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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]

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]

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]

An original partial discharge generated in oil insulation identification methodology based on simultaneously conducted measurements using electrical method, ultra high frequency method and acoustic emission method is presented in the paper. Three different partial discharge model sources as well as measuring instruments commonly applied for partial discharge detection in electrical power transformers are yielded within a laboratory research. Total of 45 scenarios, including proposed spark gap configurations, selected supply voltage levels and UHF frequencies are analyzed during measurements series. Furthermore, form among total of 93 descriptors assigned for every applied partial discharge model source configuration there are 24 proposed as potentially useful for partial discharge identification applications with their 95% confidence bounds. Attempt of discriminative descriptors selection for partial discharge source analysis in on-site transformer applications as well as a proposal of unique descriptors according to every selected spark gap configuration that could be potentially useful for partial discharge identification purposes are the main purpose of the presented paper. The proposed methodology verification on a real life transformer with particular consideration of the selected descriptors potential utility in the fields of partial discharge detection and identification in electrical power industry applications confirmed a proposed methodology usefulness. [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]

Paper is a composite of fibers, air and additives where the fiber/fiber joints keep the network structure together. A study was undertaken to establish the link between the properties of the contact zone between fibers and paper performance under mechanical loading. The contact zone between fibers was investigated using light microscopy. A staining technique was developed for evaluating the influence of surface charge on fiber/fiber joint strength. The joint strength was linearly correlated with paper tensile strength and with the average amplitude of the acoustic events measured by acoustic emission testing. The fiber surface conformability was improved by changing the surface charge. This resulted in increased fiber/fiber joint strength as the relative contact area became larger. Increasing the molecular adhesion in the contact zone with the aid of strength additives also improved the fiber/fiber joint strength. [ABSTRACT FROM AUTHOR]

The article presents the results concerning the use of clustering methods to identify signals of acoustic emission (AE) generated by partial discharge (PD) in oil-paper insulation. The conducted testing featured qualitative analysis of the following clustering methods: single linkage, complete linkage, average linkage, centroid linkage and Ward linkage. The purpose of the analysis was to search the tested series of AE signal measurements, deriving from three various PD forms, for elements of grouping (clusters), which are most similar to one another and maximally different than in other groups in terms of a specific feature or adopted criteria. Then, the conducted clustering was used as a basis for attempting to assess the effectiveness of identification of particular PD forms that modelled exemplary defects of the power transformer's oil-paper insulation system. The relevant analyses and simulations were conducted using the Matlab estimation environment and the clustering procedures available in it. The conducted tests featured analyses of the results of the series of measurements of acoustic emissions generated by the basic PD forms, which were obtained in laboratory conditions using spark gap systems that modelled the defects of the power transformer's oil-paper insulation. [ABSTRACT FROM AUTHOR]

Cracking in dryers has long been a challenge in the pulp and paper industry. The acoustic emission (AE) technique, which is capable of supplying information regarding the cracking behaviour of metal objects, can be used to reduce or prevent dryer failure. This study applies AE testing to a paper dryer made of grey cast iron in China. To realise this task, the AE characteristics of the cast iron dryer were examined by accomplishing three main work parts: (i) in the lab part, basic AE signatures from the dryer shell material were characterised; (ii) in the artificial crack test part, AE features from the crack were obtained and the AE location of the crack in the dryer was tested; (iii) in the field part, the basic procedure for testing the paper dryer under hydrostatic test conditions was verified based on the analysis of parts I and II. The results indicate that grey cast iron is a good AE emitter. The tensile test revealed that the AE responses are active and the AE sources include the deformation of pearlite, decohesion and fracture of several graphite flakes and microcrack growth. The AE attenuation in the dryer is not severe and the cracks in the dryer during hydraulic testing exhibit an active AE response. Additionally, an accurate crack location was obtained by using the cylindrical location method. These results can provide useful information for AE inspection on structures made of cast iron. [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]

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]

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]

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]

