Abstract Acoustic Emission Signals Research Articles

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.

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.

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.

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.

Gas Turbine Condition Monitoring Using Acoustic Emission Signals

Acoustic emission (AE) signals are recognized as complementary measures for detecting incipient faults and condition monitoring in rotary machinery due to their containment of sources of potential fault energy. However, determining the potential sources of faults cannot be easily realized due to the non-stationarity of AE signals. Available techniques that are capable of evoking instantaneous characteristics of a particular AE signal cannot optimally perform in a sense that there is no guarantee that these characteristics (hereinafter referred to as the “features”) remain constant when another AE signal is obtained from the system, albeit operating under the same machine condition at a different time instant. This paper provides a theoretical framework for developing a highly reliable classification and detection methodology for gas turbine condition monitoring based on AE signals. Mathematical results obtained in this paper are evaluated and validated by using actual gas turbines that are operating in power generating plants, to demonstrate the practicality and simplicity of our methodologies. Emphasis is given to acoustic emissions of similar brand and sized gas turbine turbomachinery under different health conditions and/or aging characteristics.

STEEL SAMPLES WELDING DEFECTS DETECTION BY ESTIMATING ENERGY OF CLUSTERS OF ACOUSTIC EMISSION SIGNALS

Acoustic emission (AE) detection of 5005005 mm size St3 steel samples welding defects. The informative parameters of AE signals recorded during welding with inserts of titanium and aluminum inserted into the root of the weld are analyzed. AE-signals recording was performed by welding diagnostic unit with automatically changing “floating” selection threshold. For coordinates of defects estimation clustering was performed by digital form and AE signals parameters. A comparative analysis of the clustering results was performed for each cluster. In this case, the total count of AE signals, their energy, and threshold levels were set, which were determined in accordance with the proposed algorithm. The degree of danger of defects in the weld was monitored by exceeding AE signal energy threshold level. If the AE signal had a second energy peak and when it exceeds the energy selection thresholds, the weld was rejected. It was experimentally shown that the titanium insert was a dangerous defect when the threshold energy level was exceeded, which was detected both with a high total count of AE signals and with a small number of recorded signals. In accordance with the proposed method, when introducing an aluminum insert into the weld, the defect was not considered dangerous.

ASSESSMENT OF CRACK INITIATION AND PROPAGATION IN BONE USING ACOUSTIC EMISSION (AE) TECHNIQUES

Acoustic emission (AE) is a nondestructive testing (NDT) technique used for detecting damages, cracks, and leaks in different structures such as metals, composites, wood, fiberglass, ceramics, plastics, etc. In recent years, AE has gained popularity within the field of biomedical applications. The structure of bone is similar to composite materials, therefore, it is advantageous to use NDT technique. Thus, it can be used for monitoring the fracture behavior, crack initiation/propagation, and fatigue detection in bones. The goal of this study was to determine the usefulness of AE techniques in fracture detection phase of bones and to develop an NDT methodology for the monitoring of crack initiation and propagation in bones. This study describes AE activity during fracture of bone tissue under tensile loads. The experiments were carried out in vitro techniques using intact and fracture-simulated bovine tibias. The specimens were loaded to failure in tension using a mechanical testing machine. During the mechanical tests, AE signals were measured and recorded by using AE system processor equipped with two wideband piezoelectric sensors fixed to the surfaces of both ends of the test specimens. By superposing the load–time curve and the cumulative AE event–time curve, AE activities of crack initiation and propagation were identified. In all experiments, the cumulative AE number for each period of time rose up exponentially with the incremental tensile load. Load for AE initiation demonstrated a convincing linear interaction with AE event generation.

