Search Results (26)

To elucidate the mechanisms responsible for the low strength, pronounced variability, and long-term deformation of carnallitite, and to provide a basis for stope parameter design in deep potash mines, two carnallitite seams from a potash mine were investigated. Group C carnallitite and Group D halite-dominated rock salt were subjected to short-term compression tests and multiscale comparative analyses, while Groups A and B carnallitite specimens were tested under multistage creep loading. Particle Flow Code (PFC) simulations were conducted to evaluate the influence of particle size distribution. The results indicate the following: (1) The representative Group C specimens exhibited an average uniaxial compressive strength of 7.83 MPa, which was substantially lower than that of Group D. The acoustic emission (AE), scanning electron microscopy (SEM), and computed tomography (CT) analyses revealed greater heterogeneity in damage evolution and failure behavior, mainly associated with polymineralic composition, weak particle–matrix interfaces, local pores, and insufficient particle connectivity. (2) Particle-scale heterogeneity influenced the load-bearing capacity of carnallitite. In the PFC sensitivity analysis, narrowing the prescribed particle-size-distribution range from 0.4–8.0 mm to 4.0–4.0 mm at a mean particle size of 4.0 mm was associated with an increase in simulated strength from 7.82 to 10.40 MPa. Because quantitative contact-network descriptors were not extracted, the corresponding contact-network interpretation is treated as mechanistic rather than direct quantitative evidence. (3) The long-term uniaxial strengths of Groups A and B were estimated as 3.3 MPa and 4.8 MPa, respectively, using the adopted specific-failure-energy method. The modified Burgers model provided a good fit to the creep data within the tested stress levels, yielding coefficients of determination of 0.957 and 0.964 and root-mean-square error (RMSE) values of 0.0803 and 0.0552 percentage points. Based on the long-term strength constraints and the site-specific design assumptions adopted in this study, the calculated inter-room pillar widths were 6 m for Group A and 4 m for Group B. These findings provide insights into the multiscale damage mechanisms and long-term stability assessment of carnallitite stopes in deep potash mines. Full article

Acoustic emission (AE) monitoring is widely used to characterize coal failure, but specimens with different burst tendencies cannot be distinguished reliably using a single count, energy, b-value, or fractal indicator. This study reanalyzed archived Vallen AE data from uniaxial-compression tests on five strong-burst, five weak-burst, and three specimen-matched non-burst coal specimens. Thirteen VisualAE event tables were verified against independently decoded primary-data files; hit counts were identical and cumulative-energy differences were below 1%. Vallen C and c records were identified as transmitted and received calibration pulses and were excluded consistently from the physical-AE analysis. Calibration records contributed mean energy shares of 11.1%, 4.3%, and 92.5% in the strong-, weak-, and non-burst groups, respectively. After exclusion, the top 1% of retained events contributed 96.6%, 97.9%, and 66.6% of the AE energy; mean b-values at Ht + 5 dB were 0.788, 0.793, and 1.650; and raw-energy multifractal widths were 1.933, 1.729, and 1.219. The correlation dimension depended strongly on embedding, delay, and scaling-range choices and did not show a universal late-sequence decrease. An exploratory six-component AE multi-parameter index yielded group means of 0.659, 0.798, and 0.150. The results support complementary, explicitly parameterized AE sequence descriptors, while the non-burst sample size (n = 3), absence of strict machine-AE time synchronization, and field-scale transfer requirements limit generalization. Full article

