Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the ai...Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.展开更多
Direct shear tests were conducted on sandstone specimens under different constant normal stresses to study the coalescence of cracks between non-persistent flaws and the shear sliding characteristics of the shear-form...Direct shear tests were conducted on sandstone specimens under different constant normal stresses to study the coalescence of cracks between non-persistent flaws and the shear sliding characteristics of the shear-formed fault.Digital image correlation and acoustic emission(AE)techniques were used to monitor the evolution of shear bands at the rock bridge area and microcracking behaviors.The experimental results revealed that the shear stresses corresponding to the peak and sub-peak in the stressdisplacement curve are significantly affected by the normal stress.Strain localization bands emerged at both the tip of joints and the rock bridge,and their extension and interaction near the peak stress caused a surge in the AE hit rate and a significant decrease in the AE b value.Short and curvilinear strain bands were detected at low normal stress,while high normal stress generally led to more microcracking events and longer coplanar cracks at the rock bridge area.Furthermore,an increase in normal stress resulted in a higher AE count rate and more energetic AE events during friction sliding along the shearformed fault.It was observed that the elastic energy released during the crack coalescence at the prepeak stage was much greater than that released during friction sliding at the post-peak stage.More than 75%of AE events were located in the low-frequency band(0e100 kHz),and this proportion continued to rise with increasing normal stress.Moreover,more AE events of low AF value and high RA value were observed in specimens subjected to high normal stress,indicating that greater normal stress led to more microcracks of shear nature.展开更多
In order to study fracture mechanism of rocks in different brittle mineral contents,this study pro-poses a method to identify the acoustic emission signal released by rock fracture under different brittle miner-al con...In order to study fracture mechanism of rocks in different brittle mineral contents,this study pro-poses a method to identify the acoustic emission signal released by rock fracture under different brittle miner-al content(BMC),and then determine the content of brittle matter in rock.To understand related interference such as the noises in the acoustic emission signals released by the rock mass rupture,a 1DCNN-BLSTM network model with SE module is constructed in this study.The signal data is processed through the 1DCNN and BLSTM networks to fully extract the time-series correlation features of the signals,the non-correlated features of the local space and the weak periodicity law.Furthermore,the processed signals data is input into the fully connected layers.Finally,softmax function is used to accurately identify the acoustic emission signals released by different rocks,and then determine the content of brittle minerals contained in rocks.Through experimental comparison and analysis,1DCNN-BLSTM model embedded with SE module has good anti-noise performance,and the recognition accuracy can reach more than 90 percent,which is better than the traditional deep network models and provides a new way of thinking for rock acoustic emission re-search.展开更多
Zirconia ceramics have become increasingly widely used in recent years and are favored by relevant enterprises. From the traditional dental field to aerospace, parts manufacturing has been used, but there is limited r...Zirconia ceramics have become increasingly widely used in recent years and are favored by relevant enterprises. From the traditional dental field to aerospace, parts manufacturing has been used, but there is limited research on the deformation and damage process of zirconia ceramics. This article analyzes the acoustic emission characteristics of each stage of ceramic damage from the perspective of acoustic emission, and explores its deformation process characteristics from multiple perspectives such as time domain, frequency, and EWT modal analysis. It is concluded that zirconia ceramics exhibit higher brittleness and acoustic emission strength than alumina ceramics, and when approaching the fracture, it tends to generate lower frequency acoustic emission signals.展开更多
The rock fracture characteristics and principal stress directions are crucial for prevention of geological disasters.In this study,we carried out biaxial compression tests on cubic granite samples of 100 mm in side le...The rock fracture characteristics and principal stress directions are crucial for prevention of geological disasters.In this study,we carried out biaxial compression tests on cubic granite samples of 100 mm in side length with different intermediate principal stress gradients in combination with acoustic emission(AE)technique.Results show that the fracture characteristics of granite samples change from‘sudden and aggregated’to‘continuous and dispersed’with the increase of the intermediate principal stress.The effect of increasing intermediate principal stress on AE amplitude is not significant,but it increases the proportions of high-frequency AE signals and shear cracks,which in turn increases the possibility of unstable rock failure.The difference of stress in different directions causes the anisotropy of rock fracture and thus leads to the obvious anisotropic characteristics of wave velocity variations.The anisotropy of wave velocity variations with stress difference is probable to identify the principal stress directions.The AE characteristics and the anisotropy of wave velocity variations of granite under two-dimensional stress are not only beneficial complements for rock fracture characteristic and principal stress direction identification,but also can provide a new analysis method for stability monitoring in practical rock engineering.展开更多
Acoustic emission(AE)signals contain substantial information about the internal fracture characteristics of rocks and are useful for revealing the laws governing the release of energy stored therein.Reported here is t...Acoustic emission(AE)signals contain substantial information about the internal fracture characteristics of rocks and are useful for revealing the laws governing the release of energy stored therein.Reported here is the evolution of rock failure with diferent master crack types as investigated using Brazilian splitting tests(BSTs),direct shear tests(DSTs),and uniaxial compression tests(UCTs).The AE parameters and typical modes of each fracture type were obtained,and the energy release characteristics of each fracture mechanism were discussed.From the observed changes in the AE parameters,the rock fracture process exhibits characteristics of staged intensifcation.The scale and energy level of crack activity in the BSTs were signifcantly lower than those in the DSTs and UCTs.The proportion of tensile cracks in the BSTs was 65%–75%,while the proportions of shear cracks in the DSTs and UCTs were 75%–85%and 70%–75%,respectively.During the rock loading process under diferent conditions,failure was accompanied by an increased number of shear cracks.The amplitude,duration,and rise time of the AE signal from rock failure were larger when the failure was dominated by shear cracks rather than tensile ones,and most of the medium-and high-energy signals had medium to low frequencies.After calculating the proposed energy amplitude ratio,the energy release of shear cracks was found to exceed that of tensile cracks at the same fracture scale.展开更多