Feature learning method of acoustic emission signal during single-grit scratching on BK7 in brittle regime

Acoustic emission (AE) signal is released from a material as it undergoes deformation and fracture processes. AE signal provides enough information for monitoring of the interaction between tool and material. AE RMS, AE count rates, and frequency characteristics are always employed in AE-based monitoring techniques. Monitoring sensitivity is not satisfied because information implies in AE signal is lost or weaken to some extent for the present monitoring parameters. When a hard-brittle material is removed in brittle regime, burst-type AE (b-AE) associated with crack and chip is predominant compared with low-amplitude continuous-type AE. It will be more objective and physically meaningful if b-AE event is taken as a unit to be monitored. This paper is dedicated to the method of monitoring b-AE event in AE signal. Ignoring background noise, an AE signal can be looked as a convolution of b-AE events and corresponding coefficients. b-AE events correspond to brittle transient behaviors and include information of fracture mechanism. Coefficients represent intensity and location of crack and chip during removal processes. The two elements of the convolution contain different aspects of signal feature. Considering randomness of AE signals, the convolution form will be realized by self-learning from training data set of AE signals. Dictionary-learning algorithm for decaying oscillation modes of b-AE and sparse representation procedure of an AE signal are detailed in this paper based on the convex optimization theory. AE signals in brittle regime are acquired from tests of single diamond grit scratching on BK7. Simulation and experimental results verify the correctness of the feature learning method. According to the method proposed in this paper, b-AE can be objectively monitored without the interference of varying machining parameters.

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

Acoustic emission (AE) technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA) for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage.

Irreproducibility of acoustic emission under cyclic irradiation of solids by an infrared laser pulse

Acoustic emission (AE) caused by a stress wave in solid material samples with various thermophysical properties, i.e., copper, aluminum, and glass upon exposure to СO2 laser pulses is studied. It is found that the general AE picture (the AE signal arrival time, its amplitude, and others) is not reproduced during repeated experiments following one after another, which indicates irreversible local changes occurred in the material structure. The effect of the critical material energy density on the AE pattern during the laser exposure is demonstrated.

A Preliminary Study Application Clustering System in Acoustic Emission Monitoring

Acoustic Emission (AE) is a non-destructive testing known as assessment on damage detection in structural engineering. It also can be used to discriminate the different types of damage occurring in a composite materials. The main problem associated with the data analysis is the discrimination between the different AE sources and analysis of the AE signal in order to identify the most critical damage mechanism. Clustering analysis is a technique in which the set of object are assigned to a group called cluster. The objective of the cluster analysis is to separate a set of data into several classes that reflect the internal structure of data. In this paper was used k -means algorithm for partitioned clustering method, numerous effort have been made to improve the performance of application k -means clustering algorithm. This paper presents a current review on application clustering system in Acoustic Emission.

Correlation of Acoustic Emission and Corrosion Resistance of Lacquer Coatings on Tin-Free Steel

Acoustic emission (AE) is a non-destructive technique which is well known for crack monitoring. Moreover, it has been widely used for corrosion and erosion damage on metallic structures. In this study, AE technique was applied for quick monitoring of corrosion resistance of lacquer coatings on tin free steel and laminated steel for food can-packaging. There were three types of coatings: (A) BPA-NI lacquer, (B) BPA-complied lacquer, and (C) PET lamination. These coatings were investigated in flat-sheet and in deformed sheet. Cathodic disbonding (CD) technique was carried out to cause separation between the coating and the metal substrate and initialize corrosion damage. AE signals was detected immediately with potential excitation, and showed good correlation with coating corrosion resistance during corrosion development. AE count signal matched with resulting current during CD, and proportional to corroded areas. In addition, coatings properties were measured before and after the CD process by electrochemical impedance spectroscopy (EIS). A combination of AE and CD techniques offers an in-situ non-destructive corrosion monitoring of coating quality before and during delamination development, and useful for can-packaging testing.

Dynamic precision control in single-grit scratch tests using acoustic emission signals