Results of a comparative analysis of two non-dimensional coefficients, calculated based on signals emitted by partial discharges (PD) are presented in the paper. The PD were generated using setups for modeling of three basic types of PD sources, which can occur in paper-oil insulation in power transformers. The following measuring methods were considered: electrical, ultra-high frequency, acoustic emission, and optical spectrophotometry. All measurements, for each of the PD-source configuration using the various methods, were conducted simultaneously in a laboratory under same measurement conditions. Based on the gathered results it was stated that it is possible to recognize the partial discharges form based on the non-dimensional coefficients, which differ in value for all of the considered methods except for the acoustic emission. The main contribution of the research performed lies in the simplicity of the applied signal processing procedure, which can be adopted to a device or system for diagnosis of partial discharges occurring in paper-oil insulation. [ABSTRACT FROM AUTHOR]

Frequent mining operations significantly disturb the security of deep weakly cemented rock roadways in western mining areas, constituting one of the primary causes of deformation, instability, and failure within the coal‐rock body. In this paper, dynamic uniaxial compression tests of soft rock‐coal combinations under low‐frequency disturbance with different cyclic amplitudes were conducted based on acoustic emission to elucidate fracture modes. The findings are as follows: Different cycle amplitudes manifested significant degradation effects on the soft rock‐coal combination. With increasing cycle amplitude, the proportion of tensile cracks initially decreased before subsequently increasing, demonstrating a general upward trajectory. The sudden increase in the acoustic emission RA value, the large decrease in the b‐value, and AE counts reaching the peak mean that failure and destabilization of the specimen begin to occur. The results of this study will furnish theoretical direction for dynamic disaster monitoring and early warning in soft rock mines located in western mining regions. [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]

Highlights In this paper, the mechanical properties, damage modes, and bolt load distribution of the bonded‐bolted hybrid three‐bolt joint were investigated by a combination of experiments and numerical simulations. Acoustic emission (AE) was used to monitor the tensile process of the connection in real time, and the K‐means clustering algorithm was used to realize the cluster analysis of the damage modes. A numerical simulation model of the tensile process of the joint was established using ABAQUS simulation software. The progressive damage evolution of composite laminates, and adhesive layer was characterized by using the VUMAT subroutine, the B‐K failure criterion, and cohesive element with zero thickness, respectively. The results show that four main damage modes occur in composite laminates during tension: matrix cracking, delamination, fiber pull‐out, and fiber breakage, with corresponding peak frequency ranges of (0–80 kHz), (80–210 kHz), (210–330 kHz), and (330–375 kHz), respectively. The adhesive layer between the laminates mainly exhibits peeling failure because of the secondary bending effect. In the load‐bearing process, the adhesive layer was loaded first and failed, and the bolt loading was delayed. Meanwhile, the results of bolt load distribution showed that the bolts and the adhesive layer at both ends carried greater loads. Damage modes and damage evolution of the bonded‐bolted hybrid joint in tension were analyzed by combining acoustic emission technology and numerical simulation. The simulation of delamination damage in composite laminates was carried out by inserting zero‐thickness cohesive elements into the laminates, and the delamination damage between the carbon fiber layup and the adjacent glass fiber layup was analyzed. The effect of the inclusion of the adhesive layer on the mechanical properties of the joint and the distribution of the bolt load was analyzed. [ABSTRACT FROM AUTHOR]