The anisotropy induced by rock bedding structures is usually manifested in the mechanical behaviors and failure modes of rocks.Brazilian tests are conducted for seven groups of shale specimens featuring different bedd...The anisotropy induced by rock bedding structures is usually manifested in the mechanical behaviors and failure modes of rocks.Brazilian tests are conducted for seven groups of shale specimens featuring different bedding angles. Acoustic emission (AE) and digital image correlation (DIC) technologies are used to monitor the in-situ failure of the specimens. Furthermore, the crack morphology of damaged samples is observed through scanning electron microscopy (SEM). Results reveal the structural dependence on the tensile mechanical behavior of shales. The shale disk exhibits compression in the early stage of the experiment with varying locations and durations. The location of the compression area moves downward and gradually disappears when the bedding angle increases. The macroscopic failure is well characterized by AE event location results, and the dominant frequency distribution is related to the bedding angle. The b-value is found to be stress-dependent.The crack turning angle between layers and the number of cracks crossing the bedding both increase with the bedding angle, indicating competition between crack propagations. SEM results revealed that the failure modes of the samples can be classified into three types:tensile failure along beddings with shear failure of the matrix, ladder shear failure along beddings with tensile failure of the matrix, and shear failure along multiple beddings with tensile failure of the matrix.展开更多
The mechanical properties of cemented paste backfill(CPB)determine its control effect on the goaf roof.In this study,the mechanical strength of polymer-modified cemented paste backfill(PCPB)samples was tested by uniax...The mechanical properties of cemented paste backfill(CPB)determine its control effect on the goaf roof.In this study,the mechanical strength of polymer-modified cemented paste backfill(PCPB)samples was tested by uniaxial compression tests,and the failure characteristics of PCPB under the compression were analyzed.Besides,acoustic emission(AE)technology was used to monitor and record the cracking process of the PCPB sample with a curing age of 28 d,and two AE indexes(rise angle and average frequency)were used to classify the failure modes of samples under different loading processes.The results show that waterborne epoxy resin can significantly enhance the mechanical strength of PCPB samples(when the mass ratio of polymer to powder material is 0.30,the strength of PCPB samples with a curing age of 28 d is increased by 102.6%);with the increase of polymer content,the mechanical strength of PCPB samples is improved significantly in the early and middle period of curing.Under uniaxial load,the macro cracks of PCPB samples are mostly generated along the axial direction,the main crack runs through the sample,and a large number of small cracks are distributed around the main crack.The AE response of PCPB samples during the whole loading process can be divided into four periods:quiet period,slow growth period,rapid growth period,and remission period,corresponding to the micro-pore compaction stage,elastic deformation stage,plastic deformation stage,and failure instability stage of the stress-strain curve.The AE events are mainly concentrated in the plastic deformation stage;both shear failure and tensile failure occur in the above four stages,while tensile failure is dominant for PCPB samples.This study provides a reference for the safety of coal pillar recovery in pillar goaf.展开更多
We investigate the accuracy and robustness of moment tensor(MT)and stress inversion solutions derived from acoustic emissions(AEs)during the laboratory fracturing of prismatic Barre granite specimens.Pre-cut flaws in ...We investigate the accuracy and robustness of moment tensor(MT)and stress inversion solutions derived from acoustic emissions(AEs)during the laboratory fracturing of prismatic Barre granite specimens.Pre-cut flaws in the specimens introduce a complex stress field,resulting in a spatial and temporal variation of focal mechanisms.Specifically,we consider two experimental setups:(1)where the rock is loaded in compression to generate primarily shear-type fractures and(2)where the material is loaded in indirect tension to generate predominantly tensile-type fractures.In each test,we first decompose AE moment tensors into double-couple(DC)and non-DC terms and then derive unambiguous normal and slip vectors using k-means clustering and an unstructured damped stress inversion algorithm.We explore temporal and spatial distributions of DC and non-DC events at different loading levels.The majority of the DC and the tensile non-DC events cluster around the pre-cut flaws,where macro-cracks later develop.Results of stress inversion are verified against the stress field from finite element(FE)modeling.A good agreement is found between the experimentally derived and numerically simulated stress orientations.To the best of the authors’knowledge,this work presents the first case where stress inversion methodologies are validated by numerical simulations at laboratory scale and under highly heterogeneous stress distributions.展开更多
Microcapsule self-healing technology is one of the effective methods to solve the durability problem of cementbased composites.The evaluation method of the self-healing efficiency of microcapsule self-healing cement-b...Microcapsule self-healing technology is one of the effective methods to solve the durability problem of cementbased composites.The evaluation method of the self-healing efficiency of microcapsule self-healing cement-based composites is one of the difficulties that limits the self-healing technology.This paper attempts to characterize the self-healing efficiency of microcapsule self-healing cement-based composites by acoustic emission(AE)parameters,which provides a reference for the evaluation of microcapsule self-healing technology.Firstly,a kind of self-healing microcapsules were prepared,and the microcapsules were added into the cement-based composites to prepare the compression samples.Then,the specimen with certain pre damage was obtained by compression test.Secondly,the damaged samples were divided into two groups.One group was directly used for compression tests to obtain the damage failure process.The other group was put into water for healing for 30 days,and then compression tests were carried out to study the influence of self-healing on the compression failure process.During the experiments,the AE signals were collected and the AE characteristics were extracted for the evaluation of self-healing efficiency.The results show that the compression pre damage test can trigger the microcapsule,and the compression strength of the self-healing sample is improved.The failure mechanism of microcapsule selfhealing cement-based composites can be revealed by the AE parameters during compression,and the self-healing efficiency can be quantitatively characterized by AE hits.The research results of this paper provide experimental reference and technical support for the mechanical property test and healing efficiency evaluation of microcapsule self-healing cement-based composites.展开更多
