Break Tests Research Articles

Deep learning-based acoustic emission source localization in heterogeneous rock media without prior wave velocity information

Abstract Acoustic emission (AE) source localization is crucial for monitoring but often relies on prior information, such as wave velocity and arrival time. This study introduces a novel method for locating AE sources in rocks without such information, addressing challenges posed by heterogeneous sensor arrays. Experiments involving pencil led break (PLB) tests on sandstone cubes collected AE waveforms and their coordinates. A ResNet-50 based deep learning model was developed to correlate the time-frequency spectra of AE with PLB locations, expressed as spatial Gaussian distributions. The model, achieved a 79% prediction accuracy for AE localization in complex environments. While there is room for improvement in training data quantity and diversity, the results validate the model’s effectiveness, particularly in coal mines and tunnel engineering.

A break test for the tail-event correlation matrix based on the self-normalization method

A break test for the tail-event correlation matrix based on the self-normalization method

Production of Bio-plasticized Polyvinyl Chloride (PVC) Films Synthesized from Microalgae (Chlorella vulgaris)

Abstract Bio-based plasticizers for PVC films are being explored to replace conventional phthalate-based plasticizers due to the threats they pose to human health and the environment. In the present study, fatty acids from Chlorella vulgaris were extracted via Microwave-assisted Extraction and were epoxidized using esterification and transesterification reactions in a magnetically stirred reactor to produce EFAME plasticizer. FTIR Analysis determined the presence of an epoxy group as the peak C=O stretch (1739.79 cm−1) was detected. PVC films were synthesized at different concentrations of EFAME (30%, 40%, and 60%). At 40% of EFAME, the PVC exhibited high displacement in the tensile strength and elongation at break test, which was used as a basis to produce PVC-A, PVC-B, and PVC-C, and was compared to phthalate-plasticized PVC film, PVC-D. SEM-EDX Analysis verified the presence of PVC and EFAME in the plasticized PVC films as Carbon, Oxygen, and Chlorine elements were detected. PVC A had the lowest weight loss percentage at 40.96% indicating it has developed high migration resistance among the extraction mediums used. DSC analysis confirmed the plasticizing effect of EFAME as the glass transition temperature of bioplasticized PVC films decreased. Using One-way ANOVA, the physicochemical tests of PVC films plasticized with EFAME showed significantly better results than PVC films plasticized by DOP. Thus, the present study indicates that the bio-based plasticizer derived from C. vulgaris is a better and a potential alternative for phthalates in plasticizing PVC films.

The Influence of Coupling-Specific Parameters and Structural Parameters on the Electromechanical Impedance of Acoustic Emission Sensors

Acoustic emission (AE) sensors are widely used for testing and monitoring purposes, necessitating effective field-testing methods to assess their functionality during use, ideally resulting in a method for self-diagnostic. A novel approach for field-testing is to measure the electromechanical impedance (EMI) of the AE sensor. This method requires minimal additional equipment and eliminates human interaction, which is needed for current field-tests like pencil-lead break tests. However, the EMI includes the mechanical impedances of the sensor, the coupling layer, and the structure. Therefore, the measurements contain information about all three and influences of the structure and the coupling layer need to be investigated to be able to establish a method for self-diagnosis in field. Additionally, the piezoelectric effect is temperature-dependent, which affects the measured EMI spectra and may result in erroneous assessments of the sensor's condition if not accounted for. This work presents experimental studies on the influence of measuring equipment, environmental temperature, coupling-specific parameters and structural parameters on the EMI of AE sensors. Moreover, numerical simulations have been conducted to provide additional support and to extend the experimental studies of the latter two. To ensure reproducible sensor coupling to a structure, a setup was constructed to couple the sensor to the structure at the same position and with the same force. The thickness of the coupling layer is determined by a small amount of silicon carbide particles with a specific diameter. This setup is also used to examine the effect of structural parameters.

