Multichannel Acoustic Emission Research Articles

Damage assessment of concrete via acoustic emission and mesoscopic damage constitutive model

This paper experimentally investigated the acoustic emission (AE) behavior of concrete specimens. By using the MTS810 servo hydraulic testing system and the multi-channel AE system, a series of specimens graded C40, C50, and C60 were loaded under uniaxial compression. The spatial distribution of the AE events, AE events rate, and AE energy rate were recorded. Additionally, a theoretical mesoscopic damage constitutive (MDC) model, which defines the theoretical mesoscopic damage variable as the ratio of the fractured micro-elements to the total micro-elements, was introduced. The results indicated that the spatial distribution of AE events resembled the shape of failure modes. Moreover, the theoretical mesoscopic damage calculated by the MDC model matched well with the observed experimental damage, represented by the AE events. This association not only related the theoretical mesoscopic damage with experimental macroscopic damage, but also revealed that the macroscopic damage of concrete stemmed from mesoscopic fracture. Additionally, the AE energy was related both to the energy of fractured micro-elements and its derivative in the MDC model.

−252.8 °C Liquid Hydrogen Acoustic Emission Experiment in Simulated Aerospace Fuel Tank

With the increase of space activities, the safety detection of equipment in supercold environment becomes more and more important. However, the safety detection of equipment in liquid hydrogen environment is an unsolved worldwide problem. This paper presents a new method to detect acoustic emission (AE) signals in liquid hydrogen environment, which can be used to confirm the safety and reliability of the equipment working in liquid hydrogen. First, the principle of mandrel-based fiber-optic ring (MBFOR) AE sensor and the corresponding demodulation method are introduced. Second, the effects of physical parameters such as the length of the sensing fiber, the diameter and material of the mandrel, and the number of fiber winding layers on the sensor's sensitivity are studied by experiment. Finally, the AE source localization accuracy test of the FOR AE detection system at low temperatures and the adaptability test of the proposed method in -252.8 °C liquid hydrogen are carried out. The results show that the proposed MBFOR sensor and the multi-channel AE detection system can complete the defects detection of aerospace pressure vessel in liquid hydrogen. Certainly, it can be used for safety detection for other spacecraft equipment in harsh environments.

Multivariate variational mode decomposition and generalized composite multiscale permutation entropy for multichannel fault diagnosis of hoisting machinery system

Due to the harsh working environment of hoisting machinery system, the fault information of the important components is significantly complex, which leads to the fault signals not being collected completely by using only single channel. To alleviate this problem, acoustic emission (AE) experiments are applied to collect multichannel AE signal of hoisting machinery system. Additionally, a new intelligent fault diagnosis method based on multivariate variational mode decomposition (MVMD) and generalized composite multiscale permutation entropy (GCMPE) is proposed to extract multichannel AE fault features and implement multichannel fault diagnosis of hoisting machinery system. Firstly, based on variational mode decomposition (VMD) and the idea of multichannel AE data processing, MVMD is proposed to process the original multichannel AE signals collected from hoisting machinery system, which can obtain adaptively several multichannel modal components containing discriminative information. Meanwhile, GCMPE is presented to extract the fault information of multichannel modal components obtained by MVMD, which can improve the feature extraction performance of the original multiscale permutation entropy. The experimental results demonstrate the effectiveness and superiority of the proposed method in multichannel fault diagnosis of hoisting machinery system compared with some traditional single-channel analysis and other multichannel analysis methods.

An effective method for laboratory acoustic emission detection and location using template matching

In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission (AE) monitoring. First, we used traditional methods to detect P-wave first motions and locate AE hypocenters in three dimensions. In addition, we selected events located with sufficient accuracy (normally corresponding AE events of relatively larger energy, showing clear P-wave first motion and a higher signal-to-noise ratio in most channels) as template events. Then, the template events were used to scan and match other poorly located events in triggered event records or weak events in continuous records. Through cross-correlation of the multi-channel waveforms between the template and the event to be detected, the weak signal was detected and located using a grid-searching algorithm (with the grid centered at the template hypocenter). In order to examine the performance of the approach, we calibrated the proposed method using experimental data of different rocks and different types of experiments. The results show that the proposed method can significantly improve the detection capability and location accuracy, and can be applied to various laboratory and in situ experiments, which use multi-channel AE monitoring with waveforms recorded in either triggering or continuous mode.