The stability of coal walls(pillars)can be seriously undermined by diverse in-situ dynamic disturbances.Based on a 3D par-ticle model,this work strives to numerically replicate the major mechanical responses and acous...The stability of coal walls(pillars)can be seriously undermined by diverse in-situ dynamic disturbances.Based on a 3D par-ticle model,this work strives to numerically replicate the major mechanical responses and acoustic emission(AE)behaviors of coal samples under multi-stage compressive cyclic loading with different loading and unloading rates,which is termed differential cyclic loading(DCL).A Weibull-distribution-based model with heterogeneous bond strengths is constructed by both considering the stress-strain relations and AE parameters.Six previously loaded samples were respectively grouped to indicate two DCL regimes,the damage mechanisms for the two groups are explicitly characterized via the time-stress-dependent variation of bond size multiplier,and it is found the two regimes correlate with distinct damage patterns,which involves the competition between stiffness hardening and softening.The numerical b-value is calculated based on the mag-nitudes of AE energy,the results show that both stress level and bond radius multiplier can impact the numerical b-value.The proposed numerical model succeeds in replicating the stress-strain relations of lab data as well as the elastic-after effect in DCL tests.The effect of damping on energy dissipation and phase shift in numerical model is summarized.展开更多
Acoustic emission(AE)is a nondestructive real-time monitoring technology,which has been proven to be a valid way of monitoring dynamic damage to materials.The classification and recognition methods of the AE signals o...Acoustic emission(AE)is a nondestructive real-time monitoring technology,which has been proven to be a valid way of monitoring dynamic damage to materials.The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning.Considering that the huge success of deep learning technologies,where the Recurrent Neural Network(RNN)has been widely applied to sequential classification tasks and Convolutional Neural Network(CNN)has been widely applied to image recognition tasks.A novel three-streams neural network(TSANN)model is proposed in this paper to deal with fault detection tasks.Based on residual connection and attention mechanism,each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient(MFCC),Tempogram,and short-time Fourier transform(STFT)spectral respectively.Experimental results show that,in comparison with traditional classification methods and single-stream CNN networks,TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%,which demonstrates the availability of the model proposed.展开更多
Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional...Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional tests of mechanical property can hardly meet this requirement.Design/methodology/approach-In this study the acoustic emission(AE)technology is applied in the tensile tests of the gearbox housing material of an high-speed rail(HSR)train,during which the acoustic signatures are acquired for parameter analysis.Afterward,the support vector machine(SVM)classifier is introduced to identify and classify the characteristic parameters extracted,on which basis the SVM is improved and the weighted support vector machine(WSVM)method is applied to effectively reduce the misidentification of the SVM classifier.Through the study of the law of relations between the characteristic values and the tensile life,a degradation model of the gearbox housing material amid tensile is built.Findings-The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process,and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%.The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.Originality/value-The results of this study provide new concepts for the life prediction of tensile samples,and more further tests should be conducted to verify the conclusion of this research.展开更多
Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and in...Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.展开更多
Biometrics,which has become integrated with our daily lives,could fall prey to falsification attacks,leading to security concerns.In our paper,we use Transient Evoked Otoacoustic Emissions(TEOAE)that are generated by ...Biometrics,which has become integrated with our daily lives,could fall prey to falsification attacks,leading to security concerns.In our paper,we use Transient Evoked Otoacoustic Emissions(TEOAE)that are generated by the human cochlea in response to an external sound stimulus,as a biometric modality.TEOAE are robust to falsification attacks,as the uniqueness of an individual’s inner ear cannot be impersonated.In this study,we use both the raw 1D TEOAE signals,as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform(CWT).We use 1D and 2D Convolutional Neural Networks(CNN)for the former and latter,respectively,to derive the feature maps.The corresponding lower-dimensional feature maps are obtained using principal component analysis,which is then used as features to build classifiers using machine learning techniques for the task of person identification.T-SNE plots of these feature maps show that they discriminate well among the subjects.Among the various architectures explored,we achieve a best-performing accuracy of 98.95%and 100%using the feature maps of the 1D-CNN and 2D-CNN,respectively,with the latter performance being an improvement over all the earlier works.This performance makes the TEOAE based person identification systems deployable in real-world situations,along with the added advantage of robustness to falsification attacks.展开更多
