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Identifying precursor information to rock failure is key to rock dynamic disaster warning. To solve the problem that a single in-situ monitoring method cannot accurately identify the precursors to rock failure, this paper combined a digital image correlation method, thermal infrared radiation and acoustic emission technology to conduct a multi-physical field joint monitoring experiment of granite under three-point bending. The information about strain, thermal infrared and acoustic emission of the rock brittle failure process could be obtained, and the variations in multi-physical field indices of the rock brittle failure process were analysed. Based on the theory of critical slowing-down, the study of the multi-physical field effects of critical slowing-down characteristics in the rock failure process, and further exploration of the precursor characteristics of the brittle failure of rock were undertaken. The results indicate that the strain, infrared, and acoustic emission parameters show sudden changes during brittle failure of granite. The lag step size has little effect on the variance of changes in multi-physical field parameters and exerts a similar influence on the changes in the autocorrelation coefficient curve. The degree of influence of the window length on the variance and autocorrelation coefficient curves of multi-physical field parameters is as follows: infrared field > strain field > acoustic emission field. Compared with the autocorrelation coefficient, the variance parameter is more universal and is better when the critical slowing-down theory is used to investigate the characteristics of multi-physical fields. The four parameters of the strain field variance, infrared field variance, the variance and autocorrelation coefficient for acoustic emission field increase sharply before fracture, representing precursor information to the brittle failure of granite. The four parameters can be used as disaster warning indicators in rock. Multi-mode joint monitoring and a comprehensive analysis of multi-physical information are beneficial to accurately determine the precursor points of rock failure disaster. [ABSTRACT FROM AUTHOR]

Partial Discharges (PDs) are a significant factor in reducing the insulation life of traction transformers. In recent years, the Acoustic Emission (AE) method has become the most advanced method for detecting PD signals in transformers. The AE method utilizes AE sensors placed on the transformer tank to detect ultrasonic signals emitted by PD and determine the Time Of Arrival (TOA) of the head wave. The windings and cores of a traction transformer consist mainly of metal, which greatly affects the propagation of PD ultrasonic waves. This paper establishes a 110 kV "pressure acoustic, transient" physical field model of the traction transformer with dimensions of 4.63 × 1.48 × 2.84 m3. The model is used to carry out the PD pressure acoustic physical field simulation study of the traction transformer, to clarify the physical characteristics of the ultrasound of the PD defects, and to establish observation points on the transformer tanks to receive ultrasonic time-domain waveforms for PD detection. The simulation results indicate that PD ultrasonic waves exhibit complex propagation characteristics, including reflection, refraction, and reverberation, as they pass through the windings and cores to the observation points. The TOA of the head wave in the ultrasound time-domain waveform is indicated by the first maximum value of the wave crest line. Finally, this paper proposes a multi-level localization method based on the AE method to determine which winding generates the PD in the large-scale traction transformer using only four dynamically moving observation points. [ABSTRACT FROM AUTHOR]

This research aimed to investigate the accuracy of picking of P-wave arrival times in rock fracture acoustic emission signals. In order to simulate the mining scenario, Gaussian white noise and pulse noise were added to the data collected in the laboratory. Complete ensemble empirical mode decomposition with adaptive noise + Wavelet (CEEMDAN + Wavelet) was improved in this paper, where the Spearman rank correlation coefficient was adopted to effectively select intrinsic mode functions for denoising which retained the inherent characteristics of the rock fracture signal. The absolute amplitude and energy change rate of the envelope signal, calculated based on the Hilbert transform, were used as the input of the short term average/long term average (STA/LTA) normalization algorithm to pickup the P-wave arrival time. The reliability of this method was tested on 30 groups of recorded rock fracture laboratory data and 60 groups of added noise data. Taking the manual pickup results as the standard, the errors of CEEMDAN + Wavelet + STA/LTA + AIC (Akaike information criterion) method with the absolute amplitude of the signal as the input are all within 10 ms, and 86.67% of the results are within 5 ms. The method proposed in this paper effectively addressing the issue of false pickup caused by the sensitivity of AIC and traditional STA/LTA method for strong noise, and achieving relatively high accuracy and stability in processing low signal-to-noise ratio signals. This work contributes to monitor microscopic changes in rock bodies and is of great significance for the prediction and monitoring of geological disasters. [ABSTRACT FROM AUTHOR]

Bolted joints are widely used in various engineering structures. The bolted joint may become loosening under long-term oscillating load, and cause reciprocating friction between the contact interfaces. Acoustic emission (AE) is a phenomenon of rapid release of transient elastic waves, which will be generated continuously during the reciprocating friction. The friction conditions will affect the characteristics of the contact interface and the related AE signals. Therefore, analyzing the quantitative relationship between friction conditions and AE signal is significant for applying AE technique in bolt loosening monitoring. However, researches on the relationship between AE Vrms and friction operating parameters are mostly focused on rotating machineries currently, whether the obtained conclusions are suitable for bolted joints is not clear. This paper finds that the commonly used quantitative relationship between the AE Vrms and friction operating parameters is not suitable for bolted joint structures by comparing the theoretical and experimental AE Vrms. The relationship is then modified through exponential parameter fitting and verified using the same experiments in this paper. The results show that the modified equation can accurately describe the quantitative relationship between the AE Vrms and the friction operating parameters, thus revealing the mechanism of acoustic emission signals generated during the gross-slip. [ABSTRACT FROM AUTHOR]

Deep rock are often in a true triaxial stress state. Studying the impacts of varying unloading speeds on their strain energy (SE) density is highly significant for predicting rock stability. Through true triaxial unloading principal stress experiments and true triaxial stress equilibrium unloading experiments on sandstone, this paper proposes a method to compute the SE density in a true triaxial compressive unloading principal stress test. This method aims to analyze the SE variation in rocks under the action of true triaxial unloading principal stresses. Acoustic emission is used to verify the correctness of the SE density calculation method in this paper. This study found that: (1) Unloading in one principal stress direction causes the SE density to rise in the other principal stress directions. This rise in SE, depending on its reversibility, can be categorized into elastic and dissipated SE. (2)When unloading principal stresses, the released elastic SE density in the unloading direction is influence by the stress path and rate. (3) The higher the unloading speed will leads to greater increases in the input SE density, elastic SE density, and dissipative SE density in the other principal stress directions. (4) The dissipated SE generated under true triaxial compression by unloading the principal stress is positively correlated with the damage to the rock; with an increase in unloading rate, there is a corresponding increase in the formation of cracks after unloading. (5) Utilizing the stress balance unloading test, we propose a calculation method for SE density in true triaxial unloading principal stress tests. [ABSTRACT FROM AUTHOR]