Strain rate effects on characteristic stresses and acoustic emission properties of granite under quasi-static compression

In order to further investigate the strain rate effects on characteristic stresses and acoustic emission parameters of rock under quasi-static compression, uniaxial compressive tests were conducted on cylindrical specimens measuring 50 mm in diameter and 100 mm in height using a rock material testing machine and a multi-channel acoustic emission monitoring system at strain rates ranging from 10–6 s−1 to 10–2 s−1. The stress-strain curves of rock samples, characteristic stresses, energy data, and temporal and spatial distribution of acoustic emission signals were obtained and analyzed. The experimental results certified a linearly positive correlation between characteristic stresses and the logarithm of strain rates, despite the fact that the linear correlation varies for different characteristic stresses, whereas the ratios of characteristic stresses essentially do not change with increasing strain rates. The input energy and elastic strain energy at the damage point, UCS point and failure stress point exhibit a linearly positive correlation with the logarithm of strain rates when the strain rate exceeds 10–5 s−1. Meanwhile, the characteristics of energy conversion between input energy and elastic strain energy or the dissipated energy at different characteristic stresses points were explored. Based on this, the energy conversion process of rock under quasi-static compression can be divided into three stages: energy accumulation, energy dissipation, and energy release, respectively. Besides, it is noted that the total number of the located AE events decreases as strain rates increase when the strain rate exceeds 10–5 s−1, and the majority of located AE events occur during the crack closure stage and unstable crack growth stage. Finally, based on the perspective of energy conversion and the structural properties of multi-scale defects in rock, the mechanism of the increase of characteristic stresses with the increase of strain rates was proposed: that is, when rock is subjected to quasi-static compression, the higher strain rates can activate the small-scale defects, which necessitates more input energy from the external load via continuous work and causes an increase in the associated characteristic stresses.

Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

Nowadays, the feature extraction method of multichannel acoustic emission (AE) signal provides a solid research foundation for digital and intelligent fault diagnosis of the hoisting system. More specifically, AE signal collected from the hoisting system is generally characterized by nonlinear and non-stationary, thus making the traditional intelligent fault diagnosis methods cannot accurately extract the inherent fault features. To alleviate this problem and improve the accuracy of multichannel fault diagnosis, a new fault diagnosis method for hoisting system based on differential search algorithm-variational mode decomposition (DSA-VMD) and improved deep convolutional neural network (IDCNN) is proposed in this paper. Specifically, the proposed DSA-VMD and IDCNN method is divided into two main components: (i) the inside parameters (K, a) of VMD is optimized to effectively extract the multichannel AE fault feature via DSA-VMD and (ii) the extracted multichannel fault components are fed into the designed IDCNN algorithm to accomplish fault identification automatically. Experimental results from the hoisting system demonstrate the effectiveness of the proposed approach. Additionally, the superiority of the proposed approach has also been verified in extracting fault information and fault identification compared to the other multichannel fault diagnosis methods.

