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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]

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]

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]

A fiber optic interferometric sensor with an intrinsic transducer along a length of the fiber is presented for ultrasound measurements of the acoustic emission from partial discharges inside oil-filled power apparatus. The sensor is designed for high sensitivity measurements in a harsh electromagnetic field environment, with wide temperature changes and immersion in oil. It allows enough sensitivity for the application, for which the acoustic pressure is in the range of units of Pa at a frequency of 150 kHz. In addition, the accessibility to the sensing region is guaranteed by immune fiber-optic cables and the optical phase sensor output. The sensor design is a compact and rugged coil of fiber. In addition to a complete calibration, the in-situ results show that two types of partial discharges are measured through their acoustic emissions with the sensor immersed in oil. [ABSTRACT FROM AUTHOR]

Wind power is growing rapidly as a green and clean energy source. As the core part of a wind turbine, the blades are subjected to enormous stress in harsh environments over a long period of time and are therefore extremely susceptible to damage, while at the same time, they are costly, so it is important to monitor their damage in a timely manner. This paper is based on the detection of blade damage using acoustic emission signals, which can detect early minor damage and internal damage to the blades. Instead of conventional piezoelectric sensors, we use fiber optic gratings as sensing units, which have the advantage of small size and corrosion resistance. Furthermore, the sensitivity of the system is doubled by replacing the conventional FBG (fiber Bragg grating) with a π-phase-shifted FBG. For the noise problem existing in the system, this paper combines the traditional WPD (wavelet packet decomposition) denoising method with EMD (empirical mode decomposition) to achieve a better noise reduction effect. Finally, small wind turbine blades are used in the experiment and their acoustic emission signals with different damage are collected for feature analysis, which sets the stage for the subsequent detection of different damage degrees and types. [ABSTRACT FROM AUTHOR]

Cavitation is a detrimental phenomenon in hydraulic machinery, adversely impacting its performance, inducing vibration and noise, and leading to corrosion damage of overflow components. Centrifugal pump internal cavitation will lead to severe vibration and noise, and not only will the performance of hydraulic machinery be adversely affected but the impact generated by the collapse of the vacuole will also cause damage to the impeller wall structure, seriously affecting the safety of the equipment's operation. To prevent the generation and development of internal cavitation in centrifugal pumps, to prevent the hydraulic machinery from being in a state of cavitation for a long time, to avoid the failure of the unit, and to realize the predictive maintenance of centrifugal pumps, therefore, it is of great significance to research the methods for monitoring the cavitation of hydraulic machinery and the methods for suppressing the cavitation. This paper comprehensively describes the centrifugal pump cavitation mechanism and associated hazards. It also discusses the current state of centrifugal pump cavitation monitoring methods, including commonly used approaches such as the flow-head method, high-speed photography, pressure pulsation method, acoustic emission method, and vibration method. A comparative analysis of these methods is presented. Additionally, the paper explores signal characterization methods for centrifugal pump cavitation, including time-domain feature extraction, frequency-domain feature extraction, and time–frequency-domain feature extraction. The current research status is elaborated upon. Moreover, the paper presents methods to mitigate cavitation and prevent its occurrence. Finally, it summarizes the ongoing research on identifying and determining the cavitation state in centrifugal pumps and offers insights into future research directions. [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]

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]

The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics. [ABSTRACT FROM AUTHOR]