Purpose: Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments. Design/methodology/approach: This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future. Findings: Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential. Originality/value: In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures. [ABSTRACT FROM AUTHOR]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

Monitoring the condition of the grinding wheel in real-time during the grinding process is crucial as it directly impacts the precision and quality of the workpiece. Deep learning technology plays a vital role in analyzing the changes in sensor signals and identifying grinding wheel wear during the grinding process. Therefore, this paper innovatively proposes a grinding wheel wear recognition method based on Variational Mode Decomposition (VMD) denoising and Aquila Optimizer—Convolutional Neural Network—Long Short-Term Memory (AO-CNN-LSTM). The paper utilizes Acoustic Emission (AE) signals generated during grinding to identify the condition of the grinding wheel. To address noise interference, the study introduces the VMD algorithm for denoising the sample dataset, enhancing the effectiveness of neural network training. Subsequently, the dataset is fed into the designed Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) structure with AO-optimized parameters. Experimental results demonstrate that this method achieves high accuracy and performance. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the memory of Dr. Miklós Iványi, who instilled in the authors an appreciation for experimental investigations, which are foundational to understanding material and structural behavior. Timber-concrete composite structures are increasingly adopted for new buildings due to their favorable sustainability parameters and the increased availability of cross laminated timber. For larger spans, however, solid timber floors lead to higher timber volumes and the use of glulam beams may become necessary for a more efficient use of wood. This paper presents laboratory tests of glulam-concrete composite beams and is the first in a series of two papers on investigating the associated failure mechanisms. Three full-scale glulam-concrete beam specimens were studied. The glulam and concrete are monolithically interconnected using a continuous layer of adhesive. Shear reinforcement was added to the glulam beams to allow for failure mode control. Static load tests to failure were conducted along with acoustic emission monitoring to track the progression of the failure. The results indicate that the shear reinforcement of the glulam layer affects the load capacity of the composite beam through shifting the failure from a shear to a tension failure mode. Similar glulam-concrete beams can enable larger span applications for buildings and bridges while maintaining an attractive sustainability performance. [ABSTRACT FROM AUTHOR]

Using the example of assessing the technical condition of the trunnions of drying cylinders in cardboard machines (CBM), the question of the possibility of ranking cyclically loaded elements of dynamic equipment according to the degree of damage to their material by fatigue cracks using acoustic emission (AE) measurements is discussed. As a result of special laboratory studies with varying loads in the cycle of loading and lubrication of crack edges, the features of AE due to the friction of crack edges and plastic deformation at the crack tip during its growth in a viscous material have been established. It is shown that during cyclic loading of the material, AE signals from the friction of its banks are detected more steadily than AE signals due to a crack jump with an increase in its length, and that tracking the former ensures the detection of fatigue damage in the material even under loading conditions insufficient for crack growth. Based on the data obtained, three AE signs of the presence of fatigue damage in the material of the elements of dynamic equipment were developed under the condition of cyclic activation of the movement of crack edges. As a result of industrial testing of the developed AE signs, their operability was confirmed, their boundary values on the operating trunnions of CBM drying cylinders were clarified, and a method for ranking trunnions by the level of damage due to fatigue cracks was proposed. By comparing the results of AE measurements with ultrasound testing, the reliability of the proposed approach was evaluated and showed the probability of detecting a fatigue crack in the trunnions of CBM drying cylinders at the level of 71%, with the probability of skipping and false rejection of the product of 12 and 17%, respectively. The developed technique is also transferable to other massive dynamic equipment after clarifying the boundary values of AE signs of fatigue damage in the material on this type of facilities. [ABSTRACT FROM AUTHOR]

Supporting and controlling rockburst hazards is a great challenge for deep rock engineering. A new cable, the negative Poisson's ratio (NPR) cable, exhibits excellent mechanical properties, including high energy absorption and resilience to impacts. To investigate its control effect on rockburst, a series of true triaxial strainburst experiments were conducted on sandstone specimens under different anchoring conditions (no support, ordinary support, NPR cable support). The model NPR cables were developed based on similar principles. In addition, the acoustic emission response and fragmentation characteristics were analysed. The experimental results indicate that the NPR cables are capable of co-deforming with the rock and absorbing energy, increasing rock strength and promoting internal rock tensile fracture. The rapid response of the NPR cables during rockbursts provided effective support, exerted better impact resistance and significantly reduced the mass and fractal dimension of the ejected fragments. Thus, NPR cables can reduce kinetic energy release and effectively control rockburst hazards compared to ordinary cables. The aim of this paper is to provide a reference for the design of rockburst support in engineering. [ABSTRACT FROM AUTHOR]

A novel machine learning-based model is introduced in this research paper to forecast tool wear using acoustic emission (AE) signals. Adaptive boosting (AdaBoost) and a sophisticated feature engineering strategy are employed by the model to enhance the precision of its predictions. The proposed model, Machine Learning Tool Wear Prediction (MLTWP), analyzes AE signals generated during machining operations to distinguish between healthy and worn-out tool conditions with remarkable accuracy. The crux of our approach consists of meticulously eliminating and enhancing the temporal and spectral characteristics of the AE signals. We employ the Kolmogorov-Smirnov test to identify the most valuable classification features. We implemented AdaBoost with the objective of progressively enhancing a set of weak classifiers' ability to identify instances that were incorrectly classified in previous iterations. Utilizing this method increases the model's sensitivity to minute variations in tool wear conditions and its overall classification precision. The MLTWP model underwent extensive testing on a benchmark data set comprising 25,304 AE signal records from cutting mill tools, using a training tool split of 9,989 worn (positive) and 8,990 benign (negative) instances. The results of our experiments, validated through four-fold cross-validation, indicate that the MLTWP model exhibits superior performance compared to the existing Tool Wear Prediction using Acoustic Emission Signals (TWPAE) model. To provide greater specificity, the MLTWP exhibited the following metrics on average: precision (92.2%), specificity (91.38%), sensitivity (90.42%), accuracy (90.9%), and MCC (81.72%). The fact that these metrics exhibit significant improvement over those of the TWPAE model demonstrates that our method of feature engineering and adaptive boosting is effective at precisely predicting tool wear. This research not only advances the existing understanding of tool wear prediction but also establishes a robust framework for the implementation of machine learning in manufacturing predictive maintenance. [ABSTRACT FROM AUTHOR]