This study investigates the texture of table olives by integrating mechanical, acoustic, and sensory approaches, with particular emphasis on the role of acoustic emission in the perception of crunchiness, a key quality attribute influencing consumer acceptance. Three Italian cultivars (Itrana Bianca, Nocellara del Belice, and Bella di Cerignola), processed using different debittering methods, were analyzed using needle and tip compression tests combined with simultaneous sound recording, as well as Texture Profile Analysis (TPA) and Kramer shear press, alongside sensory evaluation by a trained panel. Needle compression primarily characterized peel properties, identifying Itrana Bianca as having the firmest skin, while tip and Kramer tests, reflecting both peel and pulp, indicated that Bella di Cerignola was the hardest and most consistent sample. TPA revealed that Itrana Bianca exhibited higher elasticity and cohesiveness, whereas Bella di Cerignola showed greater firmness and chewiness. Nocellara del Belice consistently displayed the softest texture. Acoustic measurements suggested that sound emission was mainly associated with peel rupture rather than pulp characteristics and showed limited correlation with sensory crunchiness. Overall, the combined mechanical–acoustic approach provides a more comprehensive evaluation of olive texture and highlights the need for refined sensory descriptors, particularly for peel-related attributes, to better interpret acoustic responses. Full article

This research presents a robust multivariate statistical framework for the non-destructive prediction of geomechanical state parameters in quartz-rich coastal sands through acoustic emission (AE) monitoring. Granular media under symmetrical compressive stress function as complex natural systems, where microscopic energy dissipation—arising from particle rearrangement and grain microcracking—radiates as transient elastic waves. To decode these stochastic processes, 24 confined uniaxial compression tests were conducted across diverse soil typologies and moisture contents (0–12%). A high-dimensional data matrix was constructed, integrating 13 geotechnical variables with 48 acoustic descriptors formulated through three distinct temporal aggregations: stage-specific, history average and weighted history average. The statistical results identify the logarithmic effective vertical stress (${\log }_{10}\left({{\mathsf{\sigma }}^{\prime }}_{\mathrm{v}}\right)$) and the cumulative axial strain ($\mathsf{\epsilon }$) as the most significant geomechanical drivers, exhibiting Pearson correlation coefficients $\left|p\right|$ ≥ 0.85 with acoustic activity. In the acoustic domain, the analysis reveals that Signal Strength (${\mathrm{S}}_{\mathrm{s}}$) and cumulative energy ($\mathrm{E}$) flux are the most reliable predictors for volumetric deformation, while the amplitude ($\mathrm{A}$), b-value ($\mathrm{b}$), and average frequency ($\mathrm{F}$) emerge as critical indicators for identifying the transition between spatial rearrangement and the onset of grain fragmentation. Furthermore, the inclusion of dimensionless parameters, particularly earliness ($\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{l}$), enhances model stability by standardising waveform symmetry across varying stress regimes. High-order polynomial regression models (up to the third degree) were derived, demonstrating that the statistical complexity of acoustic signatures allows for the high-fidelity inference of the soil matrix’s initial and state parameters. This methodology establishes a unified mathematical architecture for the in situ characterisation of granular skeletons, balancing computational efficiency with predictive power in intricate geological domains. Full article

Accurate damage characterization in thermoset Carbon Fiber-Reinforced Polymer (CFRP) composites using Acoustic Emission (AE) requires statistically robust and interpretable models. This study employs multinomial logistic regression with forward selection and Type III analysis to identify the minimal set of AE parameters necessary for classifying damage mechanisms (fiber breaks, delamination, matrix cracks) in quasi-isotropic thermoset CFRP laminates under synchronously recorded load conditions. Starting from 18 conventional time- and frequency-domain descriptors, forward selection yielded seven candidate predictors. However, Type III analysis revealed that only four parameters, Load, Initiation Frequency, Amplitude, and Average Frequency, provide unique, statistically significant contributions (p < 0.05). The remaining predictors became redundant once these four were included. Machine learning and deep learning models trained on this minimal feature set achieved validation accuracies up to 98.7% on external specimens. High-frequency components (>1 MHz), as recorded at the sensor location after propagation and sensor convolution, were associated with fiber break events at elevated loads, while delamination events exhibited higher amplitude and lower-frequency content (<200 kHz) compared to matrix crack events. These observed frequency ranges reflect the combined effects of source mechanisms, guided wave dispersion in the 2.4 mm thick laminate, PWAS sensor response, and HDT-based hit segmentation, and are consistent with established AE damage signatures in literature. The results indicate that this four-parameter set is sufficient to classify the labeled AE waveform classes under monotonic tensile loading of quasi-isotropic [45/90/−45/0]_2s laminates, achieving 98.7% agreement with reference labels assigned via waveform morphology and spectral analysis. The proposed approach reduces computational overhead and enhances interpretability for structural health monitoring applications, pending validation across broader material systems and loading scenarios. A limitation of this study is that reference labels were assigned using waveform morphology and spectral analysis, lacking independent physical validation (e.g., microscopy). Full article

