Acoustic Emission Method Research Articles

Condition assessment of prestressed concrete channel bridge girders using acoustic emission and data-driven methods

In the United States, a significant proportion of bridges are contending with aging and decay, necessitating reliable inspection methods for ensuring safety and functionality. Traditional visual inspections, while common, are often subjective and labor-intensive. This study proposes an innovative approach using Structural Health Monitoring (SHM) systems, particularly focusing on Acoustic Emission (AE) techniques. AE is chosen for its sensitivity to the early stages of material damage. The research involves flexural tests on six prestressed concrete channel bridge girders from 30-ft span bridges, originally constructed in the 1960s, at the University of South Carolina. These girders were monitored using AE during tests and were previously rated for condition based on the Specifications for the National Bridge Inventory (SNBI). The contribution of this paper is the development of intensity analysis charts from AE data, offering a more objective and comprehensive assessment of girder conditions. These charts, calibrated against theoretical cracking load and cumulative signal strength analysis, are capable of evaluating deterioration irrespective of initial conditions, thereby aiding in determining the condition factor for bridge load rating.

Acoustic emission source localization in complex pipe structure using multi-task deep learning models

Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method).

Investigation on the damage mechanisms using acoustic emission method and damping characteristics of hybrid flax‐glass composites

AbstractFiber‐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.Highlights 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.

Exploring Beishan Granite Damage Patterns: Statistical Analysis of Single and Multiple Loading via Acoustic Emission Techniques.

Using acoustic emission (AE) methods, a research is carried out to examine the failure features of Beishan granite for both loads i.e. continuous and single. The findings demonstrated that the AE signal level increased following dilatancy during single loading but reduced as the tension in the axial direction neared the highest level of tension.AE signals were mostly created during the elastic stage of loading, when loading numerous times, but they were also generated during the failure and residual stages of unloading. In the AE waveforms, three frequency bands were seen in the majority, this was less than 200 kHz in prevailing frequency and less than 80 dB in amplitude. As the loading tension rise, the AE signals' amplitude grew. The AE a- and b-values for the specimens that experienced repeated loads showed that microcracks developed rapidly at the time of the damage stage and helped to generate macrocracks during the residual stage.

Evaluation of solid propellant burning rate by using strand burner and static firing tests

Abstract The main objective of this paper is to compare the different methods used to measure the burning rate of hydroxyl-terminated polybutadiene (HTPB) propellant. The burning rate measurement is carried out under varying crosslinking density, which is determined by the curing ratio, i.e., the equivalent ratio of diisocyanate to total hydroxyl (NCO/OH) ratio. Four batches with different curing ratios were produced using fixed production processes. To measure the burning rate, cured solid propellant strands were tested using the acoustic wave emission method at different pressures ranging from 4 to 10 MPa and also by using static firing test of subscale motors. The study showed that there was little difference between the results obtained from the acoustic wave emission method and subscale motors.

Machine learning-based improvement of crack localization in concrete structures

Acoustic emission (AE) method is useful for damage detection in structures. However, processing the massive amount of AE data with traditional analysis methods is time-consuming. Therefore, machine learning (ML) integration provides a more rational and efficient solution by accelerating the data analysis and improving the damage detection accuracy. This study aims to improve source localization, which provides to identify cracking patterns by signal-based analysis, by training the ML model with the AE dataset for accurate damage diagnosis of concrete structures. Analyzes were conducted on the ML model using AE data collected on a concrete member under laboratory conditions. In this way, the ML model that learned signals with multi-variable wave characteristics and parameters was integrated into the source localization algorithm and thus, AE source location results were enhanced. Furthermore, due to its ability to identify damaged areas, this approach is essential for structural safety by providing early damage detection and accurate localization of cracks. This integration of the machine learning model with AE data will be considered as an advanced tool for the effective monitoring of damages in structures.

Enhancing acoustic emission analysis for corrosion damage detection in reinforced concrete by k-nearest neighbors method

The Acoustic Emission (AE) method is considered a significant tool for detecting damage in reinforced concrete structures caused by reinforcement corrosion. However, processing a large amount of AE data using traditional analysis methods is time-consuming. Therefore, integrating machine learning (ML) techniques into this process offers a more logical and effective solution to expedite data analysis and enhance damage detection accuracy. This study aims to train an ML model for detecting corrosion damage in reinforced concrete structures using an AE dataset. In pursuit of this goal, it seeks to enhance source localization that contributes to identifying crack patterns through signal-based analysis methods. AE data obtained from a reinforced concrete structural element subjected to accelerated corrosion testing in laboratory conditions have undergone detailed analyses with the ML model. In this context, an ML model capable of learning signals with various wave characteristics and parameters has been successfully integrated into the source localization algorithm, significantly improving AE source localization outcomes. Furthermore, this method is critical for structural safety as it enables the early detection of reinforcement corrosion and precise localization of cracks. The integration of this machine learning model with AE data serves as a valuable tool developed for more effective monitoring of damage in structures.

