Acoustic Emission Patterns Research Articles

Laboratory Investigation on the Effects of Natural Fracture on Fracture Evolution of Granite Exposed to Freeze-Thaw-Cyclic (FTC) Loads

The natural fractures in rock mass are susceptible to damage evolution when subjecting to repeated freeze-thaw (F-T) weathering in cold regions, which can lead to the instability of rock engineering and even occurrence of geological hazards. Knowledge of how natural fracture impacts the overall fracture evolution of freeze-thawed rock is important to predict the stability of rock structure. In this work, we reported uniaxial experimental measurements of the changes in strength, deformation, acoustic emission (AE) pattern, and Felicity effect during increasing amplitude stress-cycling conditions on granite. The results show that the change of fracture aperture is related to the fracture openness and filling characteristics, open-type fracture is sensitive to F-T treatment, and its aperture increases faster than the close-type and fill-type fracture. In addition, strength decreases, and the damping characteristics first decrease and then increase with increasing natural fracture volume. AE activities also present different responses during sample deformation. The proportion of AE signals having low-frequency characteristics increases with increasing natural fracture volume, and the shear sliding along natural fracture results in the surge of AE activities. Moreover, the Felicity effect indicates that the Felicity ratio presents a fluctuation decreasing trend, and the preexisting fractures alter the stress memory characteristics of rock. It is suggested that the changes of the geomechanical and AE pattern are the interactions between the natural fracture and the newly stimulated fracture. The testing results are expected to improve the understanding of the influence of natural fractures on rock fracture evolution and can be helpful to predict the stability of rock structures and rock mass in cold regions.

Prediction of creep failure time using machine learning

A subcritical load on a disordered material can induce creep damage. The creep rate in this case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady-state regime and a stage of accelerating creep that ultimately leads to catastrophic breakdown. Due to the statistical regularities in the creep rate, the time evolution of creep rate has often been used to predict residual lifetime until catastrophic breakdown. However, in disordered samples, these efforts met with limited success. Nevertheless, it is clear that as the failure is approached, the damage become increasingly spatially correlated, and the spatio-temporal patterns of acoustic emission, which serve as a proxy for damage accumulation activity, are likely to mirror such correlations. However, due to the high dimensionality of the data and the complex nature of the correlations it is not straightforward to identify the said correlations and thereby the precursory signals of failure. Here we use supervised machine learning to estimate the remaining time to failure of samples of disordered materials. The machine learning algorithm uses as input the temporal signal provided by a mesoscale elastoplastic model for the evolution of creep damage in disordered solids. Machine learning algorithms are well-suited for assessing the proximity to failure from the time series of the acoustic emissions of sheared samples. We show that materials are relatively more predictable for higher disorder while are relatively less predictable for larger system sizes. We find that machine learning predictions, in the vast majority of cases, perform substantially better than other prediction approaches proposed in the literature.

Bidirectional Gated Recurrent Deep Learning Neural Networks for Smart Acoustic Emission Sensing of Natural Fiber–Reinforced Polymer Composite Machining Process

Abstract Natural fiber–reinforced polymer (NFRP) composites are increasingly considered in the industry for creating environmentally benign product alternatives. The complex structure of the fibers and their random distribution within the matrix basis impede the machinability of NFRP composites as well as the resulting product quality. This article investigates a smart process monitoring approach that employs acoustic emission (AE)—elastic waves sourced from various plastic deformation and fracture mechanisms—to characterize the variations in the NFRP machining process. The state-of-the-art analytic tools are incapable of handling the transient dynamic patterns with long-term correlations and bursts in AE and how process conditions and the underlying material removal mechanisms affect these patterns. To address this gap, we investigated two types of the bidirectional gated recurrent deep learning neural network (BD-GRNN) models, viz., bidirectional long short-term memory and bidirectional gated recurrent unit to predict the process conditions based on dynamic AE patterns. The models are tested on the AE signals gathered from orthogonal cutting experiments on NFRP samples performed at six different cutting speeds and three fiber orientations. The results from the experimental study suggest that BD-GRNNs can correctly predict (around 87 % accuracy) the cutting conditions based on the extracted temporal-spectral features of AE signals.