Deep learning-based monitoring of surface residual stress and efficient sensing of AE for laser shock peening

• A multi-channel acoustic emission real-time efficient signal detection method and acquisition system. • A novel spatio-temporal complementary model (CNN-LSTM) for fusion of time-varying AE signal. • Dual circulation LSTM explains the decay mechanism of AE using temporal sampling points. • Stronger correlation between information from AE attenuation profile and surface quality. Laser shock peening (LSP) is one of the main anti-fatigue technologies in high-end manufacturing industries. However, guaranteeing its quality consistency is difficult due to its process complexity. Acoustic emission (AE) is a promising solution to accurately monitoring the surface quality of LSP, although, the large scale of monitoring data is still challenging for industrial applications. This paper studied the in-situ evaluation of surface residual compressive stress (SRCS) for 7075 aluminum alloy in LSP and the efficient sensing of AE based on deep learning methods. Firstly, a monitoring system of LSP with four types of AE sensors was developed in order to simultaneously acquire both the ultra-high and ultra-low amplitude of shock wave. The performance of those sensors is further quantitatively evaluated via long-short term memory (LSTM). Then, a new spatio-temporal parallel CNN-LSTM model for SRCS classification was proposed and experimentally validated to outperform CNN and LSTM after parameters optimization. Finally, aiming to efficient sensing of AE, the importance of different frames of AE signal was analyzed by means of the proposed dual circulation LSTM (DC-LSTM) model, which can accurately locate the key frame of time series signal. Besides, the effects of different frames of AE on classification performance was discussed. It was found that the broadband AE sensor without attenuator shows the highest classification accuracy, while the fast decay stage of AE signal has more contribution to the SRCS classification. This paper provides guidance for real-time non-destructive evaluation of residual stress via AE in laser manufacturing.

A machine-learning-based method of detecting and picking the first P-wave arrivals of acoustic emission events in laboratory experiments

SUMMARY Detecting and picking the first P-wave arrivals of seismic events in seismograms is fundamental in observational seismology. Recently, several machine-learning-based algorithms have been developed to incorporate human expertise for picking P-wave arrival times automatically. One shortcoming of these models is that they pick arrival times at individual seismic stations separately, which need to be sorted and associated to identify the seismic event. Also, most of them rely on existence of P-wave arrivals in the seismograms to be picked. Here, we developed a machine-learning-based seismic event detection and P-wave arrival time picking method called MultiNet and applied it to acoustic emission (AE) waveform data recorded in laboratory experiments. The MultiNet uses 2-D waveform images from multichannel AE recordings as the input to a convolutional neural network (CNN) to detect whether there is an AE event in an image and, if so, uses a fully convolutional neural network (FCN) to pick the P-wave arrival time at each channel in the image. We tested the MultiNet using 550 known AE events recorded during syn-deformational phase transformation from olivine to spinel in Mg2GeO4 (an analogue to Mg2SiO4) in a high-pressure experiment. Waveform data of 50 events were used to train the neural networks and the rest of data were used to validate the method. At the optimal image length and detection threshold, the CNN was able to detect all 500 known events plus 48 more events missed previously. Overall, 98.7 per cent of P-wave arrival times picked by the FCN were within 0.5 $\mu$s from the manually picked times. The average picking errors at different channels range from 0.01 ± 0.05 to −0.06 ± 0.22 $\mu$s. Our method greatly reduces the amount of human labour in picking P-wave arrival times for event location and source moment tensor inversion. It can be easily adapted to process continuous waveform data of a seismic network for earthquake detection and location in real time.

Experimental investigation on fracture behaviors and acoustic emission characteristics of sandstone under different strain rates

To further investigate differences of fracture behaviors and acoustic emission characteristics of rock under different strain rates, unconfined uniaxial compressive tests of thirty cylindrical specimens with 50 mm in the diameter and 100 mm in the height were performed within quasi-static strain rates of 10–5–10–2 s−1 by rock material testing system and multi-channel acoustic emission (AE) monitoring system. The uniaxial compressive strength, peak strain and elasticity modulus, failure mode, fractal dimension and equivalent average size of the rock fragments, and the three-dimensional spatial distribution of acoustic emission events were obtained and analyzed. The experimental results verify that the uniaxial compressive strength, elasticity modulus and peak strain are strain rate-dependent significantly. Particularly, with increasing strain rates, macroscopic failure modes present three types of single shear failure, multiple shear failure, and shear and tensile mixed failure, and the rock fragments show a smaller equivalent size and a larger fractal dimension, which resulted in a higher fragmentation degree. Besides, it is noted that the evolution process of acoustic emission ring-down count and spatial distribution of events under different strain rates are closely related to macroscopic failure modes. Furthermore, characteristic stresses based on the evolution process of acoustic emission ring-down count, such as the elastic stress, initiation stress and yield stress, were analyzed deeply at different strain rates. A simplified rock model consisting of discrete fracture network and equivalent elementary volume was proposed to discuss the effect of strain rate on energy accumulation and dissipation. Finally, to better reveal the micro mechanism of the effect of strain rates on rock mechanical behaviors, the microscopic fracture morphology was observed and characterized by means of scanning electron microscopy. The experimental results should be taken into account in underground mining, tunnel excavation and other rock engineering activities.

