Acoustic Emission Source Characterization Research Articles

A Deep Learning Framework for Acoustic Emission Sources Localization and Characterization in Complex Aerospace Panels

This paper presents a data-driven approach based on deep stacked autoencoders for the localization and characterization of acoustic emission sources in complex aerospace panels. The approach leverages the multimodal and dispersive reverberations of acoustic emissions. The approach is validated by Hsu-Nielsen pencil lead break tests on a fuselage section of a Boeing 777 instrumented with a single piezoelectric sensor.

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors’ knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a ten-fold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5”) and 48 mm (1.9”), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5”) and 67 mm (2.1”), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with single-sensor.

Inversion algorithm for Lamb-wave-based depth characterization of acoustic emission sources in plate-like structures

An inversion algorithm (termed AEDep) is proposed for estimating the depth of acoustic emission (AE) sources in plate-like structural components. The work is motivated by the need for characterizing early-stage fatigue crack growth in such components. The algorithm achieves depth estimation by automatically extracting the depth-dependent amplitude ratio between the fundamental Lamb modes which comprise the AE signals. A finite element model is designed to study the frequency-dependent forward problem of Lamb wave motion due to a given source, from which the relation between source depth and amplitude ratio is established. Elastodynamic theory is used to validate the model in the frequency domain, as well as to derive a sensor tuning factor which may be incorporated into the solution. The proposed algorithm was tested on two plate-like specimens: a 6061-T6 aluminum plate and a 2025-T6 aluminum aircraft fuselage panel. Validation of the algorithm was achieved by generating controlled AE sources at various depths along the edges of the specimens, in the form of Hsu-Nielsen pencil lead breaks. Good agreement was found in the aluminum plate between the true and estimated source depths. A slight decrease in accuracy was found in the fuselage panel between the true values and their estimations. However, both experimental cases demonstrated the ability to distinguish between sources originating near the mid-plane of a plate-like structure from those near the surface. Lastly, the fast computation of the inversion algorithm shows strong potential for real-time monitoring applications.

Study on Denoising of Corrosion Acoustic Emission Signals of Tank Bottom Based on Independent Component Analysis

When tank bottom is detected by acoustic emission method, many corrosion acoustic emission signals can be obtained and adulterated many noise signals, which influence badly the estimation to the corrosion situation of tank bottom. In order to identify acoustic emission sources and disturbance sources without changing the characterization of acoustic emission sources, independent component analysis is used to deal with the denoising of corrosion acoustic emission signals of tank bottom in this paper. In the paper, acoustic emission signals of double exponential model is respectively mixed with white noise signals and stochastic noise signals, and acoustic emission sources and disturbance sources are respectively represented by double exponential model of acoustic emission signals and noise signals, which are independent on statistics, and then FastICA is used to simulation analysis, which is successful to identify acoustic emission signals and white noise signals. The results demonstrate that fastICA is effective to denoise acoustic emission signals.

20710 バイオセラミックスにおける微視き裂発生過程のAE原波形解析法による定量評価(バイオエンジニアリングI,OS.7 バイオエンジニアリング)

20710 バイオセラミックスにおける微視き裂発生過程のAE原波形解析法による定量評価(バイオエンジニアリングI,OS.7 バイオエンジニアリング)

Characterization of Acoustic Emission Sources in a Rock Salt Specimen under Triaxial Compression

This study presents the application of an automated moment tensor analysis procedure on microcracks, which were generated during a triaxial compression test of a cylindrical rock salt specimen (diameter 150 mm, length 300 mm). The acoustic emission signals were detected in a frequency range between 20 kHz and 1 MHz using 12 acoustic emission sensors mounted on the surface of the specimen cylinder. The moment tensor analysis was applied to about 30,000 events, which were precisely located using at least 16 P - and S -wave arrival times. For more than 40% of these events, approximately 12,500 events, stable moment tensor solutions could be evaluated using the first motion of the P -wave radiation patterns. Most of the evaluated events showed significant isotropic source components, which is in good agreement with dilatation of the rock during compressional loading. The majority of the events were caused by tensile opening leading to dilatation of the rock. The tension ( T ) axes, which are normal to the crack plane of these tensile microcracks, were predominantly oriented radially in the cylindrical specimen, perpendicular to the maximum principal stress, which is oriented axially. The direction of the tensile opening calculated by the moment tensor evaluation coincides very well with the direction of the minimum principal stress. The applied collapsing method discovers cellular structures with a cell size in the range of a few centimetres. However, it seems that the events occur only in zones where the cell interfaces are favourably orientated in the stress field. These events are attributed to cracking at grain interfaces which occurs in rock salt under very slow creep loading above the dilatancy boundary.

Characterization of acoustic and electromagnetic emission sources

The creation of cracks is accompanied by electric charge redistribution due to loosened chemical bonds. Electric charges on crack walls create dipole moments. Vibrations of crack walls produce time-dependent dipole moments and, consequently, electric and magnetic fields are generated. An electric signal is induced on metal electrodes. Information about the vibration of crack walls was obtained from this signal analysis. For crack lengths below 1 mm the electrical signal has a frequency of over 2 MHz. In this paper the frequency analysis was performed in a frequency band of up to 5 MHz.

