Abstract Acoustic Emission Research Articles

An integrated approach to evaluate the measurement capability and acceptability of acoustic emission sensors

Acoustic emission (AE) is a pivotal technique in condition-based maintenance (CBM) and recent years have witnessed a significant surge in the deployment of AE sensors in industrial applications. With this increase in availability, there comes a substantial challenge: evaluating the measurement capability of sensors within specific applications. As such, this study identifies a critical need for a structured approach to evaluate the measurement capabilities of AE sensors and subsequently judge their acceptability against guideline criteria. To address this need, we present an integrated approach that systematically guides the capability evaluation of AE sensors, incorporating both qualitative and quantitative analyses. The qualitative analysis aims to scrutinize the diagnostic accuracy of sensors by assessing the detectability of features critical for diagnostics. The quantitative analysis leverages the Gage repeatability and reproducibility (Gage R&R) to statistically evaluate sensor characteristics. A comprehensive experimental study further investigates the impact of measurement sources on the sensors’ repeatability, and reproducibility. This study illustrates the qualitative findings regarding sensor’s diagnostic accuracy in both time and frequency domains, revealing promising performance in diagnostic-based evaluations. In quantitative analysis, we demonstrate the results of sensor capability in terms of repeatability and reproducibility, providing the variations of different sources in statistics-based evaluations. We thoroughly investigate the influence of significant factors, quantifying their contributions to the sensor’s measurement capability. Furthermore, we introduce metrics designed to assess sensor’s acceptability, according to explicit acceptance and rejection criteria. Our experimental results affirm that root mean square measurements are within acceptable ranges for both sensors, while spectral entropy (SE) measurements for PK15I sensor satisfy the acceptable level. For HZ136I sensor, however, SE measurements are deemed conditionally acceptable. Ultimately, the proposed approach provides a robust framework for the comprehensive evaluation of AE sensor measurement capabilities, offering invaluable guidance for sensor selection and enhancement in industrial applications.

Analysis of Acoustic Emissions for Determination of the Mechanical Effects of Scratch Tests

Acoustic Emission (AE) is a promising technique for measuring tool wear online and in real time. In this work, scratch tests were conducted to better understand the “pre-wear” AE response based on loading conditions that were not sufficient to generate galling. The scratch tests used the same type of indenter against two different sheet materials: aluminum and steel. The results showed that AE parameters such as the mean frequency, Centroid frequency and Shannon entropy outperformed other frequency domain techniques by discriminating between the two sheet materials in scratch tests. From the literature, the frequency region of interest was expected to be sub 300 kHz. However, in this study, activity below this threshold was found to be noise, whereas distinct frequencies were found at much higher frequencies than expected. These results are compared against single grit “SG” tests of both mild steel- and nickel-based superalloys to allow comparison of the two test methods and materials used. This comparison showed that the SG tests excited the acoustic emission in ways in which the scratch tests did not. Another factor when using acoustic emissions to monitor sheet metal forming is the differences obtained in energy–frequency mapping, where many report the galling phenomena between a certain amplitude and frequency range. Such results are specific to the setup and the materials/geometries used. Further work presented here compares different scratch tests where energy–frequency mapping is different for different materials/geometries.

Study of the parameters of acoustic emission transducers taking into account the influence of the acoustic-electronic channel and calibration conditions

Acoustic emission transducers (AET) with piezoelectric cells converting acoustic signals into electric ones, are the most important components of the channel for transmitting information about the parameters of acoustic emission (AE) sources. The main AET parameters are determined by different calibration techniques, but the shape of amplitude-frequency characteristics (AFC) largely depends on the calibration method which significantly complicate the choice of AET for control. We present the results of studying the AET parameters taking into account the role of the acoustic-electronic channel and calibration conditions. It is shown that the most complete data on the AET properties are contained in the impulse response, which should be used to measure the AET parameters. In this case, the AFC is of secondary importance. It is preferable to calibrate the AET using longitudinal waves at normal incidence of the wave on the AET (basic scheme). The results obtained can be used when selecting methods of calibration of working transducers used for diagnostics of industrial objects.