Background:Studies on animals have demonstrated that maternal iron deficiency anaemia(IDA)could result in decreased cochlear sensory hair cells and reduced amplitudes of distortion-product otoacoustic emissions(DPOAEs...Background:Studies on animals have demonstrated that maternal iron deficiency anaemia(IDA)could result in decreased cochlear sensory hair cells and reduced amplitudes of distortion-product otoacoustic emissions(DPOAEs)of young guinea pigs.Thus,it is essential to study the functioning of cochlear hair cells using DPOAEs in human newborn babies with maternal IDA.The current study explores maternal IDA’s effect on DPOAEs in newborn babies.Method:A total of 110 newborn babies with gestational age≥34 weeks were considered and a‘betweensubjects’design was used.The participants were divided into 3 groups-“Normal”(61 babies without maternal IDA),“Mild”(28 babies with mild maternal IDA)and“Moderate”(21 babies with moderate maternal IDA).The cord blood was collected and the DPOAEs were recorded for each baby for a range of frequencies(1 k 8 kHz)and a range of intensities(7040 dB SPL in 10 dB steps).Results:The analysis of both DP-gram and DP input-output(I/O)function showed that there was no significant difference(p>0.05)across the normal,mild,and moderate groups in the overall presence of DPOAEs as well as the amplitude across frequencies or intensities(7040 dB SPL).Also,the overall correlation of RBC indices with DPOAE amplitude across frequencies as well as the slope of the I/O function showed no relationship.Conclusion:The current study concludes that there is no effect of late-term maternal IDA on the DPOAEs of newborn babies.展开更多
Objectives:This study aimed to determine the prognostic value of otoacoustic emissions(OAEs)in idiopathic sudden sensorineural hearing loss patients.Methods:The study included 30 subjects with unilateral idiopathic su...Objectives:This study aimed to determine the prognostic value of otoacoustic emissions(OAEs)in idiopathic sudden sensorineural hearing loss patients.Methods:The study included 30 subjects with unilateral idiopathic sudden sensorineural hearing loss(ISSNHL).Each patient was evaluated four times:at baseline and after one week,one month,and three months of treatment.During each visit,each patient was subjected to full audiological history,otoscopic examination,basic audiological evaluations,and transiently evoked and distortion product otoacoustic emission(TEOAEs&DEOAEs).Results:The hearing thresholds(frequency range 250e8000 Hz)and word recognition scores of patients with detectable TEOAEs and DPOAEs improved significantly,whereas no significant improvements were observed in those with no response.Conclusion:Hearing improvement is better in patients with detectable TEOAEs and DPOAEs.As a result,TEOAEs and DPOAEs are recommended as routine tests in all SSNHL patients to predict outcomes and monitor treatment as TEOAEs and DPOAEs reflect the cochlear OHCs activity.展开更多
In this study,uniaxial and triaxial compression acoustic emission(AE)tests were implemented to investigate the AE effect and failure characteristics of sandstone under different confining pressures(σ3).The evolution ...In this study,uniaxial and triaxial compression acoustic emission(AE)tests were implemented to investigate the AE effect and failure characteristics of sandstone under different confining pressures(σ3).The evolution of AE parameters in the rock failure process and fracture fractal dimension characteristics after failure were analyzed.The results revealed that the activity of the AE signal is strongly related toσ3.The evolution of the Ib value can be divided into the I-fluctuation,II-stability,and III-decrease stages.In the first stage,the Ib value of the AE was relatively high,and the AE energy was low.Then,the Ib value tended to be stable;however,the fluctuation amplitude decreased,and the AE energy rapidly increased.In the stage of decrease,the AE energy sharply increased before the load approached the peak value,and the Ib value significantly decreased and dropped to the lowest point before the peak value.Asσ3 increased,the rock’s failure mode changed from tensile failure to shear failure and became more coordinated.As the confining pressure increased,the shape dimension decreased,and the order degree of rock failure increased.The confining pressure exerted a certain control effect on the rock failure.展开更多
To extract more in-depth information of acoustic emission(AE)signal-cloud in rock failure under triaxial compression,the spatial correlation of scattering AE events in a granite sample is effectively described by the ...To extract more in-depth information of acoustic emission(AE)signal-cloud in rock failure under triaxial compression,the spatial correlation of scattering AE events in a granite sample is effectively described by the cube-cluster model.First,the complete connection of the fracture network is regarded as a critical state.Then,according to the Hoshen-Kopelman(HK)algorithm,the real-time estimation of fracture con-nection is effectively made and a dichotomy between cube size and pore fraction is suggested to solve such a challenge of the one-to-one match between complete connection and cluster size.After,the 3D cube clusters are decomposed into orthogonal layer clusters,which are then transformed into the ellip-soid models.Correspondingly,the anisotropy evolution of fracture network could be visualized by three orthogonal ellipsoids and quantitatively described by aspect ratio.Besides,the other three quantities of centroid axis length,porosity,and fracture angle are analyzed to evaluate the evolution of cube cluster.The result shows the sample dilatancy is strongly correlated to four quantities of aspect ratio,centroid axis length,and porosity as well as fracture angle.Besides,the cube cluster model shows a potential pos-sibility to predict the evolution of fracture angle.So,the cube cluster model provides an in-depth view of spatial correlation to describe the AE signal-cloud.展开更多
The acoustic emission(AE)technique can perform non-destructive monitoring of the internal damage development of bamboo and wood materials.In this experiment,the mechanical properties of different bamboo and wood(bambo...The acoustic emission(AE)technique can perform non-destructive monitoring of the internal damage development of bamboo and wood materials.In this experiment,the mechanical properties of different bamboo and wood(bamboo scrimber,bamboo plywood and SPF(Spruce-pine-fir)dimension lumber)during four-point loading tests were compared.The AE activities caused by loadings were investigated through the single parameter analysis and K-means cluster analysis.Results showed that the bending strength of bamboo scrimber was 3.6 times that of bam-boo plywood and 2.7 times that of SPF dimension lumber,respectively.Due to the high strength and toughness of bamboo,the AE signals of the two bamboo products were more abundant than those of SPF dimension lumber.However,the AE evolution trend of the three materials was similar,which all experienced three stages,including gentle period,steady period and steep period,and the area of rupture precursor characteristics could be recognized before the specimen destroyed.Due to the bottom layer was first tensile failure,the main structure of bamboo plywood was destroyed after the stress redistribution.The rupture precursor characteristics could be observed before each peak.Findings put in evidence a good correlation between AE clusters of two bamboo products,while the amplitude and energy of wood signals were lower than those of bamboo.The amplitude and energy from the propagation and aggregation of cracks were greater than those related to micro-cracks initiation.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51934007)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20220691).
文摘Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.
基金supported by the National Natural Science Foundation of China(Grant No.52125903).