The method for identifying train axle fatigue cracks based on acoustic emission technology requires the classification and processing of acoustic emission signals. However, existing methods for recognizing axle fatigue crack acoustic emission signals, including parameter analysis, wavelet analysis, and traditional machine learning algorithms, usually rely on expert experience, resulting in low detection efficiency. Additionally, the traditional machine learning algorithms above typically have shallow structures, which limits their ability to extract deeper feature information. Therefore, this paper proposes a method that uses a one-dimensional convolutional neural network (1D-CNN) to identify train axle fatigue crack acoustic emission signals. This method achieves end-to-end intelligent recognition of acoustic emission signals, eliminating the need for manual intervention or pre-set feature extractors. Furthermore, it leverages deep learning mechanisms to deeply mine the complex features within the signals, thereby enhancing the accuracy of the model’s identification. Experimental validation was conducted using the acoustic emission signals collected from the axle fatigue crack test rig. The results demonstrate that this model can accurately identify acoustic emission signals of axle fatigue cracks, achieving an identification accuracy of over 99%. Meanwhile, compared with other network models, the effectiveness of the proposed model is further verified. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation in which acoustic emission (AE) wave was generated through a pencil lead break as a point source on two pipeline sections made of mild steel and titanium. The pipelines (bare, epoxy phenolic coated) were of same length but had two different diameters and wall thicknesses. The recorded AE signals were analysed using time and frequency domain signals, energy levels and wavelet transform to explore time-frequency features for the identification of wave modes. It is concluded that the damping behaviour of coating restricts the peaks of waves, decreases the decay time of waves and reduces the energy level in the coated pipeline. It is concluded that monitoring of coated pipeline wave propagation could be done effectively with sensor placement directly on the pipeline surface compared to sensor placement on the coating surface. A high wall thickness of pipeline results in a higher number of reflected waves and increases the decay time of waves. The coating on pipeline resists wave propagation, and different densities of coating layer and pipeline affect the velocity of the wave. This experimental work advance towards designing an alternative way to monitor changes in pipeline structures (e.g., corrosion under insulation applications). [ABSTRACT FROM AUTHOR]

In 2022, the authors presented the first stage of implementing a revised climate control strategy at the National Gallery of Victoria, monitoring collection object response in collaboration with the Getty Conservation Institute using acoustic emissions (AE) monitoring. The research and education, motivating factors, and interdepartmental alignment required for adopting the Bizot Green Protocol (BGP) were discussed, as well as the experimental design and set-up of the AE system on a large Flemish altarpiece in an active gallery. This second and concluding paper presents the results of the AE monitoring study along with energy used by the HVAC system for the gallery housing the AE monitoring study, during the transition from traditional environmental parameters through to broadened parameters as defined in the BGP. The project highlights the importance of understanding individual gallery and building behaviour as well as the HVAC system in place. In the case of the gallery housing the AE study, the implementation of BGP parameters resulted in smaller environmental fluctuations than expected. That HVAC energy efficiency could be improved with a relatively low impact on the gallery environment was an unexpected but welcome outcome. This case study describes some common challenges faced when considering such a change operationally, including resourcing and communication needs. Identifying points of resistance, framing problems clearly, and identifying common goals were essential to making progress. In this case, sustainability has compelled many stakeholders into alignment and enabled a shift in institutional culture. [ABSTRACT FROM AUTHOR]

This research paper focuses on the thermo-hydro-mechanical (THM) modeling of brine migration in a heated borehole test conducted as part of the ongoing Brine Availability Test in Salt (BATS) at the Waste Isolation Pilot Plant (WIPP) in New Mexico. It is a component of the international collaboration project DECOVALEX-2023 (DEvelopment of COupled models and their VALidation against EXperiments), which aims to understand the THM processes governing brine flow in heated rock salt repositories through collaborative analysis by multiple research teams. Using the TOUGH–FLAC simulator, THM simulations were performed and compared with data from the BATS phase 1a. This experiment involved two identical horizontal-borehole arrays, one heated and one serving as a control, both equipped with sensor arrays. Analysis of measurements revealed water flow rate surges during heater power transitions, with the highest jump observed during cooling. Acoustic emission activity exhibited distinct patterns in response to heater power changes, suggesting that damage to rock salt is particularly pronounced during the cooling phase. The THM simulations successfully captured these phenomena, highlighting the significance of thermal effects, brine migration, and mechanical behavior in predicting brine availability in heated and damaged rock salt. Our modeling also revealed the critical interplay between heating and cooling-induced damage and its influence on flow properties, particularly affecting brine inflow estimation. Notably, we found that cooling-induced brine inflow spikes result from increased permeability due to tensile dilatancy. These findings have important implications for the development of robust containment strategies and enhance our understanding of the complex processes involved in repository performance. Highlights: Numerical modeling study accurately replicates the thermal-hydromechanical behavior of rock salt surrounding a heated borehole. Jumps in brine flow rate during transitions in heater power are observed, with the highest jump during cooling. Acoustic emission patterns suggest pronounced damage to rock salt during the cooling phase. Thermal pressurization and tensile-induced damage are identified as the primary contributors to brine inflow spikes. Findings improve predictions of brine availability, aiding the development of robust containment strategies for radioactive waste disposal. [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]