On using peak amplitude and rise time for AE source characterization

Acoustic Emission (AE) signals, which are electrical version of acoustic emissions, are usually analysed using a set of signal parameters. The major objective of signal analysis is to study the characteristics of the sources of emissions. Peak amplitude(P a ) and rise time(R t ) are two such parameters used for source characterization. In this paper, we theoretically investigate the efficiency ofP a andR t to classify and characterize AE sources by modelling the input stress pulse and transducer. Analytical expressions obtained forP a andR t clearly indicate their use and efficiency for source characterization. It is believed that these results may be of use to investigators in areas like control systems and signal processing also

A study on fracture behavior of particle reinforced metal matrix composites by using acoustic emission source characterization

A one directional acoustic emission (AE) source characterization has been used during a three point bending fracture toughness test on 6061 aluminum matrix composites with Al 2O 3 particle reinforcements of 5 and 10 μm sizes, in order to evaluate the dynamic process of micro-fracture in these materials. Different acoustic emission sources are characterized and, as a result, two types of AE events are distinguished. It is observed that at very low strain levels void nucleation is the main source for acoustic emission. At higher levels, the micro pop-in of primary voids and their eventual coalescence results in a different type of acoustic emission. In fine particle reinforced materials, when the amplitude of AE events in void nucleation at fine particles is not high enough to be detected, the main source of AE events is only the void coalescence. By increasing the particle size, the number of detectable events during void nucleation is increased.

Characterization of the damage process in short-fibre/thermoplastic composites by acoustic emission

Fracture processes of short glass fibre-reinforced thermoplastic tensile specimens have been investigated in relation to acoustic emission (AE) characteristics. Two fibre diameters (d=10, 23 μm) were adopted for this study. Frequency analysis employing band-pass filters suggested that AE with higher amplitude was due to fibre breakages. Fibre–matrix interfacial failure and matrix fracture emitted lower amplitude AE waves. Reflected and transmitted optical microscopy, in combination with scanning electron microscopy, revealed that fibre breakage ahead of the artificial notch tip led to the initiation of a macroscopic fracture. This coincided with the results that AE peak amplitudes, Vp, showed a minimum at fracture initiation. When the macroscopic fracture propagated, Vp again increased rapidly, indicating additional fibre breakage. These fracture mechanism worked consistently for both d=10 and 23 μm. Based on the above findings, AE source characterization was proposed for the stable fracture process of short fibre-reinforced plastics. © 1998 Chapman & Hall

Response of multilayered composite laminates to dynamic surface loads

A theoretical investigation of the response of multilayered composite laminates to concentrated and distributed dynamic surface loads is carried out. Each layer of the laminate is assumed to be transversely isotropic and dissipative with arbitrarily oriented symmetry axis. The dissipative property of the material is modeled approximately through the introduction of a frequency-dependent damping function. A multiple transform technique is used to calculate the spectra and time histories of the displacements and stresses produced by a variety of dynamic loads, and the quantitative features of the waves produced in the laminate are determined. The methodology developed in this work is expected to be useful in the prediction of the response of composite laminates to impact loads and also in the characterization of acoustic emission (AE) sources in these materials under static and dynamic loads.

Acoustic emission source characterization in concrete under biaxial loading

The results are reported of a mode identification study on cracks produced during mixed mode loading of concrete. The locations of cracks were found to agree very well with the surface crack patterns for a variety of loading paths. The classification of cracks into mode I and mode II were conducted using a simplified moment tensor analysis. The results indicated that, in general, cracking occurs in mode I, even when the loading is pure shear. Mode II deformations generally follow the mode I cracks, as the crack interface is subjected to shear.

負荷中に発生する弾性波の定量解析による気相蒸着Ｔｉ（Ｃ，Ｎ）膜の力学特性と内部応力

The objective of this study is to examine the mechanical strength of ARE-deposited Ti (C, N) film by using acoustic emission (AE) source characterization under tensile and compressive loading. Threshold stress in the substrate steel causes Mode-I fractures in the film as determined by quantitative source inversion of AE signals emitted from the limited Ti (C, N) area with a uniform stress distribution. Film fractures were invariably associated with the emergence of slip steps in the substrate steel. The threshold stress for film fractures under tensile loading was almost 9times greater than the stress under compressive loading.A simple method for estimating internal stresses in film from threshold stresses under different loading was also proposed. The internal stress in the film, estimated from the maximum strain criterion, was -3.8GPa which coincided with the reported internal stress measured using X rays.