Acoustic emission (AE) is very sensitive to minuscule molecular changes which allow it to be used in a dynamic control manner. The work presented here specifically investigates approaching grit and workpiece interaction during grinding processes. The single grit (SG) tests used in this work display that the intensities from air, occurring in between the grit and workpiece, show an increasing intensity as the grit tends towards the workpiece with 1-μm increments. As the grit interacts with the workpiece, a scratch is formed; different intensities are recorded with respect to a changing measured depth of cut (DOC). In the first instance, various AE were low tending towards high signal to noise ratios which is indicative of grit approaching contact; when contact is made, frictional rubbing is noticed, then ploughing with low DOC and, finally, actual cutting with a higher associated DOC. Dynamic control is obtained from the AE sensor extracting increasing amplitude significant of elastic changing towards greater plastic material deformation. Such control methods can be useful for grinding dressing ratios as well as achieving near optimal surface finish when faced with difficult to cut geometries. Two different materials were used for the same SG tests (aerospace alloys: CMSX4 and titanium-64) to verify that the control regime is robust and not just material dependent. The AE signals were then classified using neural networks (NNs) and classification and regression trees (CART)-based rules. A real-time simulation is provided showing such interactions allowing dynamic micro precision control. The results show clear demarcation between the extracted synthesized signals ensuring high accuracy for determining different phenomena: 3–1 μm approaching touch, touch, slight plastic deformation and, increasing plastic deformation. In addition to dressing ratios, the results are also important for micron accuracy set-up considerations.

Denoising Acoustic Emission Signal using Wavelet Transforms for Determining the Micro Crack Location Inside of Concrete

Acoustic emission (AE) technique is developed to locate source of damage inside of concrete. However, the AE signal is interfered by much noise, which makes the determination of first time amplitude of AE signal is hard to be carried out. In fact, the determination of this parameter is a significant part for locating the source of damage in concrete. Therefore, one of the denoising methods called wavelet based denoising is proposed. In this case, some wavelet bases function are investigated to find out the proper wavelet bases function to perform the denoising of AE Signal. From the experimental data, the best wavelet basis function for this case is Coiflet, which is shown by providing the best SNR than the other wavelet families. In addition, the determining cracks locations on concrete can be performed easier on denoised AE signal than on noisy AE signal.

Use of AE Testing Data for Condition Monitoring in Wind Turbine Gearbox

Acoustic emission (AE) method of nondestructive check is based on exertion wave radiation and their registration during fast local material structure reorganization. It is used as a means of analysis of materials, constructions, productions control and diagnosis during operating time. In the article, it is applied to structural health monitoring of Wind Turbine Gearbox (WTG). Acoustic emission testing has been used for years to test metallic structures. More recently it has become the primary method of testing WTG; all are present in the failure of WTG. AE has been very successful at detecting all of these failure mechanisms and sometimes identifying them from amplitude analysis of the AE signals. However in large structures, the high acoustic attenuation in WTG precludes amplitude analysis unless the origin of the individual signals can be identified and corrections for the distances traveled applied to the signal amplitudes. The usual method of testing WTG structures has been to apply an array of sensors spaced so that a moderate amplitude AE signal occurring midway between them will just barely trigger each sensor.

Extracting Acoustic Emission Signal Feature of Grinding Processing

Acoustic Emission (AE) may be defined as a transient elastic stress wave generated by the rapid release of strain energy in local area of material. To overcome the limitation of some traditional techniques, the AE technique, which provides high sensitivity and responding speed, were developed in the present paper. AE signature is usually difficult to be extracted and characterized in grinding process of 1Cr18Ni9Ti coatings due to their high hardness, great ductility, inhomogeneous structure and irregular surface with lots of hard points and pores. In this paper, AE signal of stationary grinding status before wheel-workpiece contact was characterized first, then AE signal of the grinding process was analyzed using root-mean-square (RMS) and power spectrum method. Results showed that before the contact occurred, the grinding signal is stable, with low amplitude and frequency ranging all frequency channels and no peak signal. However, when contact occurred, the RMS and spectrum of AE signal increased obviously and the bandwidth varied exquisitely between 100 KHz and 300 KHz. The real contact time between wheel and workpiece was about 0.5 to 1 ms.

Real-Time Monitoring of Degradation of Zirconia Ceramic by Acoustic Emission

Acoustic emission (AE) technique was employed to monitor the low temperature degradation (LTD) behavior of a zirconia ceramic, which was carried out in air at 200°C. AE signals, typical waveform of which exhibited as pulse-like type, were detected during LTD and increased with the duration time of LTD. The AE signal of accumulative counts number and amplitude in time domain exhibited a roughly sigmoid variation including three stages, which reflect approximately the evolution of LTD. The AE findings were supported by X-ray diffraction (XRD) examination. It was suggested that AE technique can be used to monitor the mechanism of LTD of zirconia ceramics.