Optimal Passive Source Localization for Acoustic Emissions.

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Research on the Failure Evolution Process of Rock Mass Base on the Acoustic Emission Parameters

Fracture mechanics behavior and acoustic emission (AE) characteristics of fractured rock mass are related to underground engineering safety construction, disaster prediction, and early warning. In this study, the failure evolution characteristics of intact and fracture (e.g., single fracture, parallel fractures, cross fractures, and mixed fractures) coal were studied and contrasted with each other on the basis of the distribution of max amplitude of AE. The study revealed some meaningful results, where the value of b (i.e., the distribution characteristic of max amplitude of AE) could represent the failure evolution process of intact and fractured coal. The maximum amplitude distribution of AE events was characterized by Gaussian normal distribution, and the probability of the maximum amplitude of AE events corresponding to 35∼50 dB was the largest. In the stress range of 60∼80%, AE events and maximum amplitude increased rapidly, and the corresponding b value decreased. The energy of AE events showed a downward trend after reaching the maximum value at about 80% stress level. Under the same stress level, the more complex the fracture was, the larger the b value of coal–rock mass was, and the stronger the inhibition effect on the fracture expansion caused by the internal fracture distribution was. Due to the anisotropy of coal–rock mass with a single crack, the distribution of the b value was more discrete, while the anisotropy of coal–rock mass with mixed crack decreased, and the dispersion of the b value decreased. The deformation of cracked coal mainly caused by the adjustment of cracks during the initial loading b value experienced a trend of decreasing first, then increasing, and then decreasing in the loading process. When the load reached 0.8 times of the peak strength, the b value had a secondary decreasing trend, indicating the macroscopic failure of the sample, which could be used as a precursor criterion for the complete failure of coal–rock mass.

Effect of Matrix Resin Characteristics on the Fracture Behavior for Plain-Woven Carbon Fiber Reinforced Plastics fabricated by Vacuum Assisted Resin Transfer Molding

Acoustic emission (AE) measurement has been used to investigate microscopic failures and damage progress in carbon fiber reinforced plastics (CFRP). In this work, AE measurements were conducted on plain-woven CFRPs fabricated by vacuum-assisted resin transfer molding process and neat epoxy resins during tensile tests to evaluate the failure modes. Four types of epoxy resins were used as matrix resins to investigate the effects of matrix resin characteristics on the microscopic damage behavior and the mechanical properties of CFRP. As a result, the mechanical properties of on-axis CFRP were comparable regardless of matrix resins. On the other hand, in the case of off-axis CFRP, such as 45° direction, the tensile strength was remarkably lower with a brittle matrix resin. From the AE measurement results, the number of cumulative hits for CFRP was much larger than that for neat resins due to the occurrence of fiber/matrix interfacial cracks. Also, AE waves for CFRP had a longer duration than that for neat resin, and the relationship between the degree of duration and fracture mode was clarified.

The duration-energy-size enigma for acoustic emission

Acoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

Analysis of Acoustic Emission Data for Bearings subject to Unbalance

Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance.

Violation of the Kaiser Effect under Loading of Eutectic Alloys of the Pb–Sn System

Acoustic emission upon mechanical loading of alloys of the Pb–Sn system is studied. It is revealed that the Kaiser effect is not observed under repeated loading of the material at component concentrations leading to the formation of a eutectic structure. An explanation for the absence of acoustic-emission memory in eutectic lead-based alloys is proposed. The explanation is based on the specific character of the plastic flow implemented due to the motion of eutectic α- and β-phase colonies as a whole along common interfaces.