This study evaluates the feasibility of acoustic signal analysis using different spectrographic transformation methods as a tool for assessing the quality of welding beads produced through the Shielded Metal Arc Welding (SMAW) process. Acoustic emissions were recorded during manual welding operations under controlled experimental conditions, using E6013 electrodes on A36 carbon steel plates. From the acoustic recordings of 400 welding samples, previously classified as accepted or rejected, two fundamental acoustic descriptors were extracted: the fundamental frequency (F₀) and the harmonic-to-noise ratio (HNR). These were analysed using parametric and non-parametric metrics to evaluate their discriminative capability. In addition, multiple supervised classifiers were trained and validated using stratified eight-fold cross-validation. The proposed framework enables a systematic comparison of different signal transformations and classification models for the evaluation of SMAW welding quality. Among the evaluated models (SVC, Gradient Boosting, and Extra Trees), precision rates of 90–95% were observed using Spectral Contrast, MEL, and CQT transformations. The results demonstrate that the implementation of various acoustic signal-based models and transformations for welding inspection offers a scalable and cost-effective solution for industrial quality control. Full article

Unidirectional glass fiber-reinforced thermoplastic (UGFT) composite tapes are promising recyclable structural materials for applications such as composite pressure pipes. However, their durability under hydrothermal environments remains a critical concern. This study emphasizes metrology-driven evaluation of aging behavior in polypropylene-based UGFT tapes. Specimens were conditioned at 95 °C in a deionized-water environment for up to 4 weeks, and multiple complementary measurement techniques were applied to quantify degradation. Mass-change metrology was performed to characterize water uptake kinetics and establish diffusion-driven aging progression. Tensile testing enabled quantitative assessment of mechanical strength retention, defining a >25% reduction in strength as a threshold for significant deterioration. Acoustic emission (AE) acted as the central non-destructive monitoring method, capturing high-fidelity waveforms generated during loading. AE waveform descriptors, such as amplitude, rise time, and frequency content, served as measurable indicators of internal damage mechanisms including matrix cracking, interfacial debonding and fiber breakage. To process large AE datasets, principal component analysis was used for dimensionality reduction, followed by k-means clustering to group signals by damage type. Optical microscopy provided microstructural verification of these classifications. The integrated metrological framework demonstrates a reliable pathway to monitor, identify, and quantify damage evolution in hydrothermally aged UGFT structures. Full article

Recent advancements in composite materials design have made glass fiber-reinforced polymer composites (GFRPC) a viable choice for a wide range of engineering and industrial applications. Although GFRPCs boast attractive characteristics such as low specific mass and high specific mechanical strength, identifying and characterizing damage mechanisms in these materials is challenging. Several scientific studies have examined the root causes of GFRPC failure using various methods, including non-destructive techniques and learning algorithms. Despite this, ongoing investigations aim to accurately detect mechanical defects in GFRPCs. This study explores the use of non-destructive testing (NDT) combined with unsupervised learning algorithms to identify and classify damage mechanisms in GFRPCs. The NDT method employed in this study is acoustic emission (AE), which identifies waveforms associated with various failure mechanisms during testing. These waveforms are categorized using unsupervised learning methods such as principal component analysis (PCA) and self-organizing maps. PCA selects the most appropriate AE descriptors for distinguishing between different damage mechanisms, while the self-organizing maps algorithm performs clustering analysis and classifies failure mechanisms. Scanning electron microscope images of the observed failures are provided to sup-port the findings derived from AE data. Full article