Features of Recording Energy Parameters of Acoustic Emission Signals from Materials

The acoustic emission method is used as a means of analyzing the state of materials and structures, non-destructive testing and diagnosing them during operation. An important advantage over other methods is that it reacts only to the development of truly dangerous defects and is capable of monitoring large volumes and surfaces without scanning them. The article presents the results of the development of methods and algorithms designed to analyze the energy characteristics of acoustic signals. The use of spectral methods is justified.

Acoustic Emission during Non-Uniform Progression of Processes in Composite Failure According to the Von Mises Criterion

In the study, based on the model of acoustic emission during the destruction of a composite material by shear force according to the Von Mises criterion, the effect of non-uniformity of the destruction process on the generated acoustic emission signal is simulated. The study under the accepted modeling conditions allows us to determine the patterns of changes in the amplitude envelope of acoustic emission signals at various stages of developing processes. In theoretical and experimental studies of acoustic emission signals when searching for patterns in their parameter changes and developing methods for monitoring or diagnosing the state of composite materials, the problem lies in the interpretation of recorded information. This issue arises from the complexity and diversity of processes occurring in the material structure at micro and macro levels, and the high sensitivity of the acoustic emission method to these processes, wherein structural changes lead to observable alterations in the characteristics of acoustic emissions. Solving this problem requires both theoretical and experimental studies to understand the influence of various factors on the characteristics of the generated acoustic emission. The results of the presented study can be used to assess the condition of composite materials and structures, such as bridges, e.g., in terms of defectiveness, property dispersion, damage during operation, and other characteristics.

Influence of proportional multiaxial fatigue loading on the residual mechanical properties of glass-reinforced plastic pipes

This work is dedicated to the experimental investigation of multiaxial cyclic loading influence on the residual mechanical properties of glass-reinforced plastic pipes (GRP pipes). Static, cyclic and combined (static after the preliminary cyclic loading) tests have been carried out on tubular specimens under tension with torsion. Residual stiffness curves have been made, their approximation has been carried out, using previously developed model, damage accumulation stages’ boundaries have been calculated. A specimen’s individual fatigue life prediction method, based on the initial parts of stiffness degradation curves, has been proposed. Influence of the preliminary multiaxial cyclic loading on residual Young’s modulus and tensile strength has been studied. Patterns of deformation and fracture processes have been studied, using digital image correlation method and acoustic emission method. A significant influence of fatigue damage on the residual properties of GRP pipes has been concluded.

IMPLEMENTATION OF AES AND SHA-3 CRYPTOGRAPHY ALGORITHMS IN SECURING USER SENSITIVE DATA ON THE ARTESIAN WATER BILL PAYMENT TRANSACTION WEBSITE

Transacting online is something that is familiar to almost everyone in the digital era. The presence of digital transactions brings convenience in carrying out trading activities, but the use of the internet in creating easy transactions with several challenges in the form of hacking. In order to realize secure transactions, the level of protection on digital transaction service provider sites must be made as strong as possible to prevent digital attacks. Therefore, a protection system for user payment data on one of the transaction websites was created by applying the AES and SHA-3 cryptographic algorithm methods. The AES algorithm method was chosen because its implementation is not complicated but has proven its security, while SHA-3 is used to create unique keys from user passwords with the latest hash technology, so that the data encryption and decryption process becomes more protected because it uses the secret key of each user but not directly. The application of cryptographic algorithms will provide additional protection for the security of nominal user transaction data from cyber attacks so that the data. This research will produce a cryptography program flow with the Python programming language and examples of encryption and hash process results during program execution.