文摘Direct shear tests were conducted on sandstone specimens under different constant normal stresses to study the coalescence of cracks between non-persistent flaws and the shear sliding characteristics of the shear-formed fault.Digital image correlation and acoustic emission(AE)techniques were used to monitor the evolution of shear bands at the rock bridge area and microcracking behaviors.The experimental results revealed that the shear stresses corresponding to the peak and sub-peak in the stressdisplacement curve are significantly affected by the normal stress.Strain localization bands emerged at both the tip of joints and the rock bridge,and their extension and interaction near the peak stress caused a surge in the AE hit rate and a significant decrease in the AE b value.Short and curvilinear strain bands were detected at low normal stress,while high normal stress generally led to more microcracking events and longer coplanar cracks at the rock bridge area.Furthermore,an increase in normal stress resulted in a higher AE count rate and more energetic AE events during friction sliding along the shearformed fault.It was observed that the elastic energy released during the crack coalescence at the prepeak stage was much greater than that released during friction sliding at the post-peak stage.More than 75%of AE events were located in the low-frequency band(0e100 kHz),and this proportion continued to rise with increasing normal stress.Moreover,more AE events of low AF value and high RA value were observed in specimens subjected to high normal stress,indicating that greater normal stress led to more microcracks of shear nature.
基金Supported by projects of the National Natural Science Foundation of China(Nos.52074088,52174022,51574088,51404073)Provincial Outstanding Youth Reserve Talent Project of Northeast Petroleum University(No.SJQH202002)+1 种基金2020 Northeast Petroleum University Western Oilfield Development Special Project(No.XBYTKT202001)Postdoctoral Research Start-Up in Heilongjiang Province(Nos.LBH-Q20074,LBH-Q21086).
文摘In order to study fracture mechanism of rocks in different brittle mineral contents,this study pro-poses a method to identify the acoustic emission signal released by rock fracture under different brittle miner-al content(BMC),and then determine the content of brittle matter in rock.To understand related interference such as the noises in the acoustic emission signals released by the rock mass rupture,a 1DCNN-BLSTM network model with SE module is constructed in this study.The signal data is processed through the 1DCNN and BLSTM networks to fully extract the time-series correlation features of the signals,the non-correlated features of the local space and the weak periodicity law.Furthermore,the processed signals data is input into the fully connected layers.Finally,softmax function is used to accurately identify the acoustic emission signals released by different rocks,and then determine the content of brittle minerals contained in rocks.Through experimental comparison and analysis,1DCNN-BLSTM model embedded with SE module has good anti-noise performance,and the recognition accuracy can reach more than 90 percent,which is better than the traditional deep network models and provides a new way of thinking for rock acoustic emission re-search.
文摘Zirconia ceramics have become increasingly widely used in recent years and are favored by relevant enterprises. From the traditional dental field to aerospace, parts manufacturing has been used, but there is limited research on the deformation and damage process of zirconia ceramics. This article analyzes the acoustic emission characteristics of each stage of ceramic damage from the perspective of acoustic emission, and explores its deformation process characteristics from multiple perspectives such as time domain, frequency, and EWT modal analysis. It is concluded that zirconia ceramics exhibit higher brittleness and acoustic emission strength than alumina ceramics, and when approaching the fracture, it tends to generate lower frequency acoustic emission signals.
基金This work was financially supported by the National Key Research and Development Program of China(Grant No.2021YFC2900500)the International(Regional)Cooperation and Exchange Program of National Natural Science Foundation of China(Grant No.52161135301)the Special Fund for Basic Scientific Research Operations in Universities(Grant No.2282020cxqd055).
文摘The rock fracture characteristics and principal stress directions are crucial for prevention of geological disasters.In this study,we carried out biaxial compression tests on cubic granite samples of 100 mm in side length with different intermediate principal stress gradients in combination with acoustic emission(AE)technique.Results show that the fracture characteristics of granite samples change from‘sudden and aggregated’to‘continuous and dispersed’with the increase of the intermediate principal stress.The effect of increasing intermediate principal stress on AE amplitude is not significant,but it increases the proportions of high-frequency AE signals and shear cracks,which in turn increases the possibility of unstable rock failure.The difference of stress in different directions causes the anisotropy of rock fracture and thus leads to the obvious anisotropic characteristics of wave velocity variations.The anisotropy of wave velocity variations with stress difference is probable to identify the principal stress directions.The AE characteristics and the anisotropy of wave velocity variations of granite under two-dimensional stress are not only beneficial complements for rock fracture characteristic and principal stress direction identification,but also can provide a new analysis method for stability monitoring in practical rock engineering.
基金Major Program of Shandong Provincial Natural Science Foundation(No.ZR2019ZD13)Major Scientifc and Technological Innovation Project of Shandong Provincial Key Research Development Program(No.2019SDZY02)Project of Taishan Scholar in Shandong Province.
文摘Acoustic emission(AE)signals contain substantial information about the internal fracture characteristics of rocks and are useful for revealing the laws governing the release of energy stored therein.Reported here is the evolution of rock failure with diferent master crack types as investigated using Brazilian splitting tests(BSTs),direct shear tests(DSTs),and uniaxial compression tests(UCTs).The AE parameters and typical modes of each fracture type were obtained,and the energy release characteristics of each fracture mechanism were discussed.From the observed changes in the AE parameters,the rock fracture process exhibits characteristics of staged intensifcation.The scale and energy level of crack activity in the BSTs were signifcantly lower than those in the DSTs and UCTs.The proportion of tensile cracks in the BSTs was 65%–75%,while the proportions of shear cracks in the DSTs and UCTs were 75%–85%and 70%–75%,respectively.During the rock loading process under diferent conditions,failure was accompanied by an increased number of shear cracks.The amplitude,duration,and rise time of the AE signal from rock failure were larger when the failure was dominated by shear cracks rather than tensile ones,and most of the medium-and high-energy signals had medium to low frequencies.After calculating the proposed energy amplitude ratio,the energy release of shear cracks was found to exceed that of tensile cracks at the same fracture scale.