Experimental and numerical investigation of the sensor effect on the acoustic emission during Pencil-lead Break tests on PMMA plates

Pencil leads are broken on PMMA plates at various source-sensor distances to evaluate the influence of the propagation distance and the sensor type on AE signals. The resulting waves are detected using a broadband or a resonant sensor. The ability of five transducers, PKBBI sensor, MICRO80 sensor, MICRO200HF sensor, Nano30 sensor, and WD sensor, to recreate the characteristic forms of plate waves is evaluated. Because of the response spanning between 300 to 400 kHz frequency range, Nano30 and MICRO80 sensors have a bigger magnitude in the S0 mode than in the A0 mode, especially for small sensor-source distances. Their different responses demonstrate why similar test specimens and test settings can provide diverse findings due to the sensor effect, which is significant for the use of AE data in the classification of AE sources. Then, based on the normalization method, we suggest a procedure to acquire equivalent values for the selected descriptors using Laplacian Score and Principle Component Analysis. In order to gain a thorough understanding of the sensor effect through 3D finite element simulation, a comparative analysis is conducted between the perfect contact sensor and the resonant sensor with sensor effect. The sensor effect arises from the convolution of the Fourier Transform of the signal with the sensitivity curve in the frequency domain. By comparing the waveforms in the time and frequency domain, it is useful to determine how the different sensors differ from the AE signals.

Acoustic Emission simulation: assessing the influence of AE source modeling and addressing the issue of dimensionality in the propagation medium

A thorough understanding of simulating Acoustic Emission (AE) requires the source modeling and propagation medium to be considered respectively. Crack initiation and propagation simulation as an AE source can be performed by the Successive Node Release (SNR) method and the Direct Node Release (DNR) method. Both are compared to show how the source model affects the AE signals. The SNR method is commonly used to simulate the crack initiation and propagation. It is necessary to consider the influence of the crack velocity profile on the crack initiation [1]. However, for the DNR method, all nodes are instantaneously released once the critical crack length is attained. Hence, it is not necessary to consider the velocity profile. Instead, it is sufficient to define the average crack velocity. However, the kinetic energy varies when employing different methods, leading to a variance in the source energy. We thus assess the suitable conditions to apply the DNR method to represent crack initiation and investigate the influence of the node release methods on the AE signals. Secondly, a comparative assessment of the Pencil-lead Break test on PMMA plates is conducted using both two-dimensional (2D) and three-dimensional (3D) finite element simulations. This analysis is valuable for understanding the deficiencies and constraints of the 2D simulation. However, there is a significant difference in the AE signals between 2D and 3D simulations, suggesting that the 2D simulation is unable to capture the AE information during wave propagation, in spite of its lower computational cost. Therefore, to obtain extensive and precise information from the PLB test, it is crucial to have an extensive understanding of the limits of the 2D simulation. This understanding should take into account factors such as the distance between the sensor and the source, as well as the descriptors chosen to characterize the AE.

A Hybrid GAN-Inception Deep Learning Approach for Enhanced Coordinate-Based Acoustic Emission Source Localization

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies.

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids.

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.

A Proposed Non-Destructive Method Based on Sphere Launching and Piezoelectric Diaphragm.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The pencil-lead break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection.

Analysis of different test techniques of DC circuit breaker

Abstract Amidst the escalating demand for energy and the proliferation of renewable energy sources, direct current (DC) systems have garnered increasing interest. As pivotal for the safety and reliability of DC systems, testing DC circuit breakers is a critical step to evaluate their performance and dependability. This study focuses on the breaking test method for medium and high-voltage DC circuit breakers. It compares and evaluates the current waveform, recovery voltage, and energy dissipation across various test circuit methodologies during the breaking process of DC circuit breakers and assesses the suitability of test methods across different voltage levels.