Surface Roughness Prediction Based on Acoustic Emission Signals in High-Precision Diamond Turning of Rapidly Solidified Optical Aluminum Grade (RSA443)

Acoustic emission signal-based prediction of surface roughness has been utilized widely, yet little work has been done in this regard on RSA443. This paper seeks to study the correlation between acoustic emission (AE) signal parameters and surface roughness. Estimation of surface roughness using AE signal parameters and subsequent examination of the influence of AE signal parameters (root mean square, peak rate and prominent frequency) on the accuracy of the RSM model in surface roughness prediction are carried out. The experiment is designed using the Taguchi L9 orthogonal array to minimize the number of experiments. Emitted acoustic signals are captured using a Piezotron sensor. Three RSM models are formulated and compared in this study: a model that uses only critical machining parameters (cutting speed, depth of cut and feed rate), a model that uses only AE signal parameters (root mean square, peak rate and prominent frequency) and a model that uses both critical machining parameters and AE signal parameters. An assessment based on the models’ mean absolute percentage error (MAPE) is made to see if AE signal parameters have any contribution towards surface roughness prediction accuracy. The order of parameter significance in the most accurate model is investigated in this paper. The mean absolute percentage error results for the models indicate that the model in which AE signal parameters are utilized in conjunction with critical machining parameters has the highest prediction accuracy of 97.32%. The model that utilizes only critical machining parameters has a prediction accuracy of 96.35% while the one that utilizes only AE signal parameters has a prediction accuracy of 84.43%. It is observed that the order of parameter significance from the most to the least significant is as follows: feed rate, cutting speed, peak rate, AErms, depth of cut and prominent frequency.

Characterization and analysis of the tensile and acoustic emission parameter distributions of single wool fibers

Acoustic emission (AE) parameters of damaged material have discrete characteristics that are universally known. Such characteristics restrict wider use of AE analysis in materials such as textiles. In the present work, the best-fitted distribution models of AE signal energy are analyzed: amplitude, duration, tensile breaking strength, elongation, and specific work of wool fibers. The parameters of Weibull, normal, and log-normal distribution models are obtained by regression analysis. The chi-square, Kolmogorov–Smirnov, and maximum likelihood criterions are used to discriminate against the above models. The results show that the Weibull distribution is the best-fitted model for amplitude and elongation of wool. The best-fitted model for energy, strength, and specific work is the log-normal distribution. The differences between the cumulative distributions of the AE and tensile parameters are compared. It can be seen that strength also has a high correlation coefficient and a similarly cumulative distribution with energy and amplitude. Compared to amplitude, the relationship between energy and strength is supposed to be stronger.

Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline

Acoustic emission bursts are signal waveforms that include a number of consecutive imbrication transients with variable strengths and contain crucial information on the leakage phenomenon in a pipeline system. Detection and isolation of a burst against the background signal increases the ability of a pipe’s fault diagnosis system. This paper proposes a methodology using the Enhanced Constant Fault Alarm Rate (ECFAR) to detect bursts and exploit the burst phenomenon in acoustic emission. The extracted information from the burst waveform is used to distinguish several levels of leakage in a laboratory leak-off experimental testbed. The multi-class support vector machine in the one-against-all method is established as the classifier. The results are compared with those of the wavelet threshold-based method, another algorithm utilized for impulse and burst detection, which indicates that the ECFAR method gives an ameliorative classification result with an accuracy of 93% for different levels of leakage.

Structural health monitoring of liquid filled above ground storage tank floors: a time-reversed approach to acoustic emission source location