基金financially supported by the National Natural Science Foundation of China (No.51934003)the Major Science and Technology Special Project of Yunnan Province,China(Nos.202102AF080001 and 202102AG050024)。
文摘The anisotropy induced by rock bedding structures is usually manifested in the mechanical behaviors and failure modes of rocks.Brazilian tests are conducted for seven groups of shale specimens featuring different bedding angles. Acoustic emission (AE) and digital image correlation (DIC) technologies are used to monitor the in-situ failure of the specimens. Furthermore, the crack morphology of damaged samples is observed through scanning electron microscopy (SEM). Results reveal the structural dependence on the tensile mechanical behavior of shales. The shale disk exhibits compression in the early stage of the experiment with varying locations and durations. The location of the compression area moves downward and gradually disappears when the bedding angle increases. The macroscopic failure is well characterized by AE event location results, and the dominant frequency distribution is related to the bedding angle. The b-value is found to be stress-dependent.The crack turning angle between layers and the number of cracks crossing the bedding both increase with the bedding angle, indicating competition between crack propagations. SEM results revealed that the failure modes of the samples can be classified into three types:tensile failure along beddings with shear failure of the matrix, ladder shear failure along beddings with tensile failure of the matrix, and shear failure along multiple beddings with tensile failure of the matrix.
基金supported by the National Natural Science Foundation of China (Nos.52022107,52174128,and 52104103)the Natural Science Foundation of Jiangsu Province (Nos.BK20190031 and BK20210499)+2 种基金the“Tianshan Innovation Team Plan”Project (No.2021D14016)the Xinjiang Key Research and Development Special Project (No.2022B03028-3)the Xinjiang Central Guidance Local Fund Project。
文摘The mechanical properties of cemented paste backfill(CPB)determine its control effect on the goaf roof.In this study,the mechanical strength of polymer-modified cemented paste backfill(PCPB)samples was tested by uniaxial compression tests,and the failure characteristics of PCPB under the compression were analyzed.Besides,acoustic emission(AE)technology was used to monitor and record the cracking process of the PCPB sample with a curing age of 28 d,and two AE indexes(rise angle and average frequency)were used to classify the failure modes of samples under different loading processes.The results show that waterborne epoxy resin can significantly enhance the mechanical strength of PCPB samples(when the mass ratio of polymer to powder material is 0.30,the strength of PCPB samples with a curing age of 28 d is increased by 102.6%);with the increase of polymer content,the mechanical strength of PCPB samples is improved significantly in the early and middle period of curing.Under uniaxial load,the macro cracks of PCPB samples are mostly generated along the axial direction,the main crack runs through the sample,and a large number of small cracks are distributed around the main crack.The AE response of PCPB samples during the whole loading process can be divided into four periods:quiet period,slow growth period,rapid growth period,and remission period,corresponding to the micro-pore compaction stage,elastic deformation stage,plastic deformation stage,and failure instability stage of the stress-strain curve.The AE events are mainly concentrated in the plastic deformation stage;both shear failure and tensile failure occur in the above four stages,while tensile failure is dominant for PCPB samples.This study provides a reference for the safety of coal pillar recovery in pillar goaf.
文摘We investigate the accuracy and robustness of moment tensor(MT)and stress inversion solutions derived from acoustic emissions(AEs)during the laboratory fracturing of prismatic Barre granite specimens.Pre-cut flaws in the specimens introduce a complex stress field,resulting in a spatial and temporal variation of focal mechanisms.Specifically,we consider two experimental setups:(1)where the rock is loaded in compression to generate primarily shear-type fractures and(2)where the material is loaded in indirect tension to generate predominantly tensile-type fractures.In each test,we first decompose AE moment tensors into double-couple(DC)and non-DC terms and then derive unambiguous normal and slip vectors using k-means clustering and an unstructured damped stress inversion algorithm.We explore temporal and spatial distributions of DC and non-DC events at different loading levels.The majority of the DC and the tensile non-DC events cluster around the pre-cut flaws,where macro-cracks later develop.Results of stress inversion are verified against the stress field from finite element(FE)modeling.A good agreement is found between the experimentally derived and numerically simulated stress orientations.To the best of the authors’knowledge,this work presents the first case where stress inversion methodologies are validated by numerical simulations at laboratory scale and under highly heterogeneous stress distributions.
基金support provided by the National Natural Science Foundation of China(Grant No.11872025)and the Six Talent Peaks Project in Jiangsu Province(Grant No.2019-KTHY-059).
文摘Microcapsule self-healing technology is one of the effective methods to solve the durability problem of cementbased composites.The evaluation method of the self-healing efficiency of microcapsule self-healing cement-based composites is one of the difficulties that limits the self-healing technology.This paper attempts to characterize the self-healing efficiency of microcapsule self-healing cement-based composites by acoustic emission(AE)parameters,which provides a reference for the evaluation of microcapsule self-healing technology.Firstly,a kind of self-healing microcapsules were prepared,and the microcapsules were added into the cement-based composites to prepare the compression samples.Then,the specimen with certain pre damage was obtained by compression test.Secondly,the damaged samples were divided into two groups.One group was directly used for compression tests to obtain the damage failure process.The other group was put into water for healing for 30 days,and then compression tests were carried out to study the influence of self-healing on the compression failure process.During the experiments,the AE signals were collected and the AE characteristics were extracted for the evaluation of self-healing efficiency.The results show that the compression pre damage test can trigger the microcapsule,and the compression strength of the self-healing sample is improved.The failure mechanism of microcapsule selfhealing cement-based composites can be revealed by the AE parameters during compression,and the self-healing efficiency can be quantitatively characterized by AE hits.The research results of this paper provide experimental reference and technical support for the mechanical property test and healing efficiency evaluation of microcapsule self-healing cement-based composites.