The wear state of mechanical seal friction pair directly determines the reliability of mechanical seal. In this paper, the mapping mechanism between contact wear response and acoustic emission (AE) signals of friction pair is indicatively proposed, and the relationship between wear frequency and time-averaged wear is explored. First of all, AE sensors were arranged on the Multi-function tribometer Rtec MFT-5000, static and dynamic friction tests were carried out on the contact form of M106K-WC (graphite-cemented carbide) and WC–WC, the AE signals are collected, and the wear amounts of the two groups of friction pairs were measured; then, the friction and wear signals are separated and reprocessed by time–frequency analysis. The results show that the static wear response frequency (SWRF) of M106K-WC is about 70 ± 10 kHz, the SWRF of WC–WC is about 90 ± 10 kHz, and the dynamic wear response frequency (DWRF) of WC–WC is about 175 ± 10 kHz; the root mean square (RMS) values of DWRF amplitudes is positively correlated with the wear amounts. According to the research results, it is inferred that there is a difference between the signal frequency in the quasi-static wear process and the dynamic wear process, there is a great correlation between the wear frequency and the material pair, and the working condition has little influence on the wear frequency. The mapping relationship between AE signal and time-averaged wear of friction pair is revealed. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is a non-destructive technique that relies on monitoring naturally occurring sources of high frequency ultrasound in components and structures. Ultrasonic waves propagate in the form of different wave modes—for instance Lamb waves in thin plates, or Rayleigh and P- and S- waves in bulk structures. Those wave modes have different properties, but also contain information regarding the source of the naturally occurring wave. Manually, the wave modes can be recognized by comparing a time–frequency representation of the signal to the dispersion curves expected in the tested object. For analyzing a large number of signals, this manual mode recognition becomes a tedious process. This paper proposes a method to automate the wave mode recognition based on some minimal knowledge of the occurring wave modes. As inputs, only the propagation speed of the possible wave modes and the source position need to be provided along with a limited set of reference wavelets for each wave mode. Cross-correlation of a signal with a reference wavelet of a mode reduces the signal to a limited number of peaks that may delineate the start of the mode. Using other signals from the same event but from different sensors, velocities are calculated for each peak in order to select the peak that corresponds to the arrival of the mode under investigation. To validate the method, a dataset was recorded based on four types of out-of-plane sources: Hsu-Nielsen sources of 0.3 and 0.5 mm, sensor pulse signals and AEs from melting ice. Since the presented dataset was recorded on a plate, the aim of the validation was to recognize the zero-order symmetrical and anti-symmetrical Lamb modes. The results of the proposed mode recognition method applied to this dataset are compared with results from manual mode recognition. For Hsu-Nielsen sources, the succes rate is found to be above 95%. For narrow-band pulsed signals or for AEs from melting ice with a low signal-to-noise ratio, succes rates between 75 and 80% relative to manual mode recognition are reported. [ABSTRACT FROM AUTHOR]

This paper attempts to evaluate the change in the behavior of the acoustic waves associated with flexure cracks developed in rubberized concretes in a subfreezing environment. Seven normal and rubberized concrete mixtures were developed with different compositions. Prism samples from each mixture were tested at two temperatures (25°C and −20°C) under a four-point monotonic flexure test while being monitored via two attached acoustic emission (AE) sensors to collect the emitted AEs till failure. The AE signal characteristics such as signal amplitudes, number of hits, and cumulative signal strength (CSS) were collected and used for three AE parameter-based analyses: b-value, intensity, and rise time–amplitude (RA) analysis. Analyzing the acoustic activity revealed micro- and macrocracks nucleation, which were found to be associated with a noticeable spike in CSS, historic index [H(t)], severity (Sr) values, and a significant dip in the b-values. In addition, cold temperature was found to increase the micro- and macrocracking onset load and time regardless of mixture composition. Besides, mixtures with a lower C/F, less crumb rubber (CR) content, and/or smaller rubber particle size witnessed higher micro- and macrocrack load and time thresholds. Noticeably, the AE signal attenuation effect caused by the high CR content (up to 30%) at 25°C was significantly relieved when samples were tested at −20°C. Three charts were developed to classify the cracking level based on the values of the intensity analysis parameters [H(t) and S] and RA analysis. Practical Applications: Infrastructure failures can cause severe economic losses and fatalities, but luckily they can be avoided through regular inspections with the aid of nondestructive testing and subsequent repairs. Aging structures need more inspections to detect potential deficiencies, whereas newly constructed ones can safely undergo fewer inspections and preserve resources. Data from nondestructive testing programs can optimize inspection schedules. When it comes to hard-to-reach structures in extreme weather, acoustic emission (AE) has the potential to be a more suitable nondestructive testing technique over other conventional methods. Some concerns were raised regarding the effectiveness of the AE technique for applications involving novel construction materials such as rubberized concrete, because rubber particles have noticeable acoustic absorption capacities that may affect the parameters of the AE waves and impact the posttesting analysis. Another concern was the influence of cold temperature on the AE wave characteristics due to the change in concrete microstructure at low temperatures. This study aimed at addressing these concerns by utilizing AE analysis to highlight the onset of micro- or macrocracks in rubberized concrete mixtures exposed to cold temperatures. Three user-friendly charts are presented that can advise on inspection decisions based on whether deterioration has reached a certain level. [ABSTRACT FROM AUTHOR]

Structural elements undergo multiple levels of damage at various locations due to environments and critical loading conditions. The level of damage and its location can be predicted using acoustic emission (AE) waveforms that are captured from the generation of inherent microcracks. Existing AE methods are reliant on the feature selection of the captured waveforms and may be subjective in nature. To automate this process, this paper proposes a deep-learning model to predict the damage severity and its expected location using AE waveforms. The model is based on a densely connected convolutional neural network (CNN) that offers superior feature extraction and minimal training data requirements. Time-domain AE waveforms are used as inputs of the proposed model to automate the process of predicting the severity of damage and identifying the expected location of the damage in structural elements. The proposed approach is validated using AE data collected from a concrete beam and a wooden beam and plate. The results show the capability of the proposed method for predicting the level of damage with an accuracy range of 92-95% and identifying the approximate location of damage with 90-100% accuracy. Thus, the proposed method serves as a robust technique for damage severity prediction and localization in civil structures. [ABSTRACT FROM AUTHOR]