Acoustic emission (AE) can monitor and evaluate the cracking situations of hydraulic structures for it is real-time and non-destructive. However, a current challenge of this approach is to accurately identify the various stages during the cracking process. The innovation of this paper lies in the application of AE data in analyzing the stage characteristics during the whole process of hydraulic concrete fracture. First, the slope changing of the cumulative ringing count curve was used to determine the cracking stages of hydraulic concrete. Second, the function of inter-event times (denoted as F-function) was used to further investigate the AE characteristics proximity to the final fracture. Last, the spatial-temporal-energy evolution of cracks was studied. The results show that: (1) The whole process of hydraulic concrete failure shows an obvious stage characteristic from the perspective of AE feature and it can be distinguished to five stages by the slope change of the cumulative ringing count curve; (2) The F-function can be used to identify the last two cracking stages, and it is capable of early warning the macroscopic cracking in ahead of b-value; (3) The spatial-temporal-energy distribution of cracks helps to better understanding the generation and evolution for cracks inside hydraulic concrete. [ABSTRACT FROM AUTHOR]

Because of the inhomogeneity and anisotropy of glass fibre reinforced plastics (GFRP), it is always a challenge to detect the real-time dynamic quality during manufacturing and service. In this paper, a virtual loading focusing enhancement technology (VLFET) based on time reversal (TR) theory is proposed to accurately locate the acoustic emission (AE) source in the GFRP. First, the focusing enhancement theoretical model with virtual reverse loading is deduced according to the TR theory. Then, after measuring the optimal detection range and the average acoustic velocity, the sensor array is positioned on the GFRP specimen. The excitation signals applied to simulate the AE source at some positions are produced by using lead breaking, and the detection signals are received by the sensor array. Finally, the signals received by the sensor array are processed according to the theoretical model, and the fluctuation image in the monitoring area is reconstructed with fluctuation amplitude of each pixel to evaluate the AE source position. The experimental results demonstrate that the excitation sources can be accurately evaluated with the VLFET. The location error with the proposed method is less than 3.1%, higher than that from the current AE instrument. The proposed technology can also be applied to the AE evaluation of other anisotropic materials. [ABSTRACT FROM AUTHOR]

This paper presents an accurate model for predicting different payload weights from 3DR SOLO drone acoustic emission. The dataset consists of eleven different payload weights, ranging from 0 to 500 g with a 50 g increment. Initially, the dataset's drone sounds are broken up into 34 frames, each frame was about 5 s. Then, Mel-spectrogram and VGGish model are employed for feature extraction from these sound signals. CNN network is utilized for classification, and during the training phase, the network's weights are iteratively updated using the Adam optimization algorithm. Finally, two experiments are performed to evaluate the model. The first experiment is performed utilizing the original data (before augmentation), while the second used the augmented data. Different payload weights are identified with a potential accuracy of 99.98%, sensitivity of 99.98%, and specificity of 100% based on experimental results. Moreover, a comprehensive comparison with prior works that utilized the same dataset validates the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Highlights Fiber‐reinforced polymers (FRP) feature high strength‐to‐weight ratio amongst the emerging class of natural composites. This paper presents the impact of the glass fiber on the tensile strength of the flax epoxy laminate. The mechanical behavior and damping characteristics of flax fiber reinforced polymer (FFRP) and the hybrid flax‐glass fiber reinforced polymer (HFRP) are experimentally investigated. Both the flax and hybrid FRPs are made using vacuum infusion process. The specimens without and with holes of 4, 5, and 6 mm in diameter are subjected to tensile test using acoustic emission monitoring and free vibration test. In the former testing, HFRP resulted in higher peak frequencies and cumulative counts. Also, the natural frequency and damping factor of HFRP vary proportionately with the hole size, as identified in the latter tests. Different damage mechanisms during the tensile test revealed that the presence of glass fibers in HFRP increased resistance for certain damage mechanisms. Adding glass fiber to flax fiber reinforced polymer (FFRP) creates a hybrid FRP (HFRP) with increased resistance to damage and improved damping characteristics. HFRP exhibits a distinction in failure mechanisms compared to FFRP. The study utilizes acoustic emission (AE) to identify various damage mechanisms in the material, providing a more detailed information. The addition of glass fibers in HFRP leads to a more pronounced increase in damping factors. [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]