Hospital referrals, exclusions from hospital care, and deaths among long-term care residents in the Community of Madrid during the March–April 2020 COVID-19 epidemic period: a multivariate time series analysis

BackgroundFrom March 7 to April 7, 2020, the Community of Madrid (CoM), Spain, issued interventions in response to the COVID-19 epidemic, including hospital referral triage protocols for long-term care facility (LTCF) residents (March 18–25). Those with moderate to severe physical disability and cognitive impairment were excluded from hospital referral. This research assesses changes in the association between daily hospital referrals and the deaths of LTCF residents attributable to the triage protocols.MethodsDaily hospital referrals and all-cause mortality from January to June 2020 among LTCF residents and the CoM population aged 65 + were obtained. Significant changes in LTCF resident daily hospital referrals time series, and in-LTCF and in-hospital daily deaths, were examined with tests for breaks and regimes in time series. Multivariate time series analyses were conducted to test changes in the associations between LTCF resident hospital referrals with daily deaths in-hospital and in-LTCF, and in the CoM population aged 65 + when the triage protocols were implemented.ResultsAmong LTCF residents, hospital referrals declined sharply from March 6 to March 23, 2020. Increases in LTCF residents' daily deaths occurred from March 7 to April 1, followed by a decrease reaching pre-epidemic levels after April 28. The daily ratio of in-hospital deaths to in-LTCF deaths reached its lowest values from March 9 to April 19, 2020. The four versions of the triage protocol, published from March 18 to March 25 had no impact on further changes in the association of hospital referrals with daily deaths of LTCF residents in-hospital or in-LTCF.ConclusionsWhile LTCF residents’ deaths increased, hospital referrals of LTCF residents decreased with the introduction of the CoM governmental interventions on March 7. They were implemented before the enactment of the triage protocols, protecting hospitals from collapse while overlooking the need for standards of care within LTCFs. The CoM triage protocols sanctioned the existing restrictions on hospital referrals of LTCF residents.

One Man’s Bubble Is Another Man’s Rational Behavior: Comparing Alternative Macroeconomic Hypotheses for the US Housing Market

Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a neural network model as an atheoretical non-linear approach to capture the relative importance of alternative macro variables, we show that these hypotheses generate different macro relevance. As an alternative to testing housing time series, we focus on bubble identification being hypothesis dependent. Model forecast errors (residuals) identify the potential presence of bubbles through standardized residual CUSUM tests for structural breaks. By testing for housing bubbles from these unstructured models, we generate conclusions on the presence of bubbles prior to the Great Financial Crisis and the post-pandemic periods. Competing macro hypotheses or narratives will generate different conclusions on the presence of bubbles and create bubble identification issues.

Do bitcoin electricity consumption and carbon footprint exhibit random walk and bubbles? Analysis with policy implications

One of the main current focuses of global economies and decision-makers is the efficiency of energy utilization in cryptocurrency mining and trading, along with the reduction of associated carbon emissions. Understanding the pattern of Bitcoin's energy consumption and its bubble frequency can greatly enhance policy analysis and decision-making for energy efficiency and carbon emission reduction. This research aims to assess the validity of the random walk hypothesis for Bitcoin's electricity consumption and carbon footprint. We employed both traditional methods (ADF and KPSS) and recently proposed unit root techniques that account for structural breaks and non-linearity in the data series. Our analysis covers daily data from July 2010 to December 2021. The empirical results revealed that traditional unit root techniques did not confirm the stationarity of both bitcoin's electricity consumption and carbon footprint. However, novel structural break (SB) and linearity tests conducted enabled us to discover five SB episodes between 2012 and 2020 and non-linearity of the variables, which informed our application of the newly developed non-linear unit root tests with structural breaks. With the new methods, the results indicated stationarity after accommodating the SB and non-linearity. Furthermore, based on Phillips and Shi (2019)'s test, we identified certain bubble episodes in the bitcoin energy and carbon variables between 2013 and 2021. The major drivers of the bubbles in bitcoin energy consumption and carbon footprint are variables relating to the bitcoin and financial markets activities and risks, including the global economic and political risks. The study's conclusion based on the above findings informs several policy implications drawn for energy and environmental management including the encouragement of green investments in cryptocurrency mining and trading.