Chemical pressure storage tanks are containers designed to store fluids at high pressures, i.e., their internal pressure is higher than the atmospheric pressure. They can come in various shapes and sizes, and may be fabricated from a variety of materials. As aggressive chemical agents stored under elevated pressures can cause significant damage to both people and the environment, it is essential to develop systems for the early damage detection and the monitoring of structural integrity of such vessels. The development of early damage detection and condition monitoring systems could also help to reduce the maintenance costs associated with periodic inspections of the structure and unforeseen operational breaks due to unmonitored damage development. It could also reduce the related environmental burden. In this paper, we consider a hybrid material composed of glass-fiber-reinforced polymers (GFRPs) and a polyethylene (PE) layer that is suitable for pressurized chemical storage tank manufacturing. GFRPs are used for the outer layer of the tank structure and provides the dominant part of the construction stiffness, while the PE layer is used for protection against the stored chemical medium. The considered damage scenarios include simulated cracks and an erosion of the inner PE layer, as these can be early signs of structural damage leading to the leakage of hazardous liquids, which could compromise safety and, possibly, harm the environment. For damage detection, PZT sensors were selected due to their widely recognized applicability for the purpose of structural health monitoring. For sensor installation, it was assumed that only the outer GFRP layer was available as otherwise sensors could be affected by the stored chemical agent. The main focus of this paper is to verify whether elastic waves excited by PZT sensors, which are installed on the outer GFRP layer, can penetrate the GFRP and PE interface and can be used to detect damage occurring in the inner PE layer. The efficiency of different signal characteristics used for structure evaluation is compared for various frequencies and durations of the excitation signal as well as feasibility of PZT sensor application for passive acquisition of acoustic emission signals is verified. [ABSTRACT FROM AUTHOR]

Composite materials are widely used, but they are often subjected to impacts from foreign objects, causing structural damage. To ensure the safety of use, it is necessary to locate the impact point. This paper investigates impact sensing and localization technology for composite plates and proposes a method of acoustic source localization for CFRP composite plates based on wave velocity-direction function fitting. This method divides the grid of composite plates, constructs the theoretical time difference matrix of the grid points, and compares it with the actual time difference to form an error matching matrix to localize the impact source. In this paper, finite element simulation combined with a lead-break experiment is used to explore the wave velocity-angle function relationship of Lamb waves in composite materials. The simulation experiment is used to verify the feasibility of the localization method, and the lead-break experimental system is built to locate the actual impact source. The results show that the acoustic emission time-difference approximation method can effectively solve the problem of impact source localization in composite structures, and the average localization error is 1.44 cm and the maximum localization error is 3.35 cm in 49 experimental points with good stability and accuracy. [ABSTRACT FROM AUTHOR]

For the analysis of deformation dynamics, it is necessary to analyze numerous fringe images. They applied laser spot heating to residually stressed specimens and probed the relaxation to estimate residual stress. Takahashi et al. [[9]] used an optical interferometric method called Electronic Speckle-Pattern Interferometry (ESPI) to discuss the dynamics of plastic deformation and fracture under tensile loading. Modern technological development has made the designing and characterization of materials sophisticated. [Extracted from the article]

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]

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]

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]

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]

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]

The aim of this paper is to analyze the deformation-failure degree and microstructure variations in coal under the cryogenic-freezing effect of liquid CO2. In this paper, X-ray CT scanning technology is adopted to measure the microscopic-morphological parameters of coal. Drawing support from the image processing and three-dimensional (3D) visualization functions of Avizo software, 3D spatial structure variation rules, as well as the deformation and permeability parameters, are quantitatively calculated. Under the effect of LCO2 cryogenic freezing, the macroscopic mechanical properties and deformation-failure degree of coal are thoroughly analyzed. The results show that fracture-scale parameters of treated coal are significantly increased, resulting in spatial structure parameters including the coal plug total volume (Vt), fracture network volume (V0), and proportion of fracture network (μ0) to increase by 17.11%, 56.57%, and 55.59%, respectively. A comparison analysis indicates that the coverage area of a single value function from the percolation theoretical model for treated coal plugs becomes larger, and its percolation curves are more intensive; the quantitative coal permeability coefficients are increased to more than 40% on average, which further proves that the permeability of coal by using LCO2 cryogenic freezing is significantly improved. Under the same uniaxial stress loading rate, the peak stress threshold value required by treated coal in the compaction and elastoplastic deformation stage is decreased. The corresponding output acoustic emission energy is apparently increased, owing to the increased brittleness of coal, and deformation failure of coal occurs more easily. Simultaneously, the fracture network and matrix surface of treated coal are more complex, and the corresponding fractal characteristic is obvious. It could be thus concluded that the coal plugs have deformation-failure changes under cryogenic freezing by using LCO2, increasing the proportion of coal microstructure and enhancing coal permeability. Therefore, the capability of gas migration through the coal microstructure becomes easier, which is favorable for coalbed methane recovery. [ABSTRACT FROM AUTHOR]