基金funded by Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining (GJNY-20-113-03),SHGF-16-19the Fundamental Research Funds for the Central Universities (06500182)+2 种基金Funds from Joint National-Local Engineering Research Center for Safe and Precise Coal Mining (EC2021004)Funds from State Key Laboratory of Coal Resources in Western China (SKLCRKF20-07)Funds from Humboldt Research Fellowship,Funds from NSFC (52204086).
文摘The stability of coal walls(pillars)can be seriously undermined by diverse in-situ dynamic disturbances.Based on a 3D par-ticle model,this work strives to numerically replicate the major mechanical responses and acoustic emission(AE)behaviors of coal samples under multi-stage compressive cyclic loading with different loading and unloading rates,which is termed differential cyclic loading(DCL).A Weibull-distribution-based model with heterogeneous bond strengths is constructed by both considering the stress-strain relations and AE parameters.Six previously loaded samples were respectively grouped to indicate two DCL regimes,the damage mechanisms for the two groups are explicitly characterized via the time-stress-dependent variation of bond size multiplier,and it is found the two regimes correlate with distinct damage patterns,which involves the competition between stiffness hardening and softening.The numerical b-value is calculated based on the mag-nitudes of AE energy,the results show that both stress level and bond radius multiplier can impact the numerical b-value.The proposed numerical model succeeds in replicating the stress-strain relations of lab data as well as the elastic-after effect in DCL tests.The effect of damping on energy dissipation and phase shift in numerical model is summarized.
文摘Acoustic emission(AE)is a nondestructive real-time monitoring technology,which has been proven to be a valid way of monitoring dynamic damage to materials.The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning.Considering that the huge success of deep learning technologies,where the Recurrent Neural Network(RNN)has been widely applied to sequential classification tasks and Convolutional Neural Network(CNN)has been widely applied to image recognition tasks.A novel three-streams neural network(TSANN)model is proposed in this paper to deal with fault detection tasks.Based on residual connection and attention mechanism,each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient(MFCC),Tempogram,and short-time Fourier transform(STFT)spectral respectively.Experimental results show that,in comparison with traditional classification methods and single-stream CNN networks,TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%,which demonstrates the availability of the model proposed.
基金supported by the National Natural Science Foundation of China (Grant No.U61273205).
文摘Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional tests of mechanical property can hardly meet this requirement.Design/methodology/approach-In this study the acoustic emission(AE)technology is applied in the tensile tests of the gearbox housing material of an high-speed rail(HSR)train,during which the acoustic signatures are acquired for parameter analysis.Afterward,the support vector machine(SVM)classifier is introduced to identify and classify the characteristic parameters extracted,on which basis the SVM is improved and the weighted support vector machine(WSVM)method is applied to effectively reduce the misidentification of the SVM classifier.Through the study of the law of relations between the characteristic values and the tensile life,a degradation model of the gearbox housing material amid tensile is built.Findings-The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process,and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%.The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.Originality/value-The results of this study provide new concepts for the life prediction of tensile samples,and more further tests should be conducted to verify the conclusion of this research.
文摘Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.
基金The authors would like to thank the Biometrics Security Laboratory of the University of Toronto for providing the Transient Evoked Otoacoustic Emissions(TEOAE)dataset.
文摘Biometrics,which has become integrated with our daily lives,could fall prey to falsification attacks,leading to security concerns.In our paper,we use Transient Evoked Otoacoustic Emissions(TEOAE)that are generated by the human cochlea in response to an external sound stimulus,as a biometric modality.TEOAE are robust to falsification attacks,as the uniqueness of an individual’s inner ear cannot be impersonated.In this study,we use both the raw 1D TEOAE signals,as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform(CWT).We use 1D and 2D Convolutional Neural Networks(CNN)for the former and latter,respectively,to derive the feature maps.The corresponding lower-dimensional feature maps are obtained using principal component analysis,which is then used as features to build classifiers using machine learning techniques for the task of person identification.T-SNE plots of these feature maps show that they discriminate well among the subjects.Among the various architectures explored,we achieve a best-performing accuracy of 98.95%and 100%using the feature maps of the 1D-CNN and 2D-CNN,respectively,with the latter performance being an improvement over all the earlier works.This performance makes the TEOAE based person identification systems deployable in real-world situations,along with the added advantage of robustness to falsification attacks.
文摘Background:Studies on animals have demonstrated that maternal iron deficiency anaemia(IDA)could result in decreased cochlear sensory hair cells and reduced amplitudes of distortion-product otoacoustic emissions(DPOAEs)of young guinea pigs.Thus,it is essential to study the functioning of cochlear hair cells using DPOAEs in human newborn babies with maternal IDA.The current study explores maternal IDA’s effect on DPOAEs in newborn babies.Method:A total of 110 newborn babies with gestational age≥34 weeks were considered and a‘betweensubjects’design was used.The participants were divided into 3 groups-“Normal”(61 babies without maternal IDA),“Mild”(28 babies with mild maternal IDA)and“Moderate”(21 babies with moderate maternal IDA).The cord blood was collected and the DPOAEs were recorded for each baby for a range of frequencies(1 k 8 kHz)and a range of intensities(7040 dB SPL in 10 dB steps).Results:The analysis of both DP-gram and DP input-output(I/O)function showed that there was no significant difference(p>0.05)across the normal,mild,and moderate groups in the overall presence of DPOAEs as well as the amplitude across frequencies or intensities(7040 dB SPL).Also,the overall correlation of RBC indices with DPOAE amplitude across frequencies as well as the slope of the I/O function showed no relationship.Conclusion:The current study concludes that there is no effect of late-term maternal IDA on the DPOAEs of newborn babies.