Depleted gas reservoirs are important natural gas storage media, thus research on the mechanical properties and damage evolution of reservoir rocks under alternating load conditions has significant practical implications for seal integrity studies. This paper conducted multi-level cyclic loading triaxial compression experiments on medium-porosity medium-permeability sandstone under different confining pressures and used acoustic emission (AE) instruments to detect the AE characteristics during the experiment, analyzing the mechanical characteristics, AE, and damage evolution characteristics. The experimental results show that after cyclic loading, the peak strength of sandstone increased by 14–17%. With the increase in the upper limit stress of cyclic loading, the elastic modulus showed a trend of first increasing and then gradually decreasing. The damage variable of rock samples rose with a rise in the upper limit stress of cyclic loading and confining pressure, and the rock damage was mostly localized at the peak stress. The AE b-value increased generally as confining pressure increased, showing that fractures occurred quicker and more unevenly at lower confining pressures. The distribution of RA-AF values shows that a sudden increase in stress causes the initiation and expansion of cracks in medium-permeability sandstone, and that tensile and shear cracks form continuously during the cyclic loading process, with shear cracks developing more pronounced. This research can provide some theoretical guidance for the long-term stable operation and pressure enhancement expansion of depleted gas reservoir storage facilities. [ABSTRACT FROM AUTHOR]

Drones are a promising technology performing various functions, ranging from aerial photography to emergency response, requiring swift fault diagnosis methods to sustain operational continuity and minimise downtime. This optimises resources, reduces maintenance costs, and boosts mission success rates. Among these methods, traditional approaches such as visual inspection or manual testing have long been utilised. However, in recent years, data representation methods, such as deep learning systems, have achieved significant success. These methods learn patterns and relationships, enhancing fault diagnosis, but also face challenges with data complexity, uncertainties, and modelling complexities. This paper tackles these specific challenges by introducing an efficient representation learning method denoted Multiverse Augmented Recurrent Expansion (MVA-REX), allowing for an iterative understanding of both learning representations and model behaviours and gaining a better understanding of data dependencies. Additionally, this approach involves Uncertainty Bayesian Optimisation (UBO) under Extreme Learning Machine (ELM), a lighter neural network training tool, to tackle both uncertainties in data and reduce modelling complexities. Three main realistic datasets recorded based on acoustic emissions are involved in tackling propeller and motor failures in drones under realistic conditions. The UBO-MVA Extreme REX (UBO-MVA-EREX) is evaluated under many, error metrics, confusion matrix metrics, computational cost metrics, and uncertainty quantification based on both confidence and prediction interval features. Application compared to the well-known long-short term memory (LSTM), under Bayesian optimisation of the approximation error, demonstrates performances, certainty, and cost efficiency of the proposed scheme. More specifically, the accuracy obtained by UBO-MVA-EREX, ~0.9960, exceeds the accuracy of LSTM, ~0.9158, by ~8.75%. Besides, the search time for UBO-MVA-EREX is ~0.0912 s, which is ~98.15% faster than LSTM, ~4.9287 s, making it highly applicable for such challenging tasks of fault diagnosis-based acoustic emission signals of drones. [ABSTRACT FROM AUTHOR]

This paper applied the prevalent acoustic emission (AE) technology to identify the damage modes and critical conditions for FRP/steel-concrete composite beams during the failure process. AE signals generated by the structural damages were classified efficiently by using a novel self-adaptive real-time clustering (SARTC) method; damage modes corresponding to each clustering category were recognized and analyzed, and the dominant damage type at different stages was obtained by comparing the AE activities and feature values. By conducting the AE intensity analysis, the dynamic evolutionary mechanisms and critical conditions of composite beams were identified; the increase in intensity value from 0.2 to 0.3 reflects the process from critical yielding to major fracture. By establishing the non-linear fitting model between local response and cumulative AE energy, the instantaneous status at arbitrary local position of the composite beam can be inverted and predicted quantitatively by independent AE testing. [ABSTRACT FROM AUTHOR]

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate the effect of coaxial swirlers on acoustic emission and reduction of potential core length in jet engines. Design/methodology/approach: The swirlers are introduced in the form of curved vanes with angles varied from 0° to 130°, corresponding to swirl numbers of 0–1.5. These swirlers are fixed in the annular chamber and tested at different nozzle pressure ratios of 2, 4 and 6. Findings: The study finds that transonic tones exist for the nonswirl jet, creating an unfavorable effect. However, these screech tones are eliminated by introducing a swirl jet at the nozzle exit. Weak swirl shows a greater reduction in noise than strong swirl at subsonic conditions. In addition, the introduction of swirl jets at all pressure ratios significantly reduces jet noise and core length in supersonic conditions, mitigating the noise created by shockwaves and leading to screech tone-free jet mixing. Originality/value: The paper provides valuable insights into the use of coaxial swirlers for noise reduction and core length reduction in jet engines, particularly in supersonic conditions. [ABSTRACT FROM AUTHOR]

The detection and identification of noise from moving parts inside a sealed cavity is crucial for ensuring the reliability of sealed equipment. However, traditional noise recognition methods struggle to meet the stringent demands for high detection accuracy. Inspired by the idea of ensemble learning, this paper proposes a noise recognition method that combines recognition results with high-dimensional mapping to enhance the recognition of noise. Firstly, a built noise identification experimental system is used to collect signals. Then, features are filtered and extracted based on acoustic emission principles and signal properties. Ultimately, a new fusion method is devised incorporating recognition results as new features into the original dataset and designing multiple layers of single algorithms based on their individual strengths to enhance the feature extraction capabilities of the algorithm. In the first layer of the fusion algorithm, CatBoost learns from the original dataset and incorporates its recognition results into the dataset. XGBoost then trains on the new dataset as the training set. Finally, the sparse output matrix generated by XGBoost is input into a logistic regression (LR) algorithm for training and prediction. The proposed method is verified by experiments on datasets and the results show that the accuracy of this method is higher than that of a single recogniser. It also performs better than current mature stacking fusion methods and mapping-based fusion methods. This fusion approach is of great significance for improving noise recognition accuracy and for innovating fusion methods. [ABSTRACT FROM AUTHOR]