The present experimental study assessed the viability of utilizing an acoustic emission signal as a monitoring instrument to predict the chemical characteristics of wine throughout the alcoholic fermentation process. The purpose of this study is to acquire the acoustic emission signals generated by CO₂ bubbles to calculate the must density and monitor the kinetics of the alcoholic fermentation process. The kinetics of the process were evaluated in real time using a hydrophone immersed in the liquid within the fermentation tank. The measurements were conducted in multiple fermentation tanks at a winery engaged in the production of wines bearing the Rioja Denomination of Origin (D.O.) designation. Acoustic signals were acquired throughout the entirety of the fermentation process, via a sampling period of five minutes, and stored for subsequent processing. To validate the results, the measurements obtained manually in the laboratory by the winemaker were collected during this stage. Signal processing was conducted to extract descriptors from the acoustic signal and evaluate their correlation with the experimental data acquired during the process. The results of the analyses confirm that there is a high linear correlation between the density data obtained from the acoustic analysis and the density data obtained at the laboratory level, with determination coefficients exceeding 95%. The acoustic emission signal is a valuable decision-making tool for technicians and winemakers due to its sensitivity when describing variations in kinetics and density during the alcoholic fermentation process. Full article

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. Full article

This research investigates the influence of the pre-existing defects within concrete taken from the in-service irrigation structure on the strain distribution. The X-ray Computed Tomography (CT) technique is employed to investigate the internal concrete matrix and evaluate the defect distribution in it. The cracking system in a concrete matrix is detected as a damage type caused by the severe environment, and it is varied by the different degrees in all samples. The geometric properties of defects and their spatial location are obtained by image processing of CT images. The compression test with Acoustic Emission (AE) and Digital Image Correlation (DIC) measurements is conducted to analyze the fracture processes and acquire the damage spatial information. The AE signal descriptors are effective parameters for real-time detection and potential local damage monitoring. Moreover, the analysis of the DICM strain and displacement fields reveals the most potential fracture zones. The AE source location analysis indicated a connection between pre-existing defects and strain localization. The AE events and strain are high in the defect areas. Additionally, the amplitude and frequency of the AE events correlated with the location of the defects indicating that the structure weakness at that point leads to concentrated deformation development. Full article

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. Full article

A multiparameter approach is preferred while utilizing Acoustic Emission (AE) technique for mechanical characterization of composite materials. It is essential to utilize a statistical parameter, which is independent of the sensor characteristics, for this purpose. Thus, a new information–theoretics parameter, Lempel–Ziv (LZ) complexity, is used in this research work for mechanical characterization of Carbon Fibre Reinforced Plastic (CFRP) composites. CFRP specimens in plain weave fabric configurations were tested and the acoustic activity during the loading was recorded. The AE signals were classified based on their peak amplitudes, counts, and LZ complexity indices using k-means++ data clustering algorithm. The clustered data were compared with the mechanical results of the tensile tests on CFRP specimens. The results show that the clustered data are capable of identifying critical regions of failure. The LZ complexity indices of the AE signal can be used as an AE descriptor for mechanical characterization. This is validated by studying the clustered signals in their time–frequency domain using wavelet transform. Finally, a neural network framework based on SqueezeNet was trained using the wavelet scalograms for a quantitative validation of the data clustering approach proposed in this research work. The results show that the proposed method functions at an efficiency of more than 85% for three out of four clustered data. This validates the application of LZ complexity as an AE descriptor for AE signal data analysis. Full article

In this study, we investigated defect detection in dissimilar welded joints by the acoustic emission (AE) method. The study objects were carbide and decarburized interlayers, which are formed at the fusion boundary between austenitic and pearlitic steels. Diffusion interlayers, as a structural defect, usually have microscopic dimensions and cannot be detected using conventional non-destructive testing (NDT) methods. In this regard, the AE method is a promising approach to diagnose metal objects with a complex structure and to detect microscopic defects. In this paper, the AE signatures obtained from testing defect-free specimens and specimens with diffusion interlayers are analyzed. We found that the AE signature for defective and defect-free welded joints has significant differences, which makes it possible to identify descriptors corresponding to the presence of diffusion interlayers in dissimilar welded joints. Full article

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. Full article