Calculation and characterization of the whole process of internal microscopic damage to surface damage in red-bed soft rock is a theoretical research difficulty and an urgent need for engineering safety protection. However, the current study cannot accurately and directly correlate internal and external damage. Therefore, in this paper, a coupled tensor–DEM–FEM model is proposed to deal with surface damage by indoor triaxial test digital image processing (DIC), internal damage by FJM acoustic emission study, and internal and external damage by moment tensor correlation. The study demonstrates that the whole process damage process of the red-bed soft rock peak front can be divided into six distinct phases, with early damage beginning with the elastic phase; the local strain divergence value begins to spiral out of control during the period of crack acceleration development; the overall acoustic emission intensity distribution is in the range of [−8.5, −6.3] in two dimensions and in the range of [−11, −9] in three dimensions; the R were between −40 and 40, which corresponded to the results of the indoor tests. A model has been developed that allows a direct reflection of the whole damage process. The method can be used to better understand the disaster mechanism and guide engineering practice. [ABSTRACT FROM AUTHOR]

The availability of computational power in the domain of Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots are the prime mover of modern industry. Industrial robots comprise multiple forms of rotating machinery, like servo motors and numerous gears. Thus, the PHM of the rotating components of industrial robots is crucial to minimize the downtime in the industries. In recent times, deep learning has proved its mettle in different areas, like bio-medical, image recognition, speech recognition, and many more. PHM with DL applications is a rapidly growing field. It has helped achieve a better understanding of the different condition monitoring signals, like vibration, current, temperature, acoustic emission, partial discharge, and pressure. Most current review articles are component- (or system-)specific and have not been updated to reflect the new deep learning approaches. Also, a unified review paper for PHM strategies for industrial robots and their rotating machinery with DL applications has not previously been presented. This paper presents a review of the PHM strategies with various DL algorithms for industrial robots and rotating machinery, along with brief theoretical aspects of the algorithms. This paper presents a trend of the up-to-date advancements in PHM approaches using DL algorithms. Also, the restrictions and challenges associated with the available PHM approaches are discussed, paving the way for future studies. [ABSTRACT FROM AUTHOR]

The development of industry 4.0 has put forward higher requirements for modern milling technology. Monitoring the degree of milling tool wear during machine tool processing can improve product quality and reduce production losses. In the machining process of machine tools, many kinds of tools are usually used, and the signal characteristics of various sensors of different tools are different. Therefore, before the tool wear assessment, this paper identified the tool type according to the spindle current data. After the tool type recognition, this paper evaluates the tool wear degree according to the tool force data, vibration data, acoustic emission signal, and other multi-sensor data. Firstly, the Elman neural network and Adaboost algorithm are combined to construct the Elman_Adaboost strong predictor. Then, the variance and mean of seven sensor data were selected as the characteristic quantities to input the strong predictor. Finally, three wear quantities were obtained to measure the wear degree of the tool. The method proposed in this paper is implemented by Matlab, and the validity of this method is verified using the competition data provided by PHM (Prognostics and Health Management) Society. The results show that the average evaluation accuracy of the same tool wear is more than 92%, and that of the similar tool wear is more than 85%. [ABSTRACT FROM AUTHOR]