Acoustic Emission (AE) is a proven successful Non-Destructive Testing (NDT) method to assess the state of storage tank floors. Traditional AE source location in tanks floors is performed using only the wavefronts that have traveled directly from the source to the sensor (direct hit). The wavefronts captured after reflected from the tank walls are identified and discarded. This paper proposes a new AE source location algorithm in tanks that considers a combination of reflections and direct hits. The proposed algorithm is based on time-reversed acoustics and ray theory. The methodology uses the concept of time-reversed acoustics in which a wave detected at any location can be directed back to the source when re-created at the detection place. Therefore, the developed approach takes the time at which each wave arrives to the sensor and sends it back as if time had reversed. Ray theory is used in the methodology to account for the way in which the wavefront is reflected when encounters an obstacle such as the walls of the tank. Then, the point of intersection of all wavefronts is identified using an optimization algorithm. This point where all wavefronts intersect is considered the location of the source. The location algorithm considers the first path or direct hits from the source to the sensors combined with reflections obtained by wavefronts bouncing from the tank walls. The proposed location algorithm was validated using numerical data from 176ft diameter tank and experimentally using AE data from a tank 55ft diameter.

A novel acoustic emission parameter for predicting rock failure during Brazilian test based on cepstrum analysis

Acoustic emission (AE) is widely used in the monitoring of coal and rock stability and early warning of dynamic disasters in mines. In this work, the Brazilian split test was carried out on limestone samples along with collecting full waveform AE signal during the entire loading process. The linear cepstrum coefficient (LCC) was used as the characteristic parameter of AE to analyze the correlation between the LCC of AE and the load of the samples. The results show that the LCC is an effective AE characteristic parameter. The first to fourth parameters of the LCC calculated using 1 s AE waveform meet the linear change relationship with the load of the samples. The correlation coefficients are 0.969, 0.943, 0.925, 0.833, respectively. The LCC of AE proposed in this work can be used as a characteristic parameter for predicting the tensile failure of rocks, which is helpful to improve the application effect of AE in the monitoring of coal and rock dynamic disasters and the stability of high and steep slopes.

Improving the Positioning Accuracy of Acoustic Emission Events by Optimizing the Sensor Deployment and First Arrival Signal Picking

Acoustic emission (AE) source location is an effective method used to reveal the deformation and damage characteristics of materials. Improving the positioning accuracy of AE events is extremely important for both experimental research and engineering practice. An experiment was performed on a standard rock specimen to investigate the key factors that affect the positioning accuracy of an AE event, such as the computational combinations and spatial layout of the sensors and the picking accuracy of the first arrival signal. The results show that as the number of sensors involved in the positioning calculation increases, both the maximum and average positioning errors clearly decrease, and the positioning accuracy increases. Meanwhile, a better result is achieved when the number of sensors involved in the positioning calculation reaches three-fourths of the total number of monitoring sensors used. Under this condition, both the minimum positioning error and the error dispersion are sufficiently low, and the reliability of the positioning results is greatly improved. Furthermore, when different types of and deviations in the first arrival signal are used for the location calculation of the same AE event, the positioning results are quite different, and large errors are found. Finally, the accurate positioning of an AE event is realized by adjusting the number of sensors and improving the picking accuracy of the first arrival time of the signal. The maximum reduction in the positioning error achieved is 96.62%, and the minimum reduction is 57.61%. Hence, the positioning error decreases significantly. The investigation results can provide an important reference for the precise positioning of AE events involved in experimental research and engineering.

Identification of fatigue damage evaluation using entropy of acoustic emission waveform

Acoustic emission (AE) is a passive nondestructive testing (NDT) technique which is employed to identify critical damage in structures before failure can occur. Currently, AE monitoring is carried out by calculating the features of the signal received by the AE sensor. User-defined acquisition settings (i.e., timing and threshold) significantly affect many traditional AE features such as count, energy, centroid frequency, rise time and duration. In AE monitoring, AE features are strongly related to the damage sources. Therefore, AE features that are calculated due to inaccurate user-defined acquisition settings can result in inaccurately classified damage sources. This work presents a new feature of the signal based on the measure of randomness calculated using second-order Renyi’s entropy. The new feature is computed from its discrete amplitude distribution making it independent of acquisition settings. This can reduce the need for human judgement in measuring the feature of the signal. To investigate the effectiveness of the presented feature, fatigue testing is conducted on an un-notched steel sample with simultaneous AE monitoring. Digital image correlation (DIC) is measured alongside AE monitoring to correlate both monitoring methods with material damage. The results suggest that the new feature is sensitive in identifying critical damages in the material.