IMPLEMENTATION OF AES AND SHA-3 CRYPTOGRAPHY ALGORITHMS IN SECURING USER SENSITIVE DATA ON THE ARTESIAN WATER BILL PAYMENT TRANSACTION WEBSITE

Transacting online is something that is familiar to almost everyone in the digital era. The presence of digital transactions brings convenience in carrying out trading activities, but the use of the internet in creating easy transactions with several challenges in the form of hacking. In order to realize secure transactions, the level of protection on digital transaction service provider sites must be made as strong as possible to prevent digital attacks. Therefore, a protection system for user payment data on one of the transaction websites was created by applying the AES and SHA-3 cryptographic algorithm methods. The AES algorithm method was chosen because its implementation is not complicated but has proven its security, while SHA-3 is used to create unique keys from user passwords with the latest hash technology, so that the data encryption and decryption process becomes more protected because it uses the secret key of each user but not directly. The application of cryptographic algorithms will provide additional protection for the security of nominal user transaction data from cyber attacks so that the data. This research will produce a cryptography program flow with the Python programming language and examples of encryption and hash process results during program execution.

Improved acoustic source localization method for crack identification in structures

Acoustic Emission (AE) monitoring is considered one of the popular non-destructive testing (NDT) methodologies that have been used to predict and identify the location of damage in critical civil infrastructure. In this paper, an improved AE crack localization method is proposed by integrating the empirical mode decomposition (EMD)-based signal decomposition method with a source localization model. Unlike the conventional AE method, the proposed method is free of the use of bulk AE parameters such as counts, rise time, signal strength, and energy. First, EMD is used to minimize the presence of noise in the recorded AE waveforms and extract the key AE components. Then, key AE events are located using the source localization model to localize the crack in concrete structures. The performance of the proposed method is validated experimentally on small and large-scale concrete beams, where the damage is induced using progressive static load testing. In particular, the large-scale beams are designed for flexural and shear mode failure to evaluate the performance of the proposed method under various types of damage. Finally, the results of the proposed method are compared with the traditional method that uses raw AE waveforms and the method that uses bandpass-filtered AE waveforms. The results show higher crack location accuracy of the proposed method than the other methods, which makes it a suitable approach as a crack localization technology.

Comparative Analysis of Acoustic Emission Signals from On-Load Tap-Changers for Potential Detecting of Non-Simultaneous Operations

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

Innovative acoustic emission method for monitoring the quality and integrity of ferritic steel gas pipelines

Abstract This article presents a comprehensive improvement in the experimental analysis of cracking processes in smooth and sharp V-notched samples taken from gas transport pipelines, utilizing the acoustic emission (AE) method. The research aimed to establish a robust correlation between the failure mechanisms of uni-axially tensile samples and the distinct characteristics of AE signals for enhanced quality management in pipeline integrity. The study encompassed materials from two different straight pipe sections, encompassing both long-term used materials and new, unused materials. Through the application of the k-means grouping method to AE signal analysis, we achieved the identification of AE signal parameters characteristic of various stages of the material destruction process. This advancement introduces a significant improvement in monitoring and managing the operational safety of pipeline networks, offering a methodology that leverages advanced acoustic emission signal analysis. The outcomes present significant implications for the pipeline industry by proposing methods to enhance safety systems and more effectively manage the integrity and quality of gas infrastructure.

Local bit-level image encryption algorithm based on one dimensional zero excluded chaotic map

The exchange of digital images on the internet has become more convenient, but it has also led to increasing security concerns. Image encryption differs from text encryption, as inherent features such as massive data volume and high pixel correlation make it challenging to apply traditional AES and DES methods to images. This paper introduces a novel local bit-level image encryption algorithm based on chaos. Firstly, a new one-dimensional chaos system named the One-Dimensional Zero Excluded Chaotic Map (1D-ZECM) is designed, possessing features such as approximate global chaos, a broad chaos range, and high Lyapunov exponents, making it well-suited for cryptography. To resist brute force attacks, a hash function is employed to generate the encryption system’s key, further enhanced by using the 1D-ZECM to derive the key stream for the cryptographic system. Unlike traditional encryption methods that encrypt all 8 bits of a pixel, this algorithm focuses on the first six bits of each pixel during the encryption process, as the lower two bits contain less image information. In the diffusion process, the key stream generated by the 1D-ZECM is combined with mod and XOR operations to diffuse the rearranged image. Experimental results demonstrate that the proposed encryption algorithm exhibits high security and can resist common attacks. Moreover, when compared to representative algorithms, the proposed algorithm demonstrates better security and efficiency. The encryption algorithm presented in this paper provides a high-quality encrypted output.