Seismic b-Value for Foreshock AE Events Preceding Repeated Stick-Slips of Pre-Cut Faults in Granite

In this study, the b-values for acoustic emission (AE) events during stick-slip cycles of pre-cut faults in granite (as an analogue of unfavorably oriented immature faults) under triaxial compression (confining pressure: 40 MPa) are investigated. Using a multi-channel AE waveform recording system and two peak detectors, we recorded AE waveforms at 16 bits and at a sampling rate of 25 MHz, as well as the maximum amplitude of AE events with a dynamic range of 55 dB. For stick-slip events, the b-value decreases from 1.2 to 1.5 to approximately 0.6 as the shear stress increases, and then quickly jumps back to 1.0 to 1.3 immediately prior to the dynamic stress drop. The minimum b-value coincides with the maximum event rate and a stress level of 70 to 95% of the shear strength. It is also observed that the AE activity during each cycle was linked with the pre-failure fault slip, which accounts for 30% of the dynamic slip. Our results on b-value evaluation preceding repeated stick-slips can be used as an indicator of the degree of fault maturity and shear stress acting on the fault, which is important in seismic hazard assessment and earthquake prediction, especially for the injection-induced seismicity for fields in which reactivated shear rupture of unfavorable and immature faults or tensile fractures is important.

A laboratory acoustic emission experiment and numerical simulation of rock fracture driven by a high-pressure fluid source

In order to improve our understanding of rock fracture and fault instability driven by high-pressure fluid sources, the authors carried out rock fracture tests using granite under a confining pressure of 80 MPa with fluid injection in the laboratory. Furthermore, we tested a number of numerical models using the FLAC3D modeling software to find the best model to represent the experimental results. The high-speed multichannel acoustic emission (AE) waveform recording system used in this study made it possible to examine the total fracture process through detailed monitoring of AE hypocenters and seismic velocity. The experimental results show that injecting high-pressure oil into the rock sample can induce AE activity at very low stress levels and can dramatically reduce the strength of the rock. The results of the numerical simulations show that major experimental results, including the strength, the temporal and spatial patterns of the AE events, and the role of the fluid can be represented fairly well by a model involving (1) randomly distributed defect elements to model pre-existing cracks, (2) random modification of rock properties to represent inhomogeneity introduced by different mineral grains, and (3) macroscopic inhomogeneity. Our study, which incorporates laboratory experiments and numerical simulations, indicates that such an approach is helpful in finding a better model not only for simulating experimental results but also for upscaling purposes.

Multi-sensor couplers and waveguides for efficient detection of acoustic emission behavior of snow

