Analysis Of Acoustic Emission Signals Research Articles

Acoustic emission-based leakage detection for gas safety valves: Leveraging a multi-domain encoding learning algorithm

Safety valves in gas distribution systems are crucial for preventing excessive pressure buildups. Leakage in these valves compromises system stability, causing equipment damage and environmental pollution. Acoustic emission signal analysis from the valve body is essential for detecting leakage, but these signals are often weak and easily disrupted by noise, complicating detection. This paper proposes a multi-domain encoding learning algorithm to address these challenges. Unlike conventional single-domain methods, it combines two encoding images, the Gramian angular difference field and the Markov transition field, to enhance the comprehensive expression of leakage features. A lightweight convolutional neural network reduces computing resource dependency, and a convolutional block attention mechanism improves feature identification. Experimental results demonstrate that our method detects gas leakage with an accuracy of at least 96.77%. It maintains superior performance even under intense noise interference, offering a promising solution for detecting weak gas leakages in safety valves amidst high background noise.

Damage and failure assessment of banana/ramie/epoxy composites using acoustic emission monitoring

In recent years, Composite materials that include natural fibers have played a significant role in aerospace, automotive industries, and other engineering applications. Due to its enhancing properties, the usage of composites has owing to an increase in day-to-day life. This research aims to develop a sustainable alternative material pronounced for environmental sustainability for surpassing synthetic fibers. Therefore, real-time monitoring and detection of the damage mechanisms in natural fiber composites are indispensable. In this regard, the acoustic emission (AE) technique lends itself to identifying the microscopic damage mechanisms in composite materials. Compression tests were performed in banana/ramie/epoxy composite specimens, and following consequences, the AE parameters (frequency, amplitude, counts, duration, energy rise time, etc.) were recorded. The novelty of this article is to detect the damage evolution and failure mechanisms of banana/ramie/epoxy composites under quasi-static compression with AE signal analysis for the first time. Findings showed that the stacking of hybrid reinforcing banana and ramie fiber improves the stress distribution, leading to enhanced load-carrying capacity (30.56 %), energy absorption (26.78 %), and stiffness (35.24 the compressive strength reached the maximum strength of the banana/ramie/epoxy composites%).This increase represents a substantial 30 % increase in AE energy absorption prior to failure, suggesting that the composite specimens experienced significant damage or extensive failure mechanisms Further, the AE parameters also showed that the AE energy under compression is relatively low and that frequency disperses between 70 and 150 kHz.

Acoustic emission and multiscale computation-guided tensile damage identification in woven composite laminates at cryogenic temperatures as low as 20 K

For laminated composite structures as key components of storage tanks serving at cryogenic temperature, it is crucial to identify the damage mechanisms for evaluating their mechanical properties and guiding structural design. In this work, the cryogenic tensile damage behavior of a thin-walled woven composite laminate was investigated through the acoustic emission (AE) monitoring and multiscale finite element (FE) computation at typical low temperatures of 153 K, 77 K and 20 K. We first established temperature-dependent constitutive laws for the microscale and mesoscale constituents of such composites based on experimental data, followed by the development of a hierarchical computational framework for modeling multiscale damage characteristics at different low temperatures. A fiber-optic acoustic emission measurement system was constructed to provide online monitoring of tensile damage of the woven composite laminates at cryogenic temperatures as low as 20 K. The comparations were made between the predicted cryogenic damage characteristics and in-situ AE signal analysis, assisted by scanning electron microscope (SEM) observations. The computed cryogenic damage evolution closely matched the AE signal identification results. The results indicate that fiber breakage and matrix cracking are the dominant cryogenic damage modes, and that the different low temperatures exert significant effects on the properties of the epoxy matrix, yarns and composite laminates. The combination of the AE monitoring system and the computational scheme provides a valuable tool for evaluating structural integrity and guiding the microstructural design of composite laminates used in cryogenic environments.

Acoustic emission monitoring of composite marine propellers in submerged conditions using embedded piezoelectric sensors

Flexible composite marine propellers can aid the marine industry in reducing carbon emissions and underwater radiated noise pollution. The structural integrity of the blades can be assessed using structural health monitoring. One of these methods is the measurement and analysis of damage-induced acoustic emission signals. This paper experimentally investigates the feasibility of using embedded piezoelectric sensors for the measurement of acoustic emissions throughout a submerged flexible composite marine propeller blade. A full-scale glass-fibre reinforced polymer blade has been manufactured with 24 embedded sensors. While suspended in artificial seawater, acoustic emissions were simulated on the blade. The measurements show that the embedded piezoelectric sensors can measure acoustic emissions while the blade is submerged. Further, the distance from source to sensor over which the acoustic emission is measurable was investigated. For a noise level of 40 dB and a source amplitude of 70 dB between 100 and 250 kHz, an average maximum measurable distance of 124 mm was obtained. For higher frequencies, the distance drops and for lower noise levels the distance increases.

Identification of Damage Modes and Critical States for FRP/Steel-Concrete Composite Beams Based on Acoustic Emission Signal Analysis

Identification of Damage Modes and Critical States for FRP/Steel-Concrete Composite Beams Based on Acoustic Emission Signal Analysis

Feature analysis and recognition of fiber breakage AE signals after propagation

Acoustic emission (AE) is a non-destructive testing technique, and establishing correlations between AE signals and material damage modes is one of its primary challenges. However, it is difficult to identify damage modes in ceramic matrix composites (CMCs) due to AE signal attenuation occurring after propagