase flow regimes is vital for understanding phase-change heat and mass transfer processes. Traditionally, flow regime identification has relied on subjective flow visualization studies, which are then converted to flow regime maps applicable to specific operating conditions. However, these conventional, largely subjective, maps fall short in predicting abrupt changes in flow patterns that often occur during regular operation of vapor compression and absorption heat pumps and other thermal systems. Conventional flow regime maps are also inadequate for addressing the transient, intermittent, or periodic flow regimes that occur in boiling and condensation enhanced using acoustic and other emerging techniques. Therefore, there is a pressing need for alternative flow regime identification techniques that can adapt and reliably track rapid and local changes in two-phase hydrodynamics. Promising candidates for dynamic flow pattern classification include high-resolution pressure drop signals and acoustic emission spectra, which can provide insights into the local hydrodynamics within a flow channel. To assess the feasibility of these measurement techniques, differential pressure and condenser microphone measurements are recorded alongside high-speed videos of a two-phase flow of saturated R134a. The total mass flux and vapor quality are varied to understand the effects of phase velocity and liquid inventory on wave propagation in the channel. Additionally, forced oscillations are introduced to a steady two-phase flow to analyze their impact on flow patterns and their corresponding pressure drop and acoustic signals. Statistical analyses, including Gaussian mixture modeling, are employed to reveal characteristic pressure drop probability associated with each flow pattern, which form the basis for developing a model capable of predicting flow regimes in both steady and oscillating flows. The resulting framework introduces a new flow regime identification technique that can adapt to dynamic operating conditions, benefiting a wide range of thermal systems and phase-change processes.

Crack pattern identification in cementitious materials based on acoustic emission and machine learning

The cracking patterns in cementitious materials before failure are closely related to acoustic emission (AE) monitoring signals. Traditional rise angle-average frequency analysis methods rely on empirical judgment for boundary line determination, lacking effective automated recognition methods to distinguish between tensile, shear, and mixed crack types. This paper presents an unsupervised crack-type identification model based on ensembled clustering with support vector machine (SVM) and a supervised crack-type classification model using graph neural networks (GNN). The study achieves automatic annotation of four crack signal patterns by ensembling the results of k-means++ and GMMSEQ clustering and combining them with SVM. Subsequently, with labeled data obtained, the study encodes AE signals into graphs and uses a GNN for automatic crack-type classification. The effectiveness of these methods is validated on AE test data from cementitious materials. The findings show that the mutation values of AE event interval functions can accurately pinpoint the material fracture moment, and the AE spectrum’s mid-frequency band is associated with cement–river sand interface damage. Different compositions and loading methods in cementitious materials complicate the association between signals and damage. The ensembled clustering and SVM methods can automatically distinguish different types of cracks and provide quantifiable boundary lines. With horizontal visibility graph (HVG) encoding, the supervised GraphSAGE model accurately identifies various crack types, achieving an average accuracy of up to 97.5% on the test dataset. The proposed AE time–frequency domain characterization method offers new analytical tools for understanding the damage evolution process in cementitious materials, promoting the application of machine learning in the recognition of AE signal patterns.

Machine learning model of acoustic signatures: Towards digitalised thermal spray manufacturing

Thermal spraying, an important industrial surface manufacturing process in sectors such as aerospace, energy and biomedical, remains a skill intensive process often involving multiple trial runs impacting the yield. The core research challenge in digitalisation of thermal spraying process lies in instrumenting the manufacturing platform as the process includes harsh conditions, including UV Rays, high-plasma temperature, dusty chemical environment, and spray booth inaccessibility. This paper introduces a novel application of machine learning to the acoustic emission spectra of thermal spraying. By transitioning from the amplitude-time domain to a Fourier-transformed frequency-time domain, it is possible to predict anomalies in real-time, a crucial step towards sustainable material and manufacturing digitalization. Our experimental results also indicate that this method is suitable for industrial applications by generating useful data that can be used to develop Visual Geometry Group (VGG) transfer learning models to overcome the traditional limitations of convoluted neural networks (CNN).