The efficient detection of acoustic emission (AE) activity from snow having porous and fragile character is hindered by the fact that the AE signals have small amplitudes and are typically attenuated within a short distance from the source. We therefore tested seven different types of highly sensitive resonant AE sensors and a multi-channel AE system in a wide frequency range of 1kHz–400kHz to evaluate the performance of multi-sensor coupler and waveguides with varying displacement rates. The AE generated during small fracturing of a natural snowpack, caused by a ram penetrometer, were detected using a cylindrical waveguide and a detection range of up to 16m with detection efficiency of 38dBAE were observed in snow. The AE activities produced by the snowpack were continuously recorded using a 2D-arrestor for AE in relation with the natural melt-freeze process. Prominent AE activity was observed near the phase-transition temperature of snow. Furthermore, spectral analysis of the AE signals generated by snow was carried out using the Short-time Fast Fourier Transform (ST-FFT) method. The AE behavior of snow was observed at different states of stress by varying from low to high, corresponding to different frequency ranges and grain sizes and an empirical relation was established for peak AE rates as a function of displacement rate. Apart from it, an attempt was made to monitor the real time failure of snow sample vis-à-vis AE energy with the help of an AE system and a high-speed camera. Our results are quite encouraging towards application of AE technique in the direction of slope stability evaluation and AE-based non-destructive evaluation of snow under various physical processes. The network of acoustic arrestors and waveguides can be crucial towards prediction of slope stability in view of avalanche release.

In situ monitoring of cracking behaviors of plasma-sprayed coatings by the laser acoustic emission technique

Multichannel acoustic emission (AE) measurements by four laser interferometers were developed and applied during a plasma-spray coating process that is known as being a high-temperature process and an extremely noisy environment in both mechanical and electrical domains. The AE signals could be successfully detected during the cooling period after the deposition, and it was clearly indicated that a higher preheating temperature resulted in the improvement of bonding between splats and/or at the interface of the coating and the substrate. The maximum principal stress generated during the deposition process was calculated by the transient heat and stress analysis and the obtained AE events. The critical stress for crack initiation of the alumina coating on a steel substrate with an NiCr bond coat layer was estimated as 30∼45 MPa. The developed techniques were shown to be a potential tool for in situ monitoring of the thermal spray process, by which an increase in the reliability of thermal spray coatings can be expected.

Acoustic emission and disturbances in central solenoid model coil for International Thermonuclear Experimental Reactor

This paper presents acoustic emission (AE) signals induced from the Central Solenoid (CS) model coil of the International Thermonuclear Experimental Reactor (ITER) program. Envelopes of the AE signals were quasi-continuously measured with high-time resolution of 100 μs using six multi-channel AE sensors in order to detect the disturbances in the CS model coil during the process of the series of direct current (DC) operations. The AE signals were considered to be originally induced by motion of superconducting cables in cable-in-conduit (CIC) conductors and local motion of the conductors, judging from the close correlation between the AE signals and voltage spikes in the coil, especially during the virgin current operation. The multi-channel measurements provide us with information about the distribution of disturbances that could be detected acoustically by the AE sensors installed at each point of the CS model coil. The observation of AE envelopes with high-time resolution showed that the disturbances at each location of the CS model coil decreased by repeatedly charging-up the coil, judging from instantaneous AE levels, AE energy and the number of AE events. Direct measurements of the number of AE events that were carried out at another point on the coil confirmed that the disturbance dependence on the number of operations was similar to that mentioned above. The transfer function methods using one pair of AE sensors enabled us to analyze changes that might occur in either the coil structure or in the disturbance in frequency region during the repeating of the charging-up processes of the coil.

Distributed multichannel acoustic-emission system equipped with microprocessors for strength testing of long objects

The paper analyzes the structure of a distributed multichannel microprocessor acoustic-emission (AE) facility for diagnosing metallic tested objects of large lengths. Each unit of the distributed AE facility is housed in a small case and equipped with a built-in simulator of an AE signal, which enables one to tune up in the automatic mode acoustic emission transducers and measure the propagation velocity of acoustic signals. The measuring channels are connected to a computer using the Ethernet technique. The paper describes a technique for estimating remaining service lives taking as examples samples of aircraft materials and compares calculations to experimental data.