The purpose of this study is to use acoustic emission (AE) technology to explore the changes in the interface and mechanical properties of GF/VER composite materials after being treated with NaOH and to analyze the optimal modification conditions and damage propagation process. The results showed that the GF surface became rougher, and the number of reactive groups increased after treating the GF with a NaOH solution. This treatment enhanced the interfacial adhesion between the GF and VER, which increased the interfacial shear strength by 25.31% for monofilament draw specimens and 27.48% for fiber bundle draw specimens compared to those before the GF was modified. When the modification conditions were a NaOH solution concentration of 2 mol/L and a treatment time of 48 h, the flexural strength of the GF/VER composites reached a peak value of 346.72 MPa, which was enhanced by 20.96% compared with before the GF was modified. The process of damage fracture can be classified into six types: matrix cracking, interface debonding, fiber pullout, fiber relaxation, matrix delamination, and fiber breakage, and the frequency ranges of these failure mechanisms are 0~100 kHz, 100~250 kHz, 250~380 kHz, 380~450 kHz, 450~600 kHz, and 600 kHz and above, respectively. This paper elucidates the fracture process of GF/VER composites in three-point bending. It establishes the relationship between the AE signal and the interfacial and force properties of GF/VER composites, realizing the classification of the damage process and characterizing the mechanism. The frequency ranges of damage types and failure mechanisms found in this study offer important guidance for the design and improvement of composite materials. These results are of great significance for enhancing the interfacial properties of composites, assessing the damage and fracture behaviors, and implementing health monitoring. [ABSTRACT FROM AUTHOR]

Determining reliable noise emission values for machinery is key to successfully implement the Sell and Buy Quiet concept. ISO 11202 is a basic noise emission standard to determine the emission sound pressure level of machines outside of special acoustic test rooms (in situ measurements) and enables machinery manufacturers to determine the noise emission data of their products within their own premises. However, a recent amendment to this standard was made on the basis of an unsatisfactory amount of experimental data. Therefore, this paper systematically examines the validity and accuracy of the amended part of the method. It answers the question, whether the amendment represents an improvement of the existing method. Measurements on a model machine with two configurations allow for an extensive investigation of the effects of the amendment. To that end, the emission sound pressure levels at eight positions near the machine are determined in three different acoustic environments. One finds that the amendment leads to an overestimation of the local environmental correction for the L p A , which, in turn, could lead to an underestimation of the determined emission sound pressure level. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Applied Sciences focusing on ultrasonic modeling for non-destructive testing (NDT) of materials. The issue covers a range of topics including the feasibility of using innovative NDT methods on various materials, acoustic/elastic bulk waves propagation and scattering, Lamb waves in plates, and imaging for medical and engineering applications. The papers in the issue present novel achievements in ultrasonic modeling methods for NDT inspections and provide insights into the advancements in the field. The document emphasizes the importance of ultrasonic NDT techniques in evaluating the properties and damage states of structures in different applications. [Extracted from the article]

With the widespread advent of digital technologies, traditional perspectives in rock mechanics research are poised for further expansion. This paper presents a Brazilian test conducted on granite after non-steady-state thermal disturbance at 25 °C, 200 °C, 400 °C, and 600 °C, with detailed documentation of the damage process and failure response using an acoustic emission (AE) apparatus and a digital image correlation (DIC) system. Subsequently, utilizing point cloud data captured by a three-dimensional (3D) laser scanning system, a digital reconstruction of the failed specimen's fracture surface was accomplished. The 3D fractal characteristics and roughness response of the digitized fracture surface were studied using the box-counting method and least squares approach. Furthermore, texture information of the digitized fracture surface was calculated using the Gray Level Co-occurrence Matrix (GLCM), and statistical characteristics describing the elevation distribution were analyzed. The results elucidate the influence of thermal disturbance temperature on the mechanical parameters of the specimen, acoustic emission behavior, surface strain field evolution, and digital fracture morphology characteristics. The findings indicate a non-linear degradation effect of temperature on the specimen's tensile strength, with a reduction reaching 80.95% at 600 °C, where acoustic emission activity also peaked. The rising thermal disturbance temperature inhibited the crack initiation load at the specimen's center but expanded the high-strain concentration areas and the growth rate of horizontal displacement. Additionally, varying degrees of linear or non-linear relationships were discovered between thermal disturbance temperature and the 3D fractal dimension of the fracture surface, average roughness (Ra), peak roughness (Rz), and root mean square roughness (Rq), confirming the potential of Rsm in predicting the 3D fractal dimension of Brazilian test fracture surfaces. The study of the GLCM of the digitized 3D fracture surface demonstrated a high dependency of its four second-order statistical measures on thermal disturbance temperature. Finally, the statistical parameters of the fracture surface's elevation values showed a significant non-linear relationship with thermal disturbance temperature, with a critical temperature point likely existing between 400 and 600 °C that could precipitate a sudden change in the fracture surface's elevation characteristics. [ABSTRACT FROM AUTHOR]