Establishing the nature of kinetic effects of the high-temperature oxidation (combustion) process of some liquid organic matters by acoustic radiation

In this article, the results of the study on the physicochemical characteristics of some liquid organic matters on the kinetics of their high-temperature oxidation (combustion) were presented for the first time. These results were obtained by the method of acoustic emission spectrum from heat source. The research results of the amplitude-time characteristics (until cessation of combustion completely) and the frequency response functions (in a given frequency range) of oxidation (combustion) process of liquid organic matters showed that there are following unambiguous dependences: 1) the dependences of the number of the amplitude maximum of the frequency and time spectrum in a given frequency range, as well as of the fractal dimension of the received acoustic signal on the number of carbon atoms in the carbon frame of organic matters and their molar mass; and 2) the dependences of the time of beginning of the combustion (ignition) of primary cloud of organic matters vapors and the final combustion time of the primary cloud of organic matters vapors on the number of carbon atoms in the carbon frame of the organic matters and their partial vapor pressures. The practical aspect of using the results obtained is dictated by the need to develop standard samples of amplitude-time and amplitude-frequency characteristics, depending on the physicochemical and combustible properties of the organic matters. This is necessary for the data bank of the acoustic emission monitoring system to establish a fire hazardous state and make anti-crisis decisions at critical infrastructure facilities.

A comprehensive study of damage characteristics and acoustic emission response mechanism of sandstone with different water contents

The mechanical characteristics and fracture mechanisms of rocks are usually affected by water–rock interaction. This study conducted uniaxial compression tests on sandstone to analyse the variation law of its mechanical properties and macroscopic failure morphology for various water contents. Furthermore, the relationships between the roughness of the crystal fracture surface and mechanical properties were discussed based on the scanning electron microscope (SEM) fracture morphology. The development of an internal fracture in sandstone was described using acoustic emission (AE) and low-field nuclear magnetic resonance (NMR) techniques, and the response mechanism and controlled factors of an AE dominant frequency–amplitude signal were revealed. Results reveal that the uniaxial compressive strength and elastic modulus of sandstone exhibit a nonlinear decreasing trend with an increase in water content. The deterioration to the sandstone structure by water is divided into three periods: intense, stable growth, and calm. The macroscopic fracture shape of sandstone gradually changes from single-shear failure to axial-split failure, whereas the roughness of the crystal fracture surface positively correlated with the water content, with the mechanical strength negatively correlating. Four response mechanisms of the AE spectrum exist in sandstone fracture process: sliding friction between the mineral particles or crystal fracture (transgranular or intergranular crack) corresponds to the LF–LA signal, friction of microcracks corresponds to the IF–LA signal, fracture of microcracks corresponds to the HF–LA signal, and large-scale macroscopic cracks correspond to the LF–HA signal. Moreover, the characteristics of the AE dominant frequency–amplitude signal are affected by the mineral composition and pore structure of sandstone. This study can enrich the existing research literature on sandstone damage characteristics in water-bearing state and provide new perspectives and approaches for quantitatively describing the fracture mechanism of rock.

Анализ спектров акустической эмиссии при охлаждении расплава алюминия

The results of an experimental study of the acoustic emission spectra in the frequency range 20-200 kHz, which occur when the temperature of the aluminum melt decreases from 860 to 660 °C are presented. The interrelation of acoustic signals with the processes of structural transformations in the melt with a change in temperature is considered.

ACOUSTIC EMISSION SPECTRUM CHARACTERISTICS AND LOCALIZATION EVOLUTION MECHANISM OF COAL AND COAL GANGUE SPECIMEN FAILURE

The mechanical experiments and acoustic emission (AE) experiments of unconfined compression strength (UCS) of coal and coal gangue specimens are carried out to investigate the mechanical characteristics and AE waveform spectrum and localization evolution characteristics of coal and coal gangue specimens. The phase distribution characteristics of the RA-AF values of AE signals are used to characterize the different rupture modes ofcoal and coal gangue specimens. The fast Fourier transform (FFT) method is used to obtain two-dimensional spectral maps of AE signals, extract their main frequencies, and analyze the spectral characteristics of the phase failure of coal specimens. Further, AE signal localization events are selected to study the localization evolution information of coal and coal gangue specimens in the process of phase failure. The experimental results show that the coal gangue specimens have high strength and brittle damage; the largest transverse volume expansion is in the coal specimens, and the coal gangue specimens show the characteristics of high AF and low RA in the 0-0.6<sub>σ f</sub> stage, while the coal specimens show the characteristics of high AF and low RA in the 0-0.4<sub>σ f</sub> stage. The coal gangue specimen, in the initial period of loading, has a small number of AE events, but the AE aggregation phenomenon occurs. The AE events of coal gangue specimens are obviously distributed along the fracture surface and present aggregation in space, while the AE events of coal specimens show a scattered state.