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Accurate estimation of the position of the fault source in the aircraft engine is the key to achieve engine structural health monitoring (SHM). In this paper, a convolutional neural network and graph convolutional network (CNN–GCN)-based dual-sensor acoustic emission (AE) localization method is proposed for locating the fault source in the engine casing with multi-part coupling features. Firstly, the time–frequency map data sets of AE signals at different locations are established by using continuous wavelet transform to analyze the effect of multi-part coupling features on AE signals. Secondly, combined with its reverberation mode, multi-modal and dispersion characteristics, the effectiveness of CNN–GCN model is trained, verified and tested. Finally, the sensitivity of the localization results to the sensor types is analyzed, and the sensor combination mode with high localization accuracy is obtained. These results show that the proposed method in this paper can be used as an effective means for locating the fault source of the engine casing with complex coupling interface features. [ABSTRACT FROM AUTHOR]

Steel-fibre-reinforced concrete (SFRC) has been increasingly used in the field of engineering structures in recent years. Hence, the accurate monitoring of the fracturing process of in-service SFRC has considerable significance in terms of structural safety. This paper investigates the acoustic emission (AE) and digital image correlation (DIC) features characterising the damage behaviour of SFRC samples in compression. For all the tests, cumulated AE, b-value, βt coefficient, average frequency, and rise angle are considered to describe the actual SFRC failure mechanisms. The results show that SFRC exhibits enhanced toughness compared to normal concrete (NC), with an indicated transition from a brittle to a ductile structural behaviour. This improved behaviour can be attributed to the bridging effect of steel fibres, which also drives the progressive tensile-to-shear crack transition, thus being the main cause of the final SFRC failure. As the loading rate increases, there is a corresponding increase in the number of shear cracks, leading to a decrease in the overall ductility and toughness of SFRC. Moreover, since the number of shear cracks notably increases right before SFRC fracture, this can serve as a safety warning of the impending failure. Furthermore, the cumulated AE curve displays a strong discontinuity in the occurrence of an unstable fracturing process in SFRC, which can also be forecasted by the AE time-scaling coefficient βt. The AE and DIC features can be used as failure precursors in the field of structural surveying, offering an accurate technical support for engineering failure warnings. [ABSTRACT FROM AUTHOR]

The present paper discusses the scientific and technical problem of optimizing the design and characteristics of a new type of solid-state sensors for motion parameters on bulk acoustic waves in order to increase the signal-to-noise ratio and the detectability of an informative signal against the background of its own noise and interference. Criteria for choosing materials for structural elements, including piezoelectric transducers of the sensitive element, were identified; a corresponding numerical simulation was performed using the developed program; and experimental studies according to the suggested method were carried out to validate the obtained analytical and calculated positions. The experimental results revealed the correctness of the chosen criteria for the optimization of design parameters and characteristics, demonstrated the high correlation between the results of modeling and field studies, and, thus, confirmed the prospects of using this new type of solid-state acoustic sensors of motion parameters in the navigation and control systems of highly dynamic objects. [ABSTRACT FROM AUTHOR]

Softness is one of the essential properties of hygiene tissue products. Reliably measuring it is of utmost importance to ensure the balance between customer expectations and cost-effective tissue production. This study presents a method for assessing softness by analyzing acoustic emissions produced while tearing a tissue specimen. The aim was to train neural network models using the corrected results of human panel tests as the ground truth labels and to predict the tissue softness in two- and three-class recognition tasks. We also investigate the possibility of predicting some production parameters related to the softness property. The results proved that tissue softness and production parameters could be reliably estimated only by the tearing noise. [ABSTRACT FROM AUTHOR]

Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper. [ABSTRACT FROM AUTHOR]

The combination of acoustic emission and electrical potential monitoring methods holds promise for monitoring and warning of rock bursts due to its comprehensive reflection of the damage process. However, the response features during the creep failure process remain unclear. In this paper, a coal creep test was conducted using a combination of electric potential and acoustic emission monitoring. The response characteristics were analyzed, their multifractal characteristics were analyzed, and the joint response mechanism was explored. This research demonstrated a significant correlation among acoustic emission and electrical potential signals and creep deformation and failure. At the start of loading, a brief increase in both signals was observed. As deformation progressed, the signals became steady, and their intensity and fluctuation notably increased during accelerated creep failure. Quantitative analysis of acoustic emission count rates and electric potential intensity during creep processes revealed a quadratic relationship of acoustic emission count rates with stress and strain variations, in addition to an exponential correlation with mean electric potential intensity. Additionally, the statistical analysis of the multifractal characteristics before coal sample instability and failure revealed consistent trends in the characteristic values of Δα and Δf(α), with initial decrease followed by slight fluctuations, culminating in a sudden abnormal change preceding failure. Finally, leveraging the mechanisms of acoustic emission and electrification under load, this study discusses the multifractal characteristics of acoustic-electric signals and verifies their complementary roles in accurately predicting coal rock creep failure. These studies provide essential theoretical groundwork and references for improving dynamic disaster monitoring in coal mines. [ABSTRACT FROM AUTHOR]

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established. [ABSTRACT FROM AUTHOR]

Rock mass is one of the most important load-bearing media in geotechnical engineering. It has been continually vulnerable to geological tectonic movements, natural calamities, and human excavation activities. Its inherent weak surfaces such as primary pores, joints, and fissures have resulted in varying damage degrees. In mining operations, the damaged rock mass has a variety of negative impacts on the stability of its overlying structures and is frequently disturbed by the load. To study the damage law of rock mass under cyclic loading, in this paper, an acoustic emission (AE) device was employed to monitor the rock under the action of two types of cyclic loads: the variable upper and lower pre-loads, and the fixed upper and lower pre-loads. The damage of the loaded rock was split into three stages in this research, based on the features of the AE signals of the rock under uniaxial load, and the damage evolution of the loaded rock was analyzed in distinct stages. The AE signals of the rock under cyclic loading were mainly emitted in the first loading stage. When the stress did not exceed the maximum stress value in the stress history of the loaded rock, few new AE event was generated in the loaded rock. After the low-frequency cyclic static load, the AE signals varied with the load-bearing stress of the rock during the whole process from initial loading to failure, which was consistent with the characteristics of the AE signals of the loaded rock. The research results can be adapted to rock mass in open-pit mines stability analysis and risk prediction while providing some references for the early warning and danger relief of rock masses in engineering. [ABSTRACT FROM AUTHOR]