Acoustic emission measurement during the proof loading of the new bridge over Odra river in Wrocław

The acoustic emission method is applied for damage monitoring through the analysis of internal events within the material. Sensors were placed at the cantilever joint during the proof loading. Signal analysis revealed potential damages. Signal values were compared against criteria for assessing the condition of reinforced concrete structures. Regular measurements detect potential damages, and combining this technique with other methods enhances its efficiency

Mechanical properties and damage characteristics analysis on recycled aggregate concrete with glazed hollow beads after high temperatures by acoustic emission method

This research investigates the effect of high temperature on the mechanical and acoustic emission properties of recycled aggregate concrete with glazed hollow beads (GHB-RAC). Uniaxial compression tests were conducted on GHB-RAC at varying recycled coarse aggregate (RCA) substitution rates and glazed hollow beads (GHB) contents, and acoustic emission (AE) technology was used to monitor the compression-induced damage throughout the testing process. This study delves into the mechanical properties and damage characteristics of GHB-RAC when exposed to fire. The findings reveal that AE characteristic coefficients aptly delineate the three-stage nature of axial compression damage in concrete at temperatures up to 400 °C. However, these stage-specific traits vanish at temperatures exceeding 400 °C. Additionally, through the analysis of AE signals at different temperatures, the compression damage process is elucidated based on the b-value method. The RA-AF analysis indicates that the number of shear cracks exhibits an increasing trend with increasing temperature, and an increment in GHB dosage positively influences the percentage of tensile cracking during loading, with the influence being more significant at 70 % GHB content. The acoustic emission sensing can accurately detect and localise damage. With the increase in temperature, the distribution of AE signals depicting concrete damage is more dispersed and the number of AE signals is more larger. Notably, the incidence of localized damage points diminishes with a higher GHB ratio, indicating the efficacy of GHBs in mitigating thermal damage to the concrete. Furthermore, a damage constitutive model incorporating variables such as temperature, GHB dosage, and recycled coarse aggregate replacement rate was proposed. The model employed AE energy count parameters as the damage indicator. The good fitting results suggest that the mechanics behavior of GHB-RAC under uniaxial compression can be well described by the model. Consequently, the study offers invaluable insights into the damage evolution of post-fire recycled aggregate concrete with GHB admixtures.

Identification method of internal leakage in nuclear power plants valves using convolutional block attention module

Valves are indispensable fluid control devices widely employed in Nuclear Power Plant (NPP) structures. Due to prolonged exposure to high-temperature environments, the internal sealing components become susceptible to thermal deformation or wear, leading to compromised sealing and potential leakage accidents in the valves. Therefore, detecting internal leakage in valves holds great significance. We propose an effective acoustic emission detection device designed for identifying internal leakage in valves within high-temperature environments. A valve internal leakage monitoring system based on the Acoustic Emission (AE) method is developed, utilizing high-temperature piezoelectric transducers as AE sensors. To efficiently and rapidly identify leakage states, an artificial intelligence (AI) algorithm incorporating a Convolutional Neural Network (CNN) with the Convolutional Block Attention Module (CBAM) is employed. The approach integrates both a channel attention module and a spatial attention module to capture global and local features, respectively. Normalized spectral data is used as the training set to optimize the CNN parameters. The performance of this method is then compared with BAM and SENET. The results indicate that CBAM can effectively enhance and suppress features by allocating weights among channels according to task requirements. Moreover, it can more efficiently identify multiple damage features under the constraints of limited computing resources.

Influence of duoplasmatron ion source plasma treatment on multi-axial knitted structure reinforced glass fiber composites

The present study investigates the impact of glow discharge cold plasma-induced duoplasmatron ion source on the mechanical properties of multi-axial knitted structure reinforced glass fiber composites. Analysis of failure location and mode in bending tests was conducted using the acoustic emission method. The results reveal that the tensile and bending strength of the multi-axial structure reinforced glass fiber composites treated with oxygen/nitrogen plasma had increased by 21.84% and 37.28%, respectively. In contrast, when treated with oxygen/argon plasma, the corresponding enhancements were 17.47% and 24.16%, respectively. These results indicate that oxygen/nitrogen plasma treatment yields superior mechanical properties to oxygen/argon treatment. The accumulated acoustic emission energy in the multi-axial structure reinforced glass fiber composites subjected to oxygen/nitrogen and oxygen/argon treatments was 25.49% and 62.39% lower than untreated samples, respectively. The failure mode observed in the untreated composites was matrix cracking, whereas the composites treated with dual plasma exhibited interface debonding. The dual plasma modification enhanced fiber wettability and surface roughness, facilitating increased contact area between the fiber and matrix and promoting the overall bonding performance between the two components. Taken together, our findings suggest that duoplasmatron ion source treatment enhanced the interface bonding and mechanical properties of the multi-axial structure reinforced glass fiber composites.