Correlation between failure mechanism and rupture lifetime of 2D-C/SiC under stress oxidation condition based on acoustic emission pattern recognition

Creep tests of 2D-C/SiC in a wet oxidizing atmosphere were implemented for six samples. The loading process was monitored by acoustic emission (AE). Principal component analysis and a fuzzy clustering algorithm were used to perform pattern recognition of the AE data. All of the AE events were divided into four clusters and labelled as matrix cracking, interfacial damage, fiber breakage and fiber-bundle breakage respectively, according to their physical origin. It was found C/SiC has very scattered rupture lifetimes even under the same test conditions, and the evolution of AE events corresponding to fiber failure is quite different. With increasing rupture lifetime, the AE energy of fiber-bundle breakage is higher, while the number of these events is less. Thus, it is concluded that local oxidation and damage development is the controlling failure mechanism for short-lived specimens and uniform oxidation and damage development is the controlling failure mechanism for long-lived specimens.

Rock bridge fracturing characteristics in granite induced by freeze-thaw and uniaxial deformation revealed by AE monitoring and post-test CT scanning

This work presents the impacts of freeze-thaw (F-T) fatigue damage on the crack coalescence behaviors at the rock bridge segment for granite samples containing two unparallel flaws. The pre-existing flaw geometry in the rock was a combination of a horizontal flaw and an upper inclined flaw forming a rock bridge structure. In-situ acoustic emission (AE) detection technique and post-test X-ray computed tomography (CT) scanning were used to reveal the rock bridge fracturing characteristics during the whole deformation. Results show that both the F-T cycle and flaw inclined angle influence the crack propagation path, sample strength, deformation and AE pattern and the rock bridge fracture behavior. The AE counts and energy curves present skip phenomenon and are in consistent with the stress drop points on the macroscopic stress-strain curves. The accumulative AE energy and count decrease with increasing F-T cycles. In addition, the cracks during sample failure can be classified into six types by AE spectral frequency analysis, and the high amplitude - low frequency signal was good evident to predict the brittle fracture of the rock bridge. By the post-test CT scanning visualization of the internal crack coalescence pattern at the rock bridge segment, the most striking finding is that the rock bridge structure easily deteriorates for rock subjected to high F-T damage, which suggests that repeated F-T damage drives the crack propagation at the flaw tip and can accelerate the communication of the pre-existing flaws. The testing results are expected to enhance the understanding of the F-T fatigue damage effect on the rock bridge fracturing and can be favorable for predicting the stability of rock structures and rock mass in cold regions.

Acoustic Emission of Frozen Soils under Quasi-Static Mechanical and Cyclic Thermal Loading

The effects of acoustic emission (AE) in soils during freezing and thawing under cyclic thermal and quasi-static mechanical loading have been studied. The composition and characteristics of the developed instrumentation set are presented. The informative AE parameters and primary data processing approaches were justified. The acoustic emission patterns of soil material behavior under the variable thermobaric conditions were obtained. The comparative tests were performed using static probing and ultrasonic scanning.

Full-waveform-based characterization of acoustic emission activity in a mine-scale experiment: a comparison of conventional and advanced hydraulic fracturing schemes