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]

Thermal–mechanical damage is an important problem threatening the safety of deep rock engineering. In this paper, the effects of coupling damage on the deformation and failure characteristics of rock mass were studied via cyclic loading damage, thermal damage and uniaxial compression acoustic emission (AE) tests, and the microscopic fracture process of the damaged rock mass was numerically simulated. Results showed that during heat treatment, the colour of the sample changed significantly, the mass, the P-wave velocity and the number of mineral species decreased. The peak strength and elastic modulus reach their maximum values at 600 °C and 300 °C, respectively, exhibiting a trend of initial increase and subsequent decrease. The rapid growth period of AE activity increased noticeably with increasing temperature, and the effect of energy accumulation became more significant at higher peak strengths. The failure mode was influenced primarily by the cyclic loading amplitude. In addition, an increase in the stress or temperature after crack initiation leads to a sharp increase in the damage within the rock. Temperature had a more significant effect on the generation of damage than stress. Stress-induced microcracks were concentrated in the weakly bonded particles, whilst temperature-induced microcracks were concentrated in the strongly bonded particles. Highlights: Temperature had a significant effect on the mineral composition of rock mass. The rapid growth period of AE activity was prolonged with the temperature increase. The failure mode of rock mass was mainly influenced by cyclic loading amplitude. Temperature-induced microcracks were concentrated in strongly bonded particles. [ABSTRACT FROM AUTHOR]

Location of damage sources is an important aspect of structural health monitoring research. Acoustic emission (AE) technology is broadly concerned due to its potential advantages in damage monitoring and source localization. However, the traditional positioning method is based on the arrival of P wave, and the non-uniformity of concrete materials is not considered, resulting in poor accuracy at large distances. This paper describes a non-contact AE localization method using leaky Rayleigh waves via a new air-coupled MEMS microphones array unit. Compared with traditional contact detection, this method is convenient for rapid setup and monitoring in a wider range. The feasibility of the non-contact AE localization method was verified by numerical simulation and experiments. Azimuth (direction of arrival) of AE source is a key source parameter for damage location. The research shows that this method can determine the azimuth of AE source at different positions, the results are close to the actual coordinates. Non-contact monitoring method proposed in this paper is the basis for further research on the failure prediction of concrete plate-like structures such as tunnel lining and bridge deck. [ABSTRACT FROM AUTHOR]

Rock cores retrieved from deep rock masses may be permanently damaged on account of the stress release occurring during the drilling process. The quantification of core damage is important for accurate evaluation of the rock strength at depth. This paper presents a laboratory simulation of the core drilling process under high in situ stress conditions. The study aims to quantitatively evaluate sampling damage from both macro and micro perspectives using mechanics tests and microscopic observations. The observed decrease in core specimen integrity with increasing sampling confining pressure is in line with practical engineering findings, thus validating the experimental method utilized in this paper. The test results show that the porosity of the core increases and P-wave velocity decreases as the sampling confining pressure increases, which indicates that the structure of the core is significantly changed by the sampling damage. The stress–strain curve exhibited increased nonlinearity and acoustic emission events at the initial stage of loading, indicating an increase in sampling damage with rising sampling pressure. The uniaxial compressive strength and Brazilian tensile strength of the cores drilled at 40 MPa are reduced by about 21% and 28%, respectively, compared to the undamaged rock, indicating that the effect of core damage on the rock strength cannot be ignored. Microstructural observations further reveal that the crack density of the cores drilled at 40 MPa is four times higher than that of the intact rock. Additionally, the development of microcracks shows obvious directionality, with microcracks preferentially developing perpendicular to the drilling direction. Stress path analysis of the cores indicates that the sampling damage may be primarily caused by tensile stresses. This study provides a better understanding of the mechanisms underlying sampling damage and core disking. [ABSTRACT FROM AUTHOR]