Nonlinear mechanical characteristics and damage constitutive model of coal under CO2 adsorption during geological sequestration

Geological sequestration of CO2 is an effective way to achieve the goal of reducing global warming and carbon neutralization. The complex CO2–coal interaction will lead to the change of mechanical properties of coal. Therefore, evaluating the mechanical characteristics of deep coal seam after CO2 adsorption is of great significance for analyzing the geological characteristics and structural stability of deep strata and storage site selection. In this study, the triaxial compression test and acoustic emission (AE) test were used to evaluate the effect of CO2 adsorption pressure on the mechanical properties of coal mass. On the basis of the evolution characteristics of AE ring down count (RDC), fractal theory was used to analyze the deformation and fracturing evolution of coal. Besides, an elastic damage constitutive model describing the nonlinear stress-strain relationship of coal under CO2 adsorption is established. Results show that the growth of adsorption pressure develops the risk of damage and fracturing of coal. With the increase in adsorption pressure, the value of singularity index width Δα presents a downward trend, indicating the development of internal cracks and the increase in the complexity of microunit fracturing process. When the adsorption pressure is low, the gas in large cracks and pores leads to the generation of small AE signal that occupies the dominant position. High adsorption pressure increases the complexity and nonlinearity of coal deformation, strengthens the proportion of large AE signal, and leads to the increase in the average value of multifractal spectrum width Δf. With the increase in adsorption pressure, the fracturing degree of coal is higher, a mass of large-size cracks appears, and more AE spectrum presents intensive distribution. In the plastic stage, the load level is close to the strength limit, the microcracks intersect with each other, and the average value of Δα in this stage is the smallest.

Acoustic emission spectra and statistics of dislocation movements in Fe40Mn40Co10Cr10 high entropy alloys

The defining feature of high-entropy alloys (HEAs) is their unprecedented degree of compositional inhomogeneity which influences their dislocation movements. We demonstrate differences between a HEA (Fe40Mn40Co10Cr10) and a conventional solution alloy (316L stainless steel) using acoustic emission (AE) spectroscopy. AE measurements under tension show the coexistence of two avalanche processes in Fe40Mn40Co10Cr10 HEA, whereby one avalanche process relates to the movement of dislocations and the other to detwinning/twinning processes. These two avalanche processes exhibit two branches of the E ∼ A2 correlation. The dislocation movements in Fe40Mn40Co10Cr10 HEA show systematically longer durations compared with the equivalent dislocation movements in the 316L stainless steel and a bias toward faster waiting times for subsequent dislocation movements. The aftershock rate, as identified by the Omori law, is the same for the two materials.

Data Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks

The work presented in this paper is built on a series of experiments aiming to develop a data-driven and automated method for seal diagnostics using Acoustic Emission (AE) features. Seals in machineries operate in harsh conditions, and seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In this study, we implemented a data-driven diagnostics approach which utilizes AE measurements along with light weight Artificial Neural Networks (ANN) as a classifier to investigate the performance and resources (hardware & software) required for implementing a real-time soft sensor unit for monitoring seal wear condition. We used a feedforward multilayer perceptron ANN (Scaled Conjugate Gradient- SCG algorithm) that is trained with the back propagation algorithm, which is a popular network architecture for a multitude of applications (automotive, oil and gas, electronics). We benchmark the developed method against previous work conducted based on Support Vector Machine (SVM), and we compare ANN performance in classifying the running condition of seals in hydraulic cylinders by applying it to both raw (full frequency spectrum) and down sampled frequency measurements. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions like that of hydraulic cylinders. The test cases were generated with seals of three different conditions (unworn, semi-worn, worn). From the AE spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X down sampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A general set of 16 time and frequency domain features were designed. Then a training set was developed using relevant set of features (4, 5, and 16 features). The data was used to train the ANN (70% training – 30% test & validation) and SVM (60 % training - 40% test and validation). Classification of down sampled measurements, both ANN and SVM were able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99% with execution time less than seconds. Therefore, the proposed approach can be applied as part of an automated seal wear classification technique based on AE and ANN/SVM and can be used for real-time monitoring of seal wear in hydraulic cylinders.

Experimental and Theoretical Study of Plastic Deformation of Epoxy Coatings on Metal Substrates Using the Acoustic Emission Method.