Comparison of the Performance of Structural Break Tests in Stationary and Nonstationary Series: A New Bootstrap Algorithm

AbstractStructural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

Electromechanical Impedance of Acoustic Emission Sensors used for Self-Diagnosis

Acoustic Emission (AE) is a well established NDT method which is used more and more for continuous monitoring, heading towards SHM. Today, AE sensors in the field are tested with a pencil lead break test (Hsu-Nielsen source), which requires manual interaction. Sensors are replaced if the sensitivity is too low. However, since the test is performed on the monitored structure, the structure itself may influence the test. Therefore, a detailed check of the sensor in the lab, according to SE02 and ISO 24543, is done afterwards. An automated solution, that can test AE sensors in the field, without human interaction, that needs minimal additional hardware would benefit AE based monitoring systems. The electromechanical impedance (EMI) spectrum is mostly used for local damage detection with piezoelectric transducers. In the past it has been shown that the EMI spectrum can also be used to enable automated self-diagnostic capabilities in the low frequency spectrum (Park et al., 2004) and in the higher frequency regime (Mueller, 2017) including the eigenfrequencies of the transducers. The usage of EMI for AE sensors can, thorough research in this matter provided, solve the issues mentioned above. This work presents results of EMI spectrum analysis of two sets of 12 and 17 sensors, respectively, with different levels of damage from pressure vessel burst tests. The results are compared to that of a face-2-face inspection, which is an established method of verification in the lab according to ISO 24543. It is shown that the EMI spectrum is capable of similar self-diagnostic capabilities. Thus, combining the advantages of global monitoring with AE sensors, while providing a remote and automatic method of self-diagnostic of AE sensors at any point with minimal additional hardware and software requirements.

Performance evaluation of the SMART passive safety injection system against the SBLOCA scenario with the SMART-ITL facility

A set of system performance tests has been performed and the thermal-hydraulic behaviors were investigated for the passive safety injection system (PSIS) of the SMART design. Major thermal-hydraulic characteristics of core cooling in reactor coolant system and safety injection (SI) in PSIS were investigated using the SMART-ITL. The parametric effects of break size, break location and train number on the PSIS performance were discussed with the relevant SMART-ITL data. Compared with the 2.0 inch SI line break test, the 0.4 inch SI line break test has a milder change of system parameters. The pressure is reduced more quickly, the decrease of water level is slower and the break mass flow rate is higher during the pressurizer safety valve line break than during the SI line break. Major thermal-hydraulic parameters were compared among single, two and full train operation of the PSIS focusing on the effect of train number. It was shown that multiple trains of CMT and SIT were operated independently and the reactor vessel (RV) inventory increased proportionally with the addition of each train. Sufficient SI water was injected into the RV in a passive manner to cool down the reactor core efficiently during the full train test.

Relationship Between Twitter Sentiment Analysis and Bitcoin Prices: Econometric Analysis of Long and Short Term Dynamics

The significance of social media in influencing cryptocurrency pricing has grown considerably in recent years. This study aims to explore the correlations between social media sentiments and Bitcoin pricing, both in the short and long terms, while also investigating the direction of these relationships. Sentiment analysis was conducted using the TextBlob model, which uncovers the underlying meaning in text through analysis. The study tested the hypothesis that there exists a relationship between sentiment analysis scores and Bitcoin prices over both short and long periods. Ensuring stationarity was crucial for time series analysis, involving the use of structural break and traditional unit root tests. Daily data from June 2021 to June 2022 was examined, with December 2021 serving as the focal point due to a peak in Bitcoin prices. The study focused on Bitcoin price data and sentiment analysis scores. Results revealed Twitter data as the dependent variable, showing no long-term relationship with Bitcoin prices. However, a significant and positive relationship was observed in the short term. This research contributes valuable insights into the intricate dynamics between social media sentiments and cryptocurrency pricing.