Recent optical time-resolved experiments on single supported nano-objects (gold nanodisks with various diameter over thickness ratios) have demonstrated a marked enhancement of their vibrational quality factors for specific nano-object morphologies, resulting from the near-suppression of radiative vibrational damping associated with the emission of acoustic waves in the nano-object environment. This paper clarifies the origin of this phenomenon, which is ascribed to the creation of a "quasi-bound state in the continuum" vibrational mode by radiative coupling between two nano-object modes whose frequencies become close for specific nano-object shapes. The symmetry breaking induced by the presence of a substrate, which limits nanodisk acoustic emission to a half-space, is shown to play an essential role in enabling such radiative coupling. The impact of the acoustic mismatch between the nano-object and the substrate is explored, and it is shown that a moderate acoustic mismatch can still enable the creation of near-localized vibrational modes with high radiative quality factors, while allowing radiative coupling effects to occur over a broad range of nano-object geometries. Although this paper focuses on the situation of a substrate-supported gold nanodisk, which has already been the object of experimental investigations, the effects that it describes are general and constitute a promising approach to enhance the vibrational quality factors of nano-objects. [ABSTRACT FROM AUTHOR]

This paper addresses classifying different common partial discharge (PD) types under different acoustic emission (AE) measurement conditions. Four types of PDs are considered for the multi-class classification problem, namely; PD from a sharp point to ground plane, surface discharge, PD from a void in the insulation, and PD from semi parallel planes. The collected AE signals are processed using pattern classification techniques to identify their corresponding PD types. The measurement conditions include the influences of various PD locations, oil temperatures, and having a barrier in the line-of-sight between the PD source and the AE sensor. A recognition rate of 94% is achieved when classifying the different PD types measured at the same conditions. In addition, it has been found that the different PD source locations, oil temperatures, and barrier insertion have an impact on the recognition rate. However, by including AE samples at these different conditions in the training process, a recognition rate around 90% for all cases is achieved. [ABSTRACT FROM AUTHOR]

Abstract  In this study, a simplified Acoustic Emission (AE) equipment, in essence an AE signal conditioner and a USB (Universal Serial Bus) data acquisition system, is used to study what happens in paper structures during mechanical loading. By the use of such equipment, some parameters that can be extracted are e.g. the stress and strain at onset of AE, the stress and strain at the onset of rapid AE defined as some numerical factor (larger then one) times the initial emission rate, the emission rate at the first stage of loading and the stress and strain at final failure i.e. when the specimen loses its load carrying ability.In this study however, the interest is focused on one particular parameter i.e. the elastic strain energy density W c at onset of AE. This is a parameter with a clear physical meaning and in this study, the correlation between this parameter and a fracture toughness measure, is investigated.The conclusion is that when nine different paper materials (with a large span regarding properties) are considered, there is a correlation (however not linear) between these two parameters. [ABSTRACT FROM AUTHOR]