SUMMARYUnderstanding fracturing processes and the hydromechanical relation to induced seismicity is a key question for enhanced geothermal systems (EGS). Commonly massive fluid injection, predominately causing hydroshearing, are used in large-scale EGS but also hydraulic fracturing approaches were discussed. To evaluate the applicability of hydraulic fracturing techniques in EGS, six in situ, multistage hydraulic fracturing experiments with three different injection schemes were performed under controlled conditions in crystalline rock at the Äspö Hard Rock Laboratory (Sweden). During the experiments the near-field ground motion was continuously recorded by 11 piezoelectric borehole sensors with a sampling rate of 1 MHz. The sensor network covered a volume of 30×30×30 m around a horizontal, 28-m-long injection borehole at a depth of 410 m. To extract and characterize massive, induced, high-frequency acoustic emission (AE) activity from continuous recordings, a semi-automated workflow was developed relying on full waveform based detection, classification and location procedures. The approach extended the AE catalogue from 196 triggered events in previous studies to more than 19 600 located AEs. The enhanced catalogue, for the first time, allows a detailed analysis of induced seismicity during single hydraulic fracturing experiments, including the individual fracturing stages and the comparison between injection schemes. Beside the detailed study of the spatio-temporal patterns, event clusters and the growth of seismic clouds, we estimate relative magnitudes and b-values of AEs for conventional, cyclic progressive and dynamic pulse injection schemes, the latter two being fatigue hydraulic fracturing techniques. While the conventional fracturing leads to AE patterns clustered in planar regions, indicating the generation of a single main fracture plane, the cyclic progressive injection scheme results in a more diffuse, cloud-like AE distribution, indicating the activation of a more complex fracture network. For a given amount of hydraulic energy (pressure multiplied by injected volume) pumped into the system, the cyclic progressive scheme is characterized by a lower rate of seismicity, lower maximum magnitudes and significantly larger b-values, implying an increased number of small events relative to the large ones. To our knowledge, this is the first direct comparison of high resolution seismicity in a mine-scale experiment induced by different hydraulic fracturing schemes.

Geomechanical and Acoustic Properties of Intact Granite Subjected to Freeze–Thaw Cycles during Water-Ice Phase Transformation in Beizhan’s Open Pit Mine Slope, Xinjiang, China

The deterioration of rock geomechanical behaviors subjected to freeze–thaw (F–T) action is a determining factor for rock engineering and rock structures in cold regions. In this work, taking six groups of granite obtained from an open pit mine as the research object, F–T cycle treatment, in-situ AE (acoustic emission) monitoring and ultrasonic detection techniques were performed to experimentally reveal the effects of F–T fatigue damage on the mechanical and acoustic properties of granite. The results indicate that the F–T action impacts the rock’s mesoscopic structure, deformation, strength, P and S-wave velocities, AE pattern and energy release. The accumulated AE counts and accumulated AE energy show a decreasing trend as the F–T cycle increases. The frequency spectrum revealed that the width of the low frequency band decreases and the high frequency band increases with increasing F–T cycles, indicating that there is an increase in large-scale cracks for a sample with high F–T treatment. In addition, energy balance analysis further illustrates the energy dissipation and release mechanism. The energy proportion used to drive the crack propagation is relatively small with high F–T treatment, and the final released energy becomes the minimum. The energy evolution characteristics analyzed by the energy balance approach is in good agreement with AE results. It is suggested that the F–T fatigue damage influences the rock energy storage and release characteristics and the instability of rock in the cold regions.

Unidirectional distributed acoustic reflection transducers for quantum applications

Recent significant advances in coupling superconducting qubits to acoustic wave resonators have led to demonstrations of quantum control of surface and bulk acoustic resonant modes as well as Wigner tomography of quantum states in these modes. These advances were achieved through the efficient coupling afforded by piezoelectric materials combined with GHz-frequency acoustic Fabry-Perot cavities. Quantum control of “itinerant” surface acoustic waves appears in reach but is challenging due to the limitations of conventional transducers in the appropriate gigahertz-frequency band. In particular, gigahertz-frequency unidirectional transducers would provide an important addition to the desired quantum toolbox, promising unit efficiency with directional control over the surface acoustic wave emission pattern. Here, we report the design, fabrication, and experimental characterization of unidirectional distributed acoustic reflection transducers demonstrating a high transduction frequency of 4.8 GHz with a peak directivity larger than 25 dB and a directivity greater than 15 dB over a bandwidth of 17 MHz. A numerical model reproduces the main features of the transducer response quite well, with ten adjustable parameters (most of which are constrained by geometric and physical considerations). This represents a significant step toward quantum control of itinerant quantum acoustic waves.