In this paper, a MEMS piezoresistive ultrathin silicon membrane-based strain sensor is presented. The sensor's ability to capture an acoustic emission signal is demonstrated using a Hsu–Nielsen source, and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the authors' knowledge, this makes the developed sensor the first known piezoresistive strain sensor which is capable of recording low-energy acoustic emissions. The improvements to the nondestructive evaluation and structural health monitoring arise from the sensor's low minimum detectable strain and wide-frequency bandwidth, which are generated from the improved fabrication process that permits crystalline semiconductor membranes and advanced polymers to be co-processed, thus enabling a dual-use application of both acoustic emission and static strain sensing. The sensor's ability to document quasi-static bending is also demonstrated and compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, making it a novel and useful method for nondestructive evaluations. The potential benefits include an enhanced sensitivity, a reduced sensor size, a lower cost, and a reduced instrumentation complexity. [ABSTRACT FROM AUTHOR]

Increasing the diameter of the drillhole can facilitate drillhole breakage using soundless chemical demolition agents, but it is prone to cause drillhole blowout, resulting in crushing failure. This paper conducted a blowhole prevention test on a large borehole using the internal insertion cooling pipe method (ICBPM) to test the expansion pressure of cooling pipes with different diameters. During this test, a fracture occurred in a hole with a 75 mm inner diameter in the rectangular sandstone specimens with high strength. It was found that utilizing the ICBPM can effectively hinder the development of blowholes. Expansion and blowhole prevention are optimized with a 0.14 mass ratio of the cooling water to demolition agent and a maximum expansion stress of 49.0 MPa. The guiding effect of the minimum resistance line is significant. In repeated tests, all fissures are distributed in a Y-shape on the free surface where the minimum resistance line is located. The acoustic emission signals from statically fractured hard rock increase abruptly before damage, and the development of rock expansion and fracturing can be obtained through strain monitoring. These results suggest that the ICBPM can reduce the expansion time with a strong crushing effect, satisfying the need to process more crushing projects. [ABSTRACT FROM AUTHOR]

The physical and mechanical properties of rocks change significantly after being subjected to high temperatures, which poses safety hazards to underground projects such as coal underground gasification. In order to investigate the effect of temperature on the macroscopic and microscopic properties of rocks, this paper has taken sandstone as the research object and conducted uniaxial compression tests on sandstone specimens at different temperatures (20–1000 °C) and different heating rates (5–30 °C/min). At the same time, the acoustic emission (AE) test system was used to observe the acoustic emission characteristics of the rock damage process, and the microstructural changes after high temperature were analyzed with the help of a scanning electron microscope (SEM). The test results show that the effect of temperature on sandstone is mainly divided into three stages: Stage I (20–500 °C) is the strengthening zone, the evaporation of water and the contraction of primary fissures, and sandstone densification is enhanced. In particular, the compressive strength and elastic modulus increase, the macroscopic damage mode is dominated by shear damage, and the fracture micromorphology is mainly brittle fracture. Stage II (500–600 °C) is the transition zone, 500 °C is the threshold temperature for the compressive strength and modulus of elasticity, and the damage mode changes from shear to cleavage damage, and the sandstone undergoes brittle–ductile transition in this temperature interval. Stage III is the physicochemical deterioration stage. The changes in the physical and chemical properties make the sandstone compressive strength and modulus of elasticity continue to decline, the macroscopic damage mode is mainly dominated by cleavage damage, and the fracture microscopic morphology is of a more toughness fracture. The effect of different heating rates on the mechanical properties of sandstone was further studied, and it was found that the mechanical properties of the rock further deteriorated under higher heating rates. [ABSTRACT FROM AUTHOR]

Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades. [ABSTRACT FROM AUTHOR]