Acquisition of Acoustic Emission Signals from Rocks Using Directly Bonded PZT Patches

Acoustic emission (AE) technique is a proven effective technique for monitoring underground mines and caverns. With the advancement in the data acquisition technology, AE signals up to 2–5 MHz of frequency can be recorded successfully. However, this generates a lot of data to be analyzed and stored. This paper presents a concept of a simple and cost-effective, low-frequency AE technique for monitoring damages in the rocks. The tests were performed on Kota sandstone rock specimens in compression at three different displacement rates of 0.01, 0.05 and 0.1 mm/s. The AE signals were recorded successfully through thin square piezo-patches (used as the AE transducers) and a commercially available sound card for data logging. The proposed technique has been found to be easy and very much cost-effective in capturing low-frequency AE signals. The recorded data were processed for determining different AE parameters like cumulative AE count, relative energy of an event and event rate.

A Review on the Relationships Between Acoustic Emission, Friction and Wear in Mechanical Systems

Abstract Acoustic emission (AE) techniques play a key role in machine condition monitoring and wear/fault diagnosis. Understanding the impact of friction and wear on the generation of AE signals is essential to building a reliable wear monitoring system. However, existing papers focus on only one or two factors in specific contact conditions. This paper aims at surveying studies related to both theoretical models and experimental investigations to produce a comprehensive picture of the relationship between tribological parameters (e.g., surface roughness, oil film thickness, and friction coefficient), operating parameters (e.g., sliding velocity and load), and AE signal characteristics (e.g., amplitude/energy, frequency, and event count). This result will provide guidance for the development of AE-based condition monitoring approaches and in particular for the establishment of AE-based wear assessment techniques.

Acoustic Emission and Associated Damage Mechanism Analysis in 8YSZ Thermal Barrier Coatings Under Instrumented Indentation

Acoustic emission (AE) monitoring was used during indentation tests on the cross section of plasma-sprayed 8 wt.% yttria-stabilized zirconia (8YSZ) thermal barrier coatings to investigate the relationship between AE signals and the associated deformation or single cracking events. The damage evolution in 8YSZ was studied by examining signal characteristics with the aid of AE parameter analysis and wavelet packet decomposition. The results show that the coatings were firstly elasto-plastically deformed, and microcracks gradually developed around the indentation. Then, delamination occurred and the fracture was made up of some longer cracks and dozens of microcracks. AE signals originating from coating deformation, formation of microcracks and longer cracks show different amplitudes and frequencies. The results indicate that the features of AE parameters differ depending on the mechanical failure mechanism, and AE responses are closely related to the fracture behavior and are dependent on splat microstructure of the coatings.

Acoustic emission generated by glass beads in compression and shearing

Acoustic emission (AE) monitoring offers the potential to sense particle-scale interactions that lead to macro-scale responses of granular materials. This paper presents results from a programme of drained triaxial tests performed on densely packed glass beads to establish quantitative interpretation of AE during isotropic compression, shearing and associated stick–slip events. Relationships have been quantified between: AE and boundary work (i.e. AE generated per Joule) for a unit volume of glass beads under isotropic compression and shear; AE and shear displacement rate; and the amplitude of deviator stress cycles and AE activity during stick–slip events. In shear, AE generation increased with shear strain and reached peak values that were maintained from volume minimum (i.e. the transition from contractive to dilative behaviour) to peak dilatancy, whereupon AE generation gradually reduced and then remained around a constant mean value with further increments of shear strain. In each stick–slip event, AE activity increased during shear strength mobilisation, particle climbing and dilation, and then reduced with the subsequent deviator stress drop during particle sliding and contraction. The amplitude of these cycles in AE activity were governed by the amplitude of deviator stress cycles during stick–slip events, which were also proportional to the imposed stress level and inversely proportional to particle size.