In this article, the authors refer to the measurement of acoustic emission, where they found that this methodology can determine the combustion characteristics in the combustion chamber of the engine and thus it is possible to confirm a better combustion process and overall reduction of engine noise after decarbonization. The formation of diesel exhaust soot is also supported by the authors' paper, in which they describe how such soot is formed and what effect the addition of special mixtures to the combustion process has on the formation of such carbon deposits. The article uses BG technologies for deep engine decarbonization, including intake and exhaust parts. This technology is one of many on the market, but it was only with this technology that the authors managed to achieve the relevant results that this article will introduce to. For the test, we selected vehicles that had a problem to pass at the emission control. [ABSTRACT FROM AUTHOR]

Super-precision bearing lubrication condition is essential for equipment's overall performance. This paper investigates a monitoring method of bearing lubrication using multi-sensors based on graph data. An experiment was designed and carried out, establishing a dataset including vibration, temperature, and acoustic emission signals. Graph data were constructed based on a priori knowledge and a graph attention network was employed to conduct a study on monitoring bearing lubrication abnormalities and discuss the influence of a missing sensor on the monitoring. The results show that the designed experiments can effectively respond to the degradation process of bearing lubrication, and the graph data constructed based on a priori knowledge show a good effect in the anomaly monitoring process. In addition, the multi-sensor plays a significant role in monitoring bearing lubrication. This work will be highly beneficial for future monitoring methods of bearing lubrication status. [ABSTRACT FROM AUTHOR]

The time difference of arrival (TDOA) method has traditionally proven effective for locating acoustic emission (AE) sources and detecting structural defects. Nevertheless, its applicability is constrained when applied to anisotropic materials, particularly in the context of fiber-reinforced composite structures. In response, this paper introduces a novel COmposite LOcalization using Response Surface (COLORS) algorithm based on a two-step approach for precise AE source localization suitable for laminated composite structures. Leveraging a response surface developed from critical parameters, including AE velocity profiles, attenuation rates, distances, and orientations, the proposed method offers precise AE source predictions. The incorporation of updated velocity data into the algorithm yields superior localization accuracy compared to the conventional TDOA approach relying on the theoretical AE propagation velocity. The mean absolute error (MAE) for COLORS and TDOA were found to be 6.97 mm and 8.69 mm, respectively. Similarly, the root mean square error (RMSE) for COLORS and TODA methods were found to be 9.24 mm and 12.06 mm, respectively, indicating better performance of the COLORS algorithm in the context of source location accuracy. The finding underscores the significance of AE signal attenuation in minimizing AE wave velocity discrepancies and enhancing AE localization precision. The outcome of this investigation represents a substantial advancement in AE localization within laminated composite structures, holding potential implications for improved damage detection and structural health monitoring of composite structures. [ABSTRACT FROM AUTHOR]

The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the "collapse" time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system "crash" time for beech and 4 s in advance of the system "crash" time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc. [ABSTRACT FROM AUTHOR]

Nitrides are the leading semiconductor material used for the fabrication of high electron mobility transistors (HEMTs). They exhibit piezoelectric properties, which, coupled with their high mechanical stiffness, expand their versatile applications into the fabrication of piezoelectric devices. Today, due to advances in device technology that result in a reduction in the size of individual transistor elements and due to increased structural complexity (e.g., multi-gate transistors), the integration of piezoelectric materials into HEMTs leads to an interesting occurrence, namely acoustic emission from the transistor gate due to piezoelectric effects. This could affect the device's performance, reliability, and durability. However, this phenomenon has not yet been comprehensively described. This paper aims to examine this overlooked aspect of AlGaN/GaN HEMT operation, that is, the acoustic emission from the gate region of the device induced by piezoelectric effects. For this purpose, dedicated test structures were designed, consisting of two narrow 1.7 μm-wide metallization strips placed at distances ranging from 5 μm to 200 μm fabricated in AlGaN/GaN heterostructures to simulate and examine the gate behavior of the HEMT transistor. For comparison, the test device structures were also fabricated on sapphire, which is not a piezoelectric material. Measurements of acoustic and electrical interactions in the microwave range were carried out using the "on wafer" method with Picoprobe's signal–ground–signal (SGS)-type microwave probes. The dependence of reflectance |S11| and transmittance |S21| vs. frequency was investigated, and the coupling capacitance was determined. An equivalent circuit model of the test structure was developed, and finite element method simulation was performed to study the distribution of the acoustic wave in the nitride layers and substrate for different frequencies using Comsol Multiphysics software. At frequencies up to 2–3 GHz, the formation of volume waves and a surface wave, capable of propagating over long distances (in the order of tens of micrometers) was observed. At higher frequencies, the resulting distribution of displacements as a result of numerous reflections and interferences was more complicated. However, there was always the possibility of a surface wave occurrence, even at large distances from the excitation source. At small gate distances, electrical interactions dominate. Above 100 µm, electrical interactions are comparable to acoustic ones. With further increases in distance, weakly attenuated surface waves will dominate. [ABSTRACT FROM AUTHOR]

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks which is aimed towards specialized places such as oil storage tanks with high explosion-proof requirements, such as cave oil tanks and buried oil tanks. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. An independent channel AE detection system is designed, which can store the collected data in a piece of independent explosion-proof equipment, and can analyze and process the data in a safe area after the detection, solving the problems of a short signal acquisition distance and the weak safety protection applied to traditional AE instruments. A location analysis of the AE sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open-tank tests confirms that using AE technology effectively captures corrosion signals from oil storage tanks' bottoms. The feasibility of carrying out online inspection under the condition of oil storage in vertical steel oil tanks was verified through a comparison with open inspections, which provided a guide for determining the inspection target and opening order of large-scale oil tanks. [ABSTRACT FROM AUTHOR]