This paper presents the authors' multi-channel measurement systems designed and built to conduct research on partial discharge phenomena using the acoustic emission method. The systems provide real-time monitoring, recording of signals and analysis of recorded signals. The analysis is carried out in time, frequency, time-frequency and discrimination threshold domains. In particular, a descriptor with the ADC acronym is defined, which ranks the signals according to the so-called degree of advancement. Studies have been carried out, showing that for a single partial discharge source, when tested in parallel, using the electrical and acoustic emission methods, the ranking of the signals using this descriptor is identical to the ranking according to the value of the apparent charge introduced by sources. This paper presents the authors' patented method of partial discharge location and identification in power oil transformers. The results of tests of power oil transformer at the test station, conducted in parallel with the electric method and the authors' method, and the results of tests in three selected transformers carried out during ongoing in-situ operation using the authors' method are presented. Based on these results, the authors make diagnoses of the condition of the insulation systems in the tested transformers. The inspections of these transformers confirm the diagnoses. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technology has the advantage of online localization to study the change laws of AE in the process of coal spontaneous combustion and to reveal the generation mechanisms of AE signal during the process of heating and rupture of coal body from a microscopic perspective. This paper first constructs a large-scale coal spontaneous combustion AE test system and conducts experimental tests on the AE signal in the process of coal spontaneous combustion. The results show that with the increase of temperature in the process of coal spontaneous combustion, the AE signal shows a trend of increasing fluctuations. Low-temperature nitrogen adsorption experiments studied the pore structure of coal spontaneous combustion, and the results showed a correspondence between the development of pores and the temperature of coal spontaneous combustion. Further, through the analysis of the evolution of the pore structure of coal through Fourier transform and fractal theory, it is found that the high-frequency main frequency AE signal and average frequency are continuously enhanced with the increase of temperature. The fractal dimension of the pore structure and the fractal dimension of the AE count of the coal body first rise and then decline. The mechanism of coal spontaneous combustion AE of coal is revealed, and the pore development caused by thermal stress when coal heats up is the main source of AE signal generation. The research in this paper is of great significance for applying AE technology to detect the position of coal spontaneous combustion. [ABSTRACT FROM AUTHOR]

This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies. [ABSTRACT FROM AUTHOR]

Featured Application: The Frequency Weighting Matrix Beamforming Algorithm proposed in this paper has the potential to locate leakages in large structures such as spacecraft, and the algorithm can adapt the characteristics of leaks. Clashes between space debris and spacecraft in orbit may cause air leakages, which pose a substantial danger to the crew and the spacecraft. Lamb wave dispersion in spacecraft structures and the randomness of leak holes are the difficulties in leak location. To solve these problems, a frequency weighting matrix beamforming algorithm is proposed in this paper. The elastic Lamb waves that are caused by leakages are acquired by an 'L' shaped sensor array consisting of eight acoustic emission sensors. The angle of a leak can be obtained through the superposition of different time delays, and the intersection of two angles can be used to find the location of the leak. Traditional beamforming is improved by matching the wave speeds in different frequency bands and weightings according to the energy distribution. Narrowband filtering is used to delay overlay different signal speeds with different frequency bands via a dispersion curve. The weighting method is used to compensate the frequency band response of different leak holes. The detailed location algorithm process is introduced and verified by experiments. For 1.5 and 2 mm leak holes, location direction accuracies of 1.33° and 1.93° for one sensor array were obtained, respectively. [ABSTRACT FROM AUTHOR]

Drilling pressure relief is a widely recognized and applied key technique to reduce the risk of rock burst occurrence, and its field-applied effects are also obvious. In this paper, the values of micro parameters required for discrete element method (DEM) simulations were first determined through indoor experiments and parameter calibration. Then, drilling pressure relief numerical simulations were conducted with different drilling diameters, drilling angles, and drilling depths. The results show that as the depth and diameter of the borehole increase, the peak strength of the coal sample first rapidly decreases, and then slowly decreases. The 30 mm depth and 14 mm diameter of the borehole are, respectively, the critical thresholds for the transition of the peak strength decrease rate; as the drilling angle increases, the peak strength of the coal sample first rapidly decreases and then increases, and the drilling angle of 25° is the critical threshold for the peak strength transformation. At the same time, the type of coal sample failure also changes from conjugate shear fracture to single slope shear fracture with the increase of three drilling parameters; shear and tensile cracks are the main types of micro fractures in coal samples. The research results are of great significance for improving the pressure relief plan of coal seam drilling and preventing and controlling rock burst disasters. [ABSTRACT FROM AUTHOR]