ACOUSTIC EMISSION DIRECTIVITY PATTERN OF THE COAXIAL PROPELLER FAN

The problem of further aircraft noise abatement is one of the priority issues in civil aviation. In order to improve engine efficiency, the coaxial propellers (propeller fans) may be used. The review of literature sources shows that parameters of the propeller fan have an effect on directivity of acoustic emission of the propeller fan. The objective of this work is the assessment of increased clearance effect between rows of propellers of the coaxial propeller fans on intensity and directivity of acoustic emission of the propeller fan. The engine with two row coaxial propeller fan has been selected as the object. Diameter of the propeller fan is 4.5 m. There is base and modified propeller fan addressed in the work. The work includes acoustic emission patterns for base and modified propeller fan made by research results. The acoustic emission pattern illustrates change of acoustic power when increasing a distance between rows of propeller fan. The peaks of directivity characteristic of total emission exist in backward semi-sphere in the direction of 110º-150º. Based on the results of numerical simulation of the air flow in base propeller fan it is demonstrated that in the inlet of second row of propeller fan there is high level of air distortion. The research results of intensity and directivity of acoustic emission of the coaxial propeller fan shows that the air distortion in the inlet of second row of propeller fan is an additional source of acoustic emission into the backward semi-sphere. When increasing axial clearance between rows of propeller fan by 300 mm, the total acoustic power is reduced by 2.5 — 4.6 dB in the backward semi-sphere. In future it is planned to assess acoustic characteristics of the propeller fan when changing geometry of the second row of the propeller.

Bearing failure prediction using audio signal analysis based on SVM algorithms

Bearings are machine elements used in a wide variety of applications including transportation. The accurate prediction of a bearing failure is important to sensitive applications to secure its safety during the service life. Bearing failure prediction is useful both in bearing testing phase as well as in case of lifetime use. Real-time Audio signal analysis and advanced algorithms are able to identify the incipient failure, caused by defects, fatigue, overload or poor maintenance. Audio signal analysis and processing remains a domain where technique and algorithm needs to be developed. In this paper is presented a proof-of-concept technique and equipment developed to predict failure of bearings in case of testing phase. For this study, acoustic emission signals were measured and analyzed during life testing of bearing while other sound source are also recorded. Correlation between the acoustic emission patterns were identified in order to identify noise signal and identify the signal associated with bearing degradation. The developed solution to isolate other sounds signals means that the technique could be used while lifetime of the bearings. The results of this study provide evidence that accurate estimation of the failure of various bearings is possible by processing the vibration signal acquired from a single point, even in case of multiple sound sources are present and introduce noise in signal processing. The SVM classifier provides at least 92% mean accuracy. The influence of model on prediction accuracy has also been discussed in the work.

Experimental investigation of mode I fracture features of steel fiber-reinforced reactive powder concrete using semi-circular bend test

Semi-circular bend tests were performed on reactive powder concrete (RPC) to investigate the mode I fracture characteristics. A total of 12 RPC specimens including non-fibrous RPC and steel fiber-reinforced RPC (SFR-RPC) with 1%, 2% and 4% volumetric fractions of steel fiber were tested. The characteristics of acoustic emission, fracture parameters and failure patterns were systematically investigated. The test results show that with the incorporation of steel fibers, the SFR-RPC specimens own an increasing enhancement of ductility, carrying capacity and AE activities with the increase of the steel fiber content. The crack initiation deflection, initiation toughness and initiation energy of the specimens vary as exponential functions of the steel fiber content, while the fracture toughness and fracture energy follow linear functions against the steel fiber content. The produced fracture surface shows a decrease in the smoothness with the increase of the steel fiber content.

Spectral Subband Centroid Energy Vectors Algorithm and Artificial Neural Networks for Acoustic Emission Pattern Classification

This work proposes and evaluates a methodology for monitoring and diagnosis of polymeric insulators in operation based on the parameterization of acoustic emissions (AE) created by cor ...