Highlights GLARE laminate experiences multiple damage modes during bending failure, including fiber fracture, matrix cracking, interlayer delamination, and fracture of aluminum alloy. In most cases, two or more failure modes occur together, which makes the damage process complex and unpredictable. This paper proposes a combination of experimental testing and numerical simulation to investigate the influence of fiber lay‐up direction and center open‐hole on the bending failure behavior of GLARE laminate. Simultaneously, Acoustic Emission (AE) technology was used to monitor the bending process of GLARE laminates in real‐time, and a numerical simulation model for the three‐point bending of GLARE laminates was established. The experimental results showed that the bending strength of intact specimens decreased significantly with the increase of the fiber off‐axis angle, whereas the strength retention of open‐hole specimens increased significantly with the increase of the fiber off‐axis angle. Based on the K‐means++ clustering algorithm, the peak frequencies corresponding to different damage modes of GLARE laminates were identified as follows: aluminum alloy damage [1–70 kHz], matrix cracking [93–210 kHz], interlaminar delamination [211–304 kHz], and fiber breakage [304–516 kHz]. The simulation results agreed well with the experimental results, showing that the damage in intact specimens initiated at the laminate edges and propagated sequentially along the thickness direction, while the damage in open‐hole specimens initiated at the hole edges and then propagated towards the laminate edges. The present study investigated the effects of fiber lay‐up direction and central open‐hole on the bending performance and failure behavior of GLARE laminate. In‐situ monitoring of the GLARE laminate during three‐point bending was conducted using AE technology. A model for simulating the progressive damage of GLARE laminate was established and its accuracy was verified. [ABSTRACT FROM AUTHOR]

The mechanical behaviour and failure characteristics of weakly filled granite are important factors affecting the safety and stability of rock masses. This paper presents an experimental study on uniaxial compression of granite containing both planar and circular weak filling of different diameters with the aid of three-dimensional digital image correlation (3D DIC) and acoustic emission (AE) monitoring system. The test results show the following: (1) The contact area between the planar weak filling and the bedrock is in a state of severe local deformation. The annular area of the circular weak filling in contact with the bedrock is in a severe deformation area, with detachment from the bedrock and spalling behaviour occurring. The failure modes of the specimens were summarised, and the spatial morphology of the main fracture surface was quantitatively analysed. (2) The presence of planar and circular weak filling leads to increased AE volatility. The AE hit signal is dominated by the shear signal, but the presence of circular weak filling increases the splitting signal component. (3) In the presence of circular weak filling, the energy interval of the most dominant signal is reduced, and the duration is shortened. The differences in the correlation of AE parameters are concentrated in the low-amplitude signal region. (4) Weakly filled areas will first become areas of spatiotemporal accumulation of microfracture events and will gradually spread. The accumulation and distribution characteristics of microfracture events can be used as indicators to evaluate the propensity, severity and timing of the destabilisation of rock masses containing weak filling. [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]

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]

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]

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]

This paper introduces a novel indirect tool condition monitoring (TCM) system for micro-milling brittle materials (glass and silicon) based on acoustic emission (AE) and cutting force signals. The milling experiments are also applied to a ductile material (steel) for comparison. Tool wear calibration is conducted to determine the three tool wear stages. The collected signals are processed in time, frequency, and time-frequency domains. Specific frequency subrange combinations of cutting force signals in three directions after wavelet packet decomposition show strong correlation to the tool wear stages, whilst processed AE signals are the secondary feature to the tool wear. A tool wear prediction model for each material is built based on all the chosen features and back propagation (BP) neural network. The prediction model is simply structured and highly efficient with the highest prediction accuracy of more than 95%. • A novel indirect tool condition monitoring system designed for micro-milling of brittle materials. • TCM system developed using the combination of acoustic emission and force sensors. • Special wavelet packet decomposition method was proposed for data processing. • High-accuracy tool wear prediction model was built and verified. [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]

Aiming at the problem of seepage-stress coupling failure instability of cemented gangue-fly ash backfill (CGFB) with defects, the triaxial compression test and acoustic emission (AE) monitoring test of the CGFB were carried out in this paper. The strength characteristics of defective CGFB under seepage-stress coupling were analysed，the variation rules of AE parameters (ring count, b-value and entropy value) in the failure process were systematically studied. It is found that the AE parameters (ring count, b-value, and entropy value) of backfill with defects under seepage-stress coupling have a 'mutation' phenomenon, which can be used as a precursor feature of failure instability of backfill. Combined the grey-cusp catastrophe theory, a failure prediction model of backfill based on AE parameters (ringing count) was established, and the seepage-stress coupling fracture of backfill with defects was effectively predicted, indicating that the model is feasible to predict the seepage-stress coupled failure of backfill with defects. The research results can provide a theoretical basis for stability monitoring and failure prediction of backfill in deep water-rich mines in China. [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]

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]