The occurrence of rock burst hazards is closely related to the brittleness of rocks. Current research has paid less attention to the in-depth relationship between rock brittleness and acoustic emission (AE) signal characteristics and precursor information caused by rock fracture. Therefore, in order to further improve the accuracy of the AE monitoring of rockburst hazards, uniaxial compression tests were carried out and AE were monitored for rocks with different brittleness (yellow sandstone, white sandstone, marble, and limestone) in this paper. The relationship between the mechanical properties and the time-frequency characteristics of the AE was analyzed. In addition, the multi-fractal theory was introduced to further deconstruct and mine the AE signals, and the multi-fractal characteristics of AE from rocks with different brittleness were investigated. The results show that the stronger the brittleness of the rock, the higher the main frequency and main frequency amplitude of the AE. Brittleness is positively correlated with the multi-fractal parameter Δα (uniformity of data distribution) and negatively correlated with Δf (frequency difference between large and small data). In addition, the dynamics of Δα and Δf provide new indicators for AE monitoring of rock stability, and their abrupt changes can be regarded as precursors of failure. The weaker the brittleness of the rock, the earlier the failure precursor is and the more significant it is. This has potential engineering application value, which can help identify rockburst precursors and take timely protective measures to ensure engineering safety. [ABSTRACT FROM AUTHOR]

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection. [ABSTRACT FROM AUTHOR]

Acoustic Emission (AE) is a non-destructive evaluation method that uses transient elastic waves produced by the sudden release of mechanical energy in a material or structure. This method generates multiple AE events during testing; therefore, it is important to develop parameters that capture the characteristics of each event (AE hit). The paper introduces new dimensionless parameters to characterize the waveform of AE signals: Earliness, Transitoriness, and Early Transitoriness. The study shows that these parameters provide an accurate description of AE waveforms, in some respects, better than traditional parameters, which makes them suitable for filtering with simple rules or in combination with machine-learning techniques. Two examples of the application of AE hit filtering from sedimentation and soil compression experiments are provided. In the sedimentation test analysis, the proposed parameters were used with K-means clustering to filter AE hits from outside the zone of interest and to calculate the rate of sedimentation. In the compression test of a sand sample under oedometric conditions, a simple filtering rule was applied to discriminate AE hits from unwanted sources and obtain a clear AE energy cumulative curve. In both cases, the dimensionless parameters have shown the capacity to discriminate between different AE sources and paths and the possibility of filtering hits from unwanted sources. [ABSTRACT FROM AUTHOR]

In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper studies the decay law of low-temperature crack resistance performance of rubber powder basalt fiber composite-modified porous asphalt concrete (CM-PAC) under medium- and high-temperature water erosion. Firstly, the prepared Marshall specimens were subjected to water erosion treatment at different temperatures of 20 °C, 40 °C, and 60 °C for 0–15 days. Then, the processed specimens were subjected to low-temperature splitting tests, and acoustic emission data during the splitting test process were collected using an acoustic emission device. It can be seen that the low-temperature splitting strength and low-temperature splitting stiffness modulus of CM-PAC gradually decrease with the increase in water erosion time. The maximum reduction rates of the two compared to the control group reached 72.63% and 91.60%, respectively. The low-temperature splitting failure strain gradually increases. Under the same erosion time, the higher the temperature of water, the more significant the amplitude of changes in the above parameters. In addition, it is shown that as the water erosion time increases, the first stage of loading on the specimen gradually shortens, and the second and third stages gradually advance. As the water temperature increases and the water erosion time prolongs, the acoustic emission energy released by the CM-PAC specimen during the splitting process slightly decreases. The application of acoustic emission technology in the splitting process can clarify the changes in the failure pattern of CM-PAC specimens during the entire loading stage, which can better reveal the impact of medium- to high-temperature water on the performance degradation of CM-PAC. [ABSTRACT FROM AUTHOR]