ＣＦ／ＰＥＥＫ材とＣＦ／エポキシ材を用いた補強平板構造の衝撃後残留圧縮強度

Compression after impact (CAI) tests are conducted for stiffened panels made of carbon fiber (CF)/epoxy and CF/PEEK composite systems. Delaminations between skin and stiffeners are created by impact with various energy levels. Relationships between normalized impact energy levels with respect to thickness and delamination area are obtained through a robotic ultrasonic C-scan system. Compression tests are conducted with the aid of moire-topography camera and multi-channel acoustic emission analyzer. CAI strength of a CF/PEEK panel is superior to those of CF/Epoxy panels. In the CF/PEEK panels, delamination propagation is well arrested just by the instance of catastrophic failure, and hence, high CAI strengths are obtained. In the CF/epoxy panels with a current skin of low axial stiffness, delamination propagation between skin and stiffeners is well arrested after initial separation onset. Hence, their CAI strengths are higher than those of thick plates where delaminations easily propagates transversely to loads. Initial buckling stresses are numerically obtained through a FEM code. Some factors are crucial for accurate predictions. Stress dependency of longitudinal elastic modulus of unidirectional CFRP is the most important point. For relatively short panels used here, delicate consideration of loading edge boundary conditions is required. Consideration of realistic initial imperfection slightly improves correlation between prediction and experiments.

沿層掘進におけるAE活動の計測

The acoustic emission activity from coal gas outburst was monitored during an entry drivage by blasting against the 11th coal seam in SUNAGAWA Coal Mine. In this observation, the source location analysis was conducted to determine the location of the acoustic emission activity which occurred prior to the coal and gas outburst using a multi-channel acoustic emission monitoring system. Moreover, themethane gas concentration in the driving face were measured successively after blasting.As the observational results, both acoustic emissions and gas emission after blasting increased and decreased remarkably in the approaching process of an entry driving face to outburst-prone fault zone, as well as the cases of cross-measure drivages presented in the first report of this study. Additionally, the continuous AE waveforms similar to the waveforms of gas outbursts were detected immediately after each blasting prior to the coal and gas outbursts. On the other hand, from the source location analysis and the frequency distribution of acoustic emission events against median frequency, it was revealed that the acoustic emission activity occurred locally ahead of the driving face. Therefore, these flactuations in acoustic emission activity and gas emission after blasting in the approaching process of driving face to outburst-prone area seem to be related to a local fracturing activity in the gas outburst source ahead of the driving face.

Characteristics of the operation of multichannel acoustic emission monitoring systems

Characteristics of the operation of multichannel acoustic emission monitoring systems

Effect of crack size on acoustic emission related to moving dislocations

Acoustic emission from a crack vicinity in a uniformly stressed solid is correlated with the crack size based on a continuous theory of dislocations. It is assumed that the total number of acoustic emissions is directly proportional to the total number of nonuniformly distributed moving dislocations in the plastic zone near the crack. The resultant equation of the relationship between acoustic emission and crack size can be expanded into a power series of applied stress. The first-order approximation is consistent with that of a macroscopic fracture mechanics consideration on the emission related to the plastic zone size. The theoretical calculation is verified with experimental results obtained from a flawed carbon steel vessel by a multichannel acoustic emission monitoring system and agrees reasonably well with the experimental data. Further calculations by utilizing the theory and by analyzing available experimental data of acoustic emissions from center-surface-flaw specimens and wedge opening load specimens of various crack sizes also show agreement between the theory and the experimental data.

Effect of crack size on acoustic emission

Acoustic emissions from the vicinities of cracks were monitored by a multichannel acoustic emission system, The experiment was performed on a flawed carbon steel vessel under hydraulic pressure providing a nearly uniform external stress in the vessel wall, However, the stress fields near the cracks were nonuniform. The numbers of acoustic wave packets generated from the individual cracked regions were analyzed by an on-line minicomputer, The correlation between the number of the wave packets and the size of crack at low stress levels was found experimentally and is consistent with a theory of non-uniformly distributed moving dislocations in the vicinity of crack, Further calculations by utilizing the theory and by analyzing available experimental data of acoustic emissions from other types of flawed specimens of various crack sizes also show agreement between the theory and the experimental data.