The instability of hard and brittle rock often leads to disastrous consequences in underground engineering. Under various surrounding rock pressure conditions, in situ stress induces corresponding deformation and damage to the floor post-mining. Therefore, it is crucial to examine the effects of mining under different confining pressures on rock disturbance, damage characteristics, and their distribution. Consequently, triaxial loading experiments under varying intermediate principal stress conditions were conducted on red sandstone specimens, using an acoustic emission monitoring system to track energy changes during rock damage and failure. This approach aids in studying crack generation, propagation, and fracture damage evolution. The results indicate that rock deformation results in axial compression and dilatancy, aligned with the direction of minimum and intermediate principal stresses. Ductility in rock failure becomes more pronounced with increased stress, primarily manifesting as shear failure. Internal cracks in the specimen lead to stress concentration and marked plastic deformation under compression, yet do not result in macroscopic surface cracks. The fracture angle θ of specimens post-failure generally exceeds 45° and varies with stress changes; at consistent burial depths, the angle of the sandstone failure surface increases with intermediate principal stress. This paper preliminarily establishes the informational linkage between rock failure and energy release, analyzing the rock samples over time and space. This research offers insights for analyzing and mitigating sudden rock instability. [ABSTRACT FROM AUTHOR]

A two-dimensional unsteady seepage model for coal using a finite element program is developed, and the temporal variations of key factors such as water pressure and hydraulic gradient are analyzed in this paper. Additionally, the triaxial rock mechanical experiment and utilized pneumatic fracturing equipment on raw coal samples to investigate both hydraulic and pneumatic fracturing processes are conducted. Through these experiments, the relationship between pressure and crack formation and expansion are examined. The analysis reveals that the pore pressure gradient at the coal inlet reaches its peak during rapid surges in water pressure but diminishes over time. Conversely, the pore pressure gradient at the outlet side exhibits a gradual increase. Hydraulic fracturing is most likely to occur at the water inlet during sudden increases in water pressure. Besides, as the permeability of coal decreases, the duration for seepage stabilization prolongs due to the intensified pore pressure gradient resulting from sudden increases in water pressure. Moreover, an extended period of high hydraulic gradient further increases the risk of hydraulic fracturing. The experimental findings indicate that coal samples initially experience tensile failure influenced by water and air pressure. Subsequently, mode I cracks form under pressure, propagating along the fracture surface and becoming visible. The main types of failure observed in hydraulic and pneumatic fracturing are diametrical tensile failure, and the development of fractures can be categorized into three distinct stages, which contains the initial stage characterized by slight volume changes while water pressure increases, the expansion stage when pressure reaches the failure strength, and the crack closure stage marked by little or even decreasing volume changes during pressure unloading. The acoustic emission signal accurately corresponds to these three stages. [ABSTRACT FROM AUTHOR]

In order to solve the problem of untimely monitoring of tool wear status during laser-ultrasonic compound cutting (LUCC), which leads to the reduction of process accuracy and product quality damage, a method of tool wear status monitoring based on acoustic emission (AE) and deep learning is proposed in this paper. The AE signal is collected and analyzed in the frequency and time-frequency domains by using Fourier transform (FT) and wavelet packet decomposition (WPD), it is found that the AE signal energy is mainly concentrated in the 1st, 2nd, 3rd, and 4th frequency bands, and the energy of each frequency band in the signal increases gradually when the tool wear degree increases, however, the percentage of energy of each frequency band in the total energy shows different change trend. According to the amount of flank wear of the tool, the tool wear degree is divided into four tool wear states. The SVM model and BP neural network model were established based on MATLAB software to realize the tool wear state monitoring. The results show that the tool wear monitoring based on BP neural network is better than that of SVM for tool wear status monitoring. The model identification correct rate reaches 99 %, which is more suitable for the tool wear state monitoring designed in this paper. [ABSTRACT FROM AUTHOR]

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes. [ABSTRACT FROM AUTHOR]

We present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red–green–blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available. [ABSTRACT FROM AUTHOR]

The freeze–thaw (F-T) cycle is one of the weathering factors that can cause rapid deterioration of the mechanical properties of rocks and threaten the stability and durability of rock engineering in cold regions. To study the influence of F-T action on the mechanical and deformation characteristics of sandstone, triaxial compression tests were performed under four confining pressures (0, 1, 3, and 5 MPa) and five F-T cycles (0, 40, 80, 120, and 160). The experimental results show that the stress–strain curves of the sandstone after F-T cycles can be divided into four phases: compaction, elasticity, strain-hardening, and postpeak failure. According to the deformation behaviors of the sandstone with various F-T cycles, an elastoplastic damage constitutive equation was established considering the compaction effect and F-T action. The model parameters were determined, and the theoretical curves were calculated. The comparisons with theoretical and experimental stress–strain curves demonstrate that the formulated constitutive equation can reflect the key deformation stages and mechanical behaviors of sandstone with different F-T cycles. Because the damage variable is introduced in establishing the model, the proposed model can also provide a quantitative description of the macrodamage, which agrees well with the microdamage of the sandstone after many F-T cycles measured by acoustic emission (AE) technology. The formulated constitutive equation in this paper can well describe the experimental deformation curves of sandstone with various F-T cycles and is easy to embed into numerical analysis software. The results can provide a certain theoretical reference for the evaluation of the durability and stability of rock engineering structures impacted by F-T-induced damage in cold regions. [ABSTRACT FROM AUTHOR]

Underground reservoirs in coal mines, consisting of goafs (By goaf, we mean the space that remains underground after the extraction of valuable minerals), are commonly utilized for mine water storage and drainage, with their primary load-bearing structures being the "roof–coal pillar" systems. Consequently, this structure must endure the repeated immersion behavior resulting from fluctuations in the mine water level, resulting in the risk of geological disasters. This paper analyzes the variation in mechanical properties of sandstone–coal composite samples after repeated immersion cycles through axial loading tests. The results indicate that the water content of the sample exhibits a notable and rapid increase with each successive immersion cycle. This corresponds to a decrease in the stress threshold and modulus parameters of the samples. Moreover, the acoustic emission signals serve as indicators of the softening characteristics of the samples. With the increase in immersion cycles, there is an augmentation in both the frequency and extent of shear cracks. The non-linear failure characteristics of the samples become more pronounced. Consequently, water significantly weakens the cementing material between rock grains. Both sandstone and coal display a decrease in deformation resistance capabilities at a macroscopic level. The constitutive model of the composite sample was improved based on the degradation characteristics of mechanical strength and strain energy parameters, which offers enhanced accuracy in analyzing the degradation process caused by water immersion. This paper offers a crucial theoretical foundation for comprehending the deterioration evolution characteristics of the "roof–coal pillar" bearing structure affected by repeated immersion. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss the effect of the sensor on the acoustic emission (AE) signature and to develop a methodology to reduce the sensor effect. Pencil leads are broken on PMMA plates at different source–sensor distances, and the resulting waves are detected with different sensors. Several transducers, commonly used for acoustic emission measurements, are compared with regard to their ability to reproduce the characteristic shapes of plate waves. Their consequences for AE descriptors are discussed. Their different responses show why similar test specimens and test conditions can yield disparate results. This sensor effect will furthermore make the classification of different AE sources more difficult. In this context, a specific procedure is proposed to reduce the sensor effect and to propose an efficient selection of descriptors for data merging. Principal Component Analysis has demonstrated that using the Z-score normalized descriptor data in conjunction with the Krustal–Wallis test and identifying the outliers can help reduce the sensor effect. This procedure leads to the selection of a common descriptor set with the same distribution for all sensors. These descriptors can be merged to create a library. This result opens up new outlooks for the generalization of acoustic emission signature libraries. This aspect is a key point for the development of a database for machine learning. [ABSTRACT FROM AUTHOR]