Propagation of acoustic emission signals in continuous conjugated media under real-time loading was explored. The results of explored plastic deformation polymer coatings on a metal base using the acoustic emission method with synchronization of deformations and the moments of occurrence of acoustic emission signals are presented. Using the principal component method, the acoustic emission spectra, which make it possible to trace the evolution of deformation transformation processes, were analyzed. Presented the results of theoretical and experimental studies on the separate propagation of acoustic emission vibrations in a polymer coating, a metal base, and their joint combination in the form of multilayer structures. Boundary problems of propagation of acoustic emission signals in the conjugation of continuous media are considered from the standpoint of an elastic continuum and wave representations. The main variables are the force that initiates the appearance of acoustic emission signals and the displacement that determines the propagation of elastic waves. Based on the local rearrangement of the internal structure of conjugated media under conditions of development of deformation processes in the material, the verification of the main theoretical models of energy spectrum acoustic signals in continuous media at the micro-, meso-, and macro-levels was carried out. In this work, we present experimental data on a set of basic acoustic emission characteristics for four-point bending. It is shown that the principal components method reduces the dimension of data while maintaining the least amount of new information. Using the method of principal components to determine the stages of plastic deformation of polymer coatings on a metal base using the acoustic emission method. With the digitalization of acoustic emission signals and noise filtering, new possibilities for isolating a weak signal at the noise level appear even when its amplitude is significantly lower than the noise level. The study results can be used to predict the degree of destruction of two-layer materials under loading.

Tracking changes in grinding wheel condition using reduced features of acoustic emission signals

In this paper, a universal method is proposed to accurately identify wheel state during grinding brittle materials. In contrast with conventional methods relying on adequate repetitive experimental data under the same process conditions, only history acoustic emission (AE) data in the same life cycle of a grinding wheel are needed for current wheel state identification. During grinding process, AE spectra samples are acquired sequentially on a series of nodes with equal interval. Samples on each node are categorized into the same class of wheel state. In the theoretical part, AE spectrum is proved to be suitable for feature representation of the degradation of wheel condition. Linear discriminant analysis is used to project AE spectra samples into a two-dimensional feature space, and the changes of the projection gives a clue to the evolution of wheel state. Two types of commonly used diamond wheels for optical surface grinding, which are straight wheel and cup wheel, were used to verify the general applicability of the method. The experiments were carried out on different machine tools and the two wheels also possessed different degradation process, yet for all of them, the evolution of wheel state can be accurately identified. It was proved that the proposed method can adaptively trace wear stage change of diamond grinding wheel.

2E2-3 Effect of dynamic behavior of single-bubble on acoustic emission spectra

2E2-3 Effect of dynamic behavior of single-bubble on acoustic emission spectra

On acoustic emission analysis in circular saw cutting beech wood with respect to power consumption and surface roughness

A sound or a noise that accompanies wood machining processes is introduced by the tool rotation itself, by the friction of moving machine parts, or by wood-tool interaction. The sounds generated during machining with a circular saw could be analysed in order to monitor and possibly control the cutting process. Applying altered cutting parameters while cutting beech wood (Fagus sylvatica L.), which is the most common wood species in the Republic of Serbia, caused acoustic emissions that could be analysed throughout corresponding spectra. As shown in previous studies, altering the cutting parameters, e.g., the feed speed and tool override, resulted in variations in power consumption, surface roughness, and acoustic emission (or acoustic pressure). The aim of this paper was to provide a possible correlation between the applied cutting parameters and the acoustic emission spectra with respect to consumed power and the state of the machined surface. Along with acoustic emissions, the power consumption and surface roughness data were also acquired in order to make a possible relationship. By associating the idle circular saw acoustic spectra with background noise and comparing them with those obtained during machining, it was possible to indicate spectrum areas of particular interest for further analysis.

Automated and Rapid Seal Wear Classification Based on Acoustic Emission and Support Vector Machine

Seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In literature, successful attempts have been made using acoustic emission-based condition monitoring to classify the seal wear. However, limited attempts have been made to implement automated approaches to classify seal wear using acoustic emission features. Therefore, this article presents an automated approach for rapid and computationally economical diagnosis of seal wear using acoustic emission. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions similar to that of hydraulic cylinders. From the acoustic emission spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X downsampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A set of time and frequency domain features were designed, and the Support Vector Machine (SVM) was trained on 60 % of the test cases generated with seals of three different conditions. The SVM was able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99 %. Therefore, the proposed automated seal wear classification technique based on acoustic emission and SVM can be used for real-time monitoring of seal wear in hydraulic cylinders.

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.