Alternative monetary policies and renewable energy stock returns

The aim of this study is to determine how monetary policy interacts with the financial sector specialising in renewable energy, especially since the implementation of Quantitative Easing (QE). Using EU data and the VAR approach incorporating the interest rate, representing monetary policy, an index of renewable energy stock prices, oil prices, technology and the VIX, this paper applies Granger causality, generalised impulse response functions and historical variance decompositions to explain this interaction. To account for the changes in monetary policy such as QE, structural break tests have been used to determine their effects on the variables, as the breaks correspond to the main QE events, the data has been divided into subsamples to reflect the possible differing effects of QE. The results suggest that monetary policy has only a limited effect overall on renewable energy stocks, as the long recent period of alternative monetary policies has been found not to influence renewable energy stock prices. More time-disaggregated analysis undertaken by incorporating structural breaks identifies a significant influence during some time periods depending on the type of monetary policy being conducted.

Rebalancing agri-food trade flows due to Russian import ban: the case of direct neighbors

In recent years, research on trade rebalancing in agri-food supply chains has gained prominence due to trade sanctions, supply chain disruptions, and vulnerabilities exposed by pandemics and conflicts. This study focuses on the recalibration of agri-food trade dynamics, using the 2014 Russian import ban as a case study. The ban significantly altered the structure of agri-food export destinations for affected countries, particularly those sharing a border with Russia (e.g., Poland, Finland, Estonia, Latvia, Lithuania). Employing a cross-section regression model and structural break tests, we assess the trade rebalancing process. Our findings reveal short-term trade rebalancing effects, primarily observed in product groups not traditionally considered main trade specializations. There is evidence that significant part of the lost trade was redirected to EU28 or EAEU countries and end up in the common trade areas of these countries. Furthermore, we argue that the Russian import ban initiated a long-term structural shift in export patterns for non-traditionally traded banned products, while rebalancing for traditionally traded products was significantly quicker.

Fracture characteristics and energy dissipation law of shale under water-based impact loads

Shale fracture and energy dissipation are the basis of reservoir fracturing effect evaluation. The damage and fracture mechanism of shale under dynamic impact load is the key scientific concern of reservoir fracturing. To study the law of fracture, fracture propagation and energy dissipation of shale under dynamic impact, dynamic tests of water-based impact load under different impact velocities were carried out using impact rock breaking test platform based on air cannon. Topological and fractal theory, Weibull distribution model, space vector geometry method and Griffith fracture mechanics theory were used to systematically study the effects of impact velocity on fracture development and expansion, fragment mass and size distribution, damage degree of abutment residual and energy dissipation transformation. Scanning electron microscopy (SEM) was used to observe the micromorphology of the impact fracture surface. The results demonstrated that an increase in impact velocity led to the formation of a network fracture featuring main fractures, cavity fissure, bedding fractures, and crushing zones, thereby enhancing the fracture’s capability to penetrate bedding. The apparent fracture network connectivity, topology structure and fractal dimension were positively correlated with impact velocity. The residual height of the shale decreased, resulting in an escalation of the damage degree from mild (0.43) to severe (0.75), while the average penetration angle decreases from 81.00° to 63.52°. The area of new surface experiences rapid initial growth followed by a more gradual increase, with dissipative energy showing a more pronounced increase compared to crushing energy. The micro-fracture mode underwent an evolution from brittleness to toughness and a transition from low energy consumption to high energy consumption. The propagation, reflection, and superposition of stress waves within the rock mass gave rise to a complex stress zone, ultimately causing the rock mass to break. These findings lay the groundwork for developing a stress-damage-energy-failure combined model for shale subjected to dynamic impact loads, thereby enhancing the development of intricate networks in shale reservoir and improving the effective extraction of shale gas.