文摘Objectives:This study aimed to determine the prognostic value of otoacoustic emissions(OAEs)in idiopathic sudden sensorineural hearing loss patients.Methods:The study included 30 subjects with unilateral idiopathic sudden sensorineural hearing loss(ISSNHL).Each patient was evaluated four times:at baseline and after one week,one month,and three months of treatment.During each visit,each patient was subjected to full audiological history,otoscopic examination,basic audiological evaluations,and transiently evoked and distortion product otoacoustic emission(TEOAEs&DEOAEs).Results:The hearing thresholds(frequency range 250e8000 Hz)and word recognition scores of patients with detectable TEOAEs and DPOAEs improved significantly,whereas no significant improvements were observed in those with no response.Conclusion:Hearing improvement is better in patients with detectable TEOAEs and DPOAEs.As a result,TEOAEs and DPOAEs are recommended as routine tests in all SSNHL patients to predict outcomes and monitor treatment as TEOAEs and DPOAEs reflect the cochlear OHCs activity.
基金the financial s upport from the National Natural Science Foundation of China(No.41702326)the Jiangxi Provincial Natural Science Foundation(No.20202ACB214006)+2 种基金the Innovative Experts,Long-term Program of Jiangxi Province(jxsq2018106049)the Supported by Program of Qingjiang Excellent Young Talents,Jiangxi University of Science and Technologythe Innovation Fund Designated for Graduate Students of Jiangxi Province(YC2020-S451)。
文摘In this study,uniaxial and triaxial compression acoustic emission(AE)tests were implemented to investigate the AE effect and failure characteristics of sandstone under different confining pressures(σ3).The evolution of AE parameters in the rock failure process and fracture fractal dimension characteristics after failure were analyzed.The results revealed that the activity of the AE signal is strongly related toσ3.The evolution of the Ib value can be divided into the I-fluctuation,II-stability,and III-decrease stages.In the first stage,the Ib value of the AE was relatively high,and the AE energy was low.Then,the Ib value tended to be stable;however,the fluctuation amplitude decreased,and the AE energy rapidly increased.In the stage of decrease,the AE energy sharply increased before the load approached the peak value,and the Ib value significantly decreased and dropped to the lowest point before the peak value.Asσ3 increased,the rock’s failure mode changed from tensile failure to shear failure and became more coordinated.As the confining pressure increased,the shape dimension decreased,and the order degree of rock failure increased.The confining pressure exerted a certain control effect on the rock failure.
基金This study was sponsored by the National Natural Science Foundation of China(No.51504257)the State Key Research Development Program of China(No.2016YFC0600704)+1 种基金the Fundamental Research Funds for the Central Universities(Yueqi Outstanding Scholars)(No.2018B051616,2021JCCXLJ01,2021YJSLJ06)the Open Fund of the State Key Laboratory of Coal Mine Disaster Dynamics and Control(No.2011DA105287-FW201604).
文摘To extract more in-depth information of acoustic emission(AE)signal-cloud in rock failure under triaxial compression,the spatial correlation of scattering AE events in a granite sample is effectively described by the cube-cluster model.First,the complete connection of the fracture network is regarded as a critical state.Then,according to the Hoshen-Kopelman(HK)algorithm,the real-time estimation of fracture con-nection is effectively made and a dichotomy between cube size and pore fraction is suggested to solve such a challenge of the one-to-one match between complete connection and cluster size.After,the 3D cube clusters are decomposed into orthogonal layer clusters,which are then transformed into the ellip-soid models.Correspondingly,the anisotropy evolution of fracture network could be visualized by three orthogonal ellipsoids and quantitatively described by aspect ratio.Besides,the other three quantities of centroid axis length,porosity,and fracture angle are analyzed to evaluate the evolution of cube cluster.The result shows the sample dilatancy is strongly correlated to four quantities of aspect ratio,centroid axis length,and porosity as well as fracture angle.Besides,the cube cluster model shows a potential pos-sibility to predict the evolution of fracture angle.So,the cube cluster model provides an in-depth view of spatial correlation to describe the AE signal-cloud.
基金This paper was supported in part by Project funded by the National Natural Science Foundation of China(Grant Nos.32071700 and 31570559).
文摘The acoustic emission(AE)technique can perform non-destructive monitoring of the internal damage development of bamboo and wood materials.In this experiment,the mechanical properties of different bamboo and wood(bamboo scrimber,bamboo plywood and SPF(Spruce-pine-fir)dimension lumber)during four-point loading tests were compared.The AE activities caused by loadings were investigated through the single parameter analysis and K-means cluster analysis.Results showed that the bending strength of bamboo scrimber was 3.6 times that of bam-boo plywood and 2.7 times that of SPF dimension lumber,respectively.Due to the high strength and toughness of bamboo,the AE signals of the two bamboo products were more abundant than those of SPF dimension lumber.However,the AE evolution trend of the three materials was similar,which all experienced three stages,including gentle period,steady period and steep period,and the area of rupture precursor characteristics could be recognized before the specimen destroyed.Due to the bottom layer was first tensile failure,the main structure of bamboo plywood was destroyed after the stress redistribution.The rupture precursor characteristics could be observed before each peak.Findings put in evidence a good correlation between AE clusters of two bamboo products,while the amplitude and energy of wood signals were lower than those of bamboo.The amplitude and energy from the propagation and aggregation of cracks were greater than those related to micro-cracks initiation.