In addition to measuring the strain, stress, and Young's modulus of materials through tension and compression, in-plane shear modulus measurement is also an important part of parameter testing of composites. Tensile testing of ±45° composite laminates is an economical and effective method for measuring in-plane shear strength. In this paper, the in-plane shear modulus of T800 carbon fiber/epoxy composites were measured through tensile tests of ±45° composite laminates, and acoustic emission (AE) was used to characterize the damage of laminates under in-plane shear loading. Factor analysis (FA) on acoustic emission parameters was performed and the reconstructed factor scores were clustered to obtain three damage patterns. Finally, the development and evolution of the three damage patterns were characterized based on the cumulative hits of acoustic emission. The maximum bearing capacity of the laminated plate is about 17.54 kN, and the average in-plane shear modulus is 5.42 GPa. The damage modes of laminates under in-plane shear behavior were divided into three types: matrix cracking, delamination and fiber/matrix interface debonding, and fiber fracture. The characteristic parameter analysis of AE showed that the damage energy under in-plane shear is relatively low, mostly below 2000 mV × ms, and the frequency is dispersed between 150–350 kHz. [ABSTRACT FROM AUTHOR]

This work is an introduction of acoustic emission (AE) signals used in order to detect the malfunction of selected semiconductor elements. The authors proposed the use of internally generated signals (elastic waves) of acoustic emission leading to the detection of the pre-fail state of switching IGBT transistors. The analysis of the AE signals allows the creation of a reference pattern of properly working transistors and at the same time the identification of abnormal signals, which are generated by a defective element. Unlike many papers, this article shows experimental results demonstrating a comparison of undamaged, properly working and defective IGBT transistors which can be used, for example, as a reference for diagnostic tools. Analysis of the signal in the frequency domain obtained from the faulty transistor (overheated or with damaged casing) shows the presence of additional frequencies which can indicate the imminent occurrence of critical damage. [ABSTRACT FROM AUTHOR]

The impact acoustic emission (AE) of plate structures is a transient stress wave generated by local materials under impact force that contains the state information of the impacted area. If the impact causes damage, the AE from material damage will be superimposed on the impact AE. Therefore, this paper details the direct extraction of damage-induced AEs from impact AEs for the health monitoring of plate structures. The damage-induced AE was analysed based on various aspects, including the cut-off range and propagation speed characteristics of the Lamb wave mode, the correlation between the force direction and the Lamb wave mode, and the impact damage process. According to these features, the damage-induced AE wave packets were extracted and verified via impact tests on epoxy glass fibreboards. The results demonstrated the feasibility of the proposed method for determining whether an impact causes damage via the direct extraction of the damage-induced AE from the impact AE. [ABSTRACT FROM AUTHOR]

Dynamic failure of surrounding rock often causes many casualties and financial losses. Predicting the precursory characteristics of rock failure is of great significance in preventing and controlling the dynamic failure of surrounding rock. In this paper, a triaxial test of granite is carried out, and the acoustic emission events are monitored during the test. The fractal characteristics of acoustic emission events' energy distribution and time sequence are analyzed. The correlation dimension and the b value are used to study the size distribution and sequential characteristics. Furthermore, a rock failure prediction method is proposed. The correlation dimension is chosen as the main index and the b value is chosen as a secondary index for the precursor of granite failure. The study shows that: (1) The failure process can be divided into an initial stage, active stage, quiet stage, and failure stage. (2) The b value and correlation dimension both can describe the process of rock failure. There is a continuous decline before failure. Because of the complexity of the field, it is difficult to accurately estimate the stability of surrounding rock using a single index. (3) The combination of the b value and correlation dimension to establish a new method, which can accurately represent the stability of the surrounding rock. When the correlation dimension is increasing, the surrounding rock is stable with stress adjusting. When the correlation dimension is decreasing and the b value remains unchanged after briefly rising, the surrounding rock is stable, and stress is finished adjusting. When the correlation dimension and b value are both decreasing, the surrounding rock will be destroyed. [ABSTRACT FROM AUTHOR]