With the increasing application of composite components in various industries, the assessment of their structural integrity, the analysis of damage processes, and the identification of damage patterns are becoming increasingly important. The accuracy of the analysis relies heavily on the selection of features. This paper presents a new idea to extract effective damage features from acoustic emission (AE) signals and accurately identify different damages in the failure process of carbon fiber reinforced polymer specimens. The method combines Hilbert–Huang transform (HHT) and Linear Discriminant Analysis (LDA) to analyze the AE signals generated during the damage process of composite specimens. Specifically, the Hilbert marginal energy spectrum of the signals was regarded as frequency domain descriptors. The frequency domain descriptors were subsequently associated with the parametric features after dimensionality reduction by LDA to construct the classification framework named HHT-LDA. The results show that the frequency domain descriptors of the AE signals associated with each damage mode characterized distinctly. The frequency band of energy distribution in the raw waveform for matrix cracking, delamination, and fiber breakage are (100–150 kHz), (150–300 kHz), and (300–350 kHz), respectively. In addition, the three damage patterns mentioned above were successfully detected and recognized from the complex AE waveforms using HHT-LDA with 85% overall classification rate. This research idea will serve as a potential method for future composite damage pattern recognition and provide supporting knowledge for practical applications of AE monitoring. [ABSTRACT FROM AUTHOR]

Carbon-fiber-reinforced polymer (CFRP) composites are widely used in lightweight structures because of their high specific strength, specific modulus, and low coefficient of thermal expansion. Additionally, the unidirectionally arrayed chopped strand (UACS) laminates have excellent mechanical properties and flowability, making them suitable for fabricating structures with complex geometry. In this paper, the damage process of UACS quasi-isotropic laminates under tensile load was tested using acoustic emission detection technology. The mechanical properties and damage failure mechanism of UACS laminates were studied combined with finite element calculation. By comparing and analyzing the characteristic parameters of acoustic emission signals such as amplitude, relative energy, and impact event, it is found that acoustic emission behavior can accurately describe the damage evolution of specimens during loading. The results show that the high-amplitude signals representing fiber fracture in continuous fiber laminates are concentrated in the last 41%, while in UACS laminates they are concentrated in the last 30%. In UACS laminates, more of the damage is caused by matrix cracks and delamination with medium- and low-amplitude signals, which indicates that UACS laminates have a good suppression effect on damage propagation. The stress–strain curves obtained from finite element analysis agree well with the experiment results, showing the same damage sequence, which confirms that the model described in this research is reliable. [ABSTRACT FROM AUTHOR]

This paper presents the effects of matrix and interface on the fracture toughness and progressive damage mechanisms of 2.5D woven SiCf/SiC composites. Third-generation SiC fibers were selected. 2.5D woven SiCf/SiC composites with four interfacial layers PyC, PyC/SiC, BN, BN/SiC and two matrix polycarbosilane (PCS) and liquid hyperbranched polycarbosilane with vinyl groups (VHPCS) were prepared by a combination of chemical vapour deposition (CVD), chemical vapor infiltration (CVI), and precursor infiltration pyrolysis (PIP) processes, respectively. Single edge notch bending (SENB) tests were carried out to investigate the fracture toughness. Moreover, acoustic emission (AE) and X-ray micro-computed tomography (Micro-CT) techniques were employed to analyze the progressive mechanical properties and failure mechanism. Results showed that the matrix and interface have significant effects on the fracture toughness. Moreover, 2.5D woven composites with PyC/SiC interfacial layers and VHPCS matrix have the highest fracture toughness (16.93 ± 1.20 Mpa • m1/2). Furthermore, during progressive damage, the pores can influence the crack expansion path, and effectively regulate the stress concentration and increase the toughness of the composites. [ABSTRACT FROM AUTHOR]

Liquid nitrogen (LN2) fracturing is beneficial to the development and utilization of geothermal energy. In this paper, red sandstone was heated from room temperature to different temperatures (25°C–800°C) and then cooled with LN2. After attaining room temperature, nuclear magnetic resonance (NMR), uniaxial compression, and acoustic emission (AE) tests were conducted, and results were compared for different samples. The results showed that with an increase in quenching temperature difference, the volume of micropores decreased gradually, while the volume of fine pores, mesopores, macropores, and total porosity increased, resulting in the reduction of the compressive strength. A higher quenching temperature difference also reduced the sample's total time to fail, and the failure mode was transformed from single inclined shear failure to conical failure. This is because the thermal stress (caused by the rapid cooling of LN2) expanded the original cracks of the sandstone, leading to high porosity and low compressive strength of the heat-treated and quenched samples. [ABSTRACT FROM AUTHOR]

The research reported in this paper deals with the potential of detecting non-simultaneous operation in on-load tap-changer (OLTC) using an acoustic emission method. Tests conducted under laboratory conditions were carried out using an OLTC model. Three transducers with different characteristics were used: WD 17 AH, D9241A, and R15α, alongside oscillography as the reference method. The use of two new descriptors in the time domain was proposed. The feasibility of detecting the defect with different piezoelectric transducers was investigated. As a result of the analysis of the results, it was found that each piezoelectric transducer can identify nonsimultaneous operation of the switch. The most significant changes in descriptor values occurred in the time domain, and the most effective transducer turned out to be R15α. [ABSTRACT FROM AUTHOR]