Acoustic emission pattern recognition in CFRP retrofitted RC beams for failure mode identification

The application of fiber reinforced polymer (FRP) composites to repair reinforcement concrete (RC) structures has emerged as a new and viable choice. However, the understanding of the durability and long-term performance of this combined system still remains elusive. Adopting non-destructive techniques such as acoustic emission (AE) will raise confidence in exploiting the full potential of this material. The objective of the current study is to identify failure mechanisms in CFRP-retrofitted RC beams by applying advanced pattern recognition techniques on the collected AE data. Six RC beams with artificially induced damage repaired with CFRP sheets are tested with flexural loads and monitored with AE sensors. Since damage mechanisms in the retrofitted RC beams are unknown a priori, a pattern recognition methodology is developed. After preprocessing the AE data using the principal component analysis (PCA), the unsupervised k-means clustering method is applied to automatically cluster and separate the AE patterns. The neural networks based on multi-layer perceptron (MLP) or support vector machine (SVM) algorithm are then developed to better understand the trends in the AE data and their association with the observed damage mechanism. Finally, the trained models are used to successfully identify damage modes in other similar samples.

Characterization of piezoelectric wafer active sensor for acoustic emission sensing

In this article, a new avenue of using the piezoelectric wafer active sensor (PWAS) for detecting the fatigue crack generated acoustic emission (AE) signals is presented. In-situ AE-fatigue experiments were conducted using PWAS along with two commercially available AE sensors. It has been shown that the PWAS and existing AE sensors successfully captured the AE signals from the fatigue crack growth in a thin aerospace specimen. Two experiments were conducted using the PWAS with each of the commercial AE sensors. For each experiment, two AE analyses were performed: (1) the hit-based analysis, (2) the waveform-based analysis. The fatigue loading was synchronized with the AE measurements. This allowed comparing the AE hits due to a particular AE event captured by PWAS and the other sensors. All the sensors showed a very similar pattern of AE hits as observed from the hit-based analysis. The AE waveform-based analysis was used to compare the waveforms and their frequency spectra captured by the three sensors. The commercial PICO showed ringing in the AE signals and showed a weak response in high-frequency region. The commercial S9225 had better signal-to-noise ratio but it also showed a weak response in high-frequency region. It was found that all sensors captured the low-frequency flexural modes of the guided acoustic waves. However, the high-frequency acoustic wave signals were predominately captured by the PWAS. The AE waveform-based analysis provided more insight of the AE source and guided wave propagation modes.

Identification of corrosion mechanisms on 13% Cr martensitic stainless steel in thiosulphate containing chloride solution by acoustic emission technique

The use of brine containing thiosulphate solution to simulate the sour gas environment was recently evaluated in the scientific literature to overcome the problems related to H2S use for laboratory testing of corrosion-resistant alloy (CRA) for Oil and Gas industry. However, corrosion attacks on stainless steel alloys are strongly influenced by thiosulphate to chloride concentration ratio. The identification of a reliable non destructive monitoring technique that can allow to distinguish between the different corrosion mechanisms is a big issue and it is the main aim of the present paper. Corrosion behaviour under tensile load of 13% Cr martensitic stainless steel in a modified NACE TM0177 standard solution at room temperature in presence of thiosulphate ions at different concentration (from 10-3 to 10-1 M) was evaluated by acoustic emission technique. By microstructural observations and a bivariate analysis it was possible to evidence how some acoustic emission patterns can clearly identify specific corrosion mechanisms.

An Experimental Investigation on the Relationship between MS Frequency Response and Coal and Gas Outburst