Acoustic Emission Source and Transmission Path Characterization Through Homomorphic Processing

Acoustic emission (AE) has been chosen as a means of monitoring cutting system changes from remote sensor locations. The determination of a processing strategy which can remove signal transmission path variability has been explored. This strategy, termed homomorphic processing, is developed and tested on both simulated and experimentally collected data. The recovery properties of this strategy are analyzed in terms of both AE source characterization and signal path transmission effects. These properties are found to be influenced by the choice of homomorphic lifter cutoff time. Guidelines are given for the selection of this parameter. Spectral representations of the recovered AE source show that signatures collected at remote sensor locations can be successfully used to identify process and structural changes in the cutting system.

Commentary on ‘quantitative acoustic emission source characterization of microcracking in steel’

Commentary on ‘quantitative acoustic emission source characterization of microcracking in steel’

High‐fidelity displacement sensors for acoustic emission source characterization

Acoustic emission (AE) source characterization requires the use of broadband, typically 20 kHz–2 MHz, ‘‘point‐contact’’ sensors. Generally, AE sensors based on the NIST ‘‘conical’’ transducer approach can meet this requirement. However, because naturally occurring AE events are considerably less energetic than those produced by artificial AE sources, optimization of the sensor signal‐to‐noise performance is of critical importance. Using a computer model, a variety of sensor‐design approaches have been studied, with particular emphasis on transducer/preamplifier/specimen compatibility issues. In this paper, computer‐model predictions are compared with the results of experiments. The use of different piezoelectric transducer materials—PZT‐5A, lead metaniobate, LiNbO3, X‐cut quartz, and PVDF—is considered. It is shown that ceramic materials exhibiting the highest dielectric constants, e33S, are best suited for most high‐performance AE sensor applications. In addition, the impact of preamplifier input capacit...

An experimental investigation of the AE source location and magnitude on 2-D frame structures using intelligent signal processing

The results of experimental investigation of acoustic emission (AE) source characterization in 2-D frames is reported in this paper. Determination of the AE source location and magnitude from the displacement field is an inverse problem. It is known that humans have remarkable sensory abilities to undo a large number of demanding inverse problems with little effort. Here, nature is modeled by implementing intelligent signal processing algorithms to solve the problems of the AE source location and magnitude determination. The signal processing algorithms implemented are; FFNN trained by a modified back-propagation algorithm, and a linear system called an auto-associative processor (AAP). The standard feature of these algorithms is the use of a set of pre-processed, measured signals to form a system memory. The pre-processed signals used by the AAP algorithm are tutor augmented, while in the case of the FFNN the pre-processed signals are nonlinearly mapped to the training vectors provided by the tutor. The system memory is consequently utilized to process experimental signals to determine some of the AE source properties. [Work partially supported by NSF and ONR.]

Quantitative acoustic emission source characterization of fatigue cracks in a thin-plate of 7075-T6 aluminum: Journal Applied Physics, Vol. 64, No. 11, pp. 6264–6275 (Dec. 1988)

Acoustic emission (AE) signals generated by microcrack formation in front of a propagating fatigue crack in a thin‐plate compact tension specimen of 7075‐T6 aluminum alloy were analyzed to investigate the dynamics of microcrack formation. The dominant wave motion was in‐plane so that the dynamic elastic wave field could be analyzed by generalized plane stress theory. The acoustic signals were detected by an array of four miniature piezoelectric transducers which were mounted on the edge of a specimen ahead of the advancing crack. A second‐rank force moment tensor was used to model the microcrack formation and a time‐domain double‐iterative signal deconvolution method was used to process the detected acoustic signals to remove the effects of the structure and to recover the temporal and spatial characteristics of the AE source. It was found that a force moment tensor model can be used to represent the growth of the microcrack.

Commentary on “quantiative acoustic emission source characterization of microcracking in steel”

Results published recently by Takatsubo and Kishi concerning acoustic emission during ductile fracture of A533B steel indicated that high-amplitude emissions observed were associated with tension/shear mixed-mode cracking. These results appear to be consistent with (and complementary to) the conclusions reached by the present author in work on pressure vessel steels, including A533B, in which the onset of slow stable cracking was associated with the occurrence of high-amplitude emissions, and in which these emissions were shown to be associated with a fracture mechanism involving shear linkage of voids. The present paper examines the conclusions which can be drawn from these complementary programs, and discusses the link between ductile fracture and acoustic emission.

Dynamic Crack Microscope and Its Application

A new generation acoustic emission (AE) source characterization system combined with detected wave analyzing system was developed for materials evaluation. The system consists of six AE transducers. parallel processing units. a host CPU and a desk-top computer subsystem. AE signals from each transducer are digitized by a high-speed ND converter in each processing unit. From digitized waveform data, AE parameters such as arrival time, peak amplitude, rise time and phase of the first motion of longitudinal wave are extracted by a Digitial Signal Processor (DSP) installed in each processing unit. The data of arrival time in each channel is fed into the host CPU to determine AE source location. The location result together with both AE parameters and waveform data are transferred to the desk-top computer, on which AE source functions are calculated by single deconvolution method using both the dynamic Green's function of a medium and the transfer function of the measuring system. Then the size and mean nuclea...