Microseismic (MS) frequency response is an important part of high‐efficiency data mining to achieve the aim of coal and gas outburst (CGOB) early warning. Based on the variation pattern of acoustic emission (AE) signal in the coal failure process, the experimental characteristics of MS activity and typical signals CGOB were obtained in this study. First, the AE behavior of coal failure experiment was studied, and an explanation of laws was provided as follows: the fracture behavior of coal sample exhibits certain characteristics of AE response in terms of AE event count, signal amplitude, and frequency; each stage has its own physical meaning during the process of loading test. Based on these laws, CGOB experiments were carried out using a large CGOB physical simulation system with a MS monitoring system. Notching filter and wavelet packet transform technique were used in the denoising and feature extraction of six typical MS events (signals). The features of each stage, including the time‐frequency domain, were extracted and quantitatively expressed. We finally arrive at the following conclusions: (1) CGOB exhibits significantly periodic characteristics, and each CGOB stage corresponds to the significant response characteristics of MS. CGOB presents varying characteristics, such as “valley‐peaks‐valley”. (2) From the incubation stage to happen stage of outburst, the spectrum significantly moved from extremely low frequency (100‐200 Hz) to high‐frequency band (approach to 1600 Hz). During the residual stage, MS frequency manifested the concentration distribution (50 Hz) and offered the advantage of energy concentration. (3) The phenomenon of signal energy also shows the trend of energy transform low to high and to low modes along with the process. Signals total energy distribution (42.81%, 1,437.5‐1,812.5 Hz) in the happen stage are markedly larger than those of events in incubation stage (7.01%) and residual stage (1.44%). The methodology presented in this paper for CGOB signal analysis provides a new method to obtain MS response precursor and predict CGOB disaster. This approach can be useful for rockburst anticipation and control during mining in gas and highly stressed coal mines.

Acoustic emission pattern of shale under uniaxial deformation

The brittle fracture mechanism of shale is an important issue to both borehole stability and hydraulic fracturing. A number of experimental studies on shale macroscopic mechanical properties have been carried out; however, the acoustic emission (AE) patterns of anisotropic shale are not fully understood. In this paper, a series of uniaxial compressive strength tests were conducted on cylindrical shale obtained by drilling at different orientations to the bedding plane. The anisotropic failure modes of shale samples have been monitored by real-time AE monitoring and stress–strain response, under ambient conditions and uniaxial stresses. The experimental results suggest that the pronounced bedding planes of shale have a significant influence on the mechanical properties and the AE responses. The AE counts and AE accumulative energy release curves clearly indicate the initiation and propagation of cracks during compression, and the ‘U’ shaped curves well reflect the anisotropic failure mechanism. The sounded foliation in the shale formation results in the anisotropy of elasticity, strength, AE counts and accumulated energy release from which the changes in strength dominate the entire failure pattern of shale samples. It is suggested that the AE activities monitored during deformation can provide good information to grasp the anisotropic failure mechanism of shale.

Estimation of Crack Initiation and Propagation Thresholds of Confined Brittle Coal Specimens Based on Energy Dissipation Theory

A new energy-dissipation method to identify crack initiation and propagation thresholds is introduced. Conventional and cyclic loading–unloading triaxial compression tests and acoustic emission experiments were performed for coal specimens from a 980-m deep mine with different confining pressures of 10, 15, 20, 25, 30, and 35 MPa. Stress–strain relations, acoustic emission patterns, and energy evolution characteristics obtained during the triaxial compression tests were analyzed. The majority of the input energy stored in the coal specimens took the form of elastic strain energy. After the elastic-deformation stage, part of the input energy was consumed by stable crack propagation. However, with an increase in stress levels, unstable crack propagation commenced, and the energy dissipation and coal damage were accelerated. The variation in the pre-peak energy-dissipation ratio was consistent with the coal damage. This new method demonstrates that the crack initiation threshold was proportional to the peak stress (σ p) for ratios that ranged from 0.4351 to 0.4753σ p, and the crack damage threshold ranged from 0.8087 to 0.8677σ p.

The use of b-value and Ib-value of acoustic emission in monitoring hydrogen-assisted cracking of martensitic stainless steel

Hydrogen-assisted cracking phenomena in thiosulphate solution of martensitic stainless steel were investigated by using acoustic emission technique. A time domain approach was adopted in order to analyse the corrosion phenomena by acoustic emission pattern time evolution. An appropriate set of variables was used, some of them directly related to the acoustic event waveform, while others have been derived from them, such as b-value and Ib-value, which are descriptors related to the time domain distribution of the acoustic event energies. Aim of this work is to show how these two descriptors allow to obtain an easy-to-use diagnostic system for damage progression, without a deep knowledge of the acting corrosion phenomena themselves. Tests were carried out on a X12Cr13 martensitic stainless steel, according to NACE TM 0177 standard, using a modified sour simulated environment (5% NaCl, 2.5% acetic acid and 10-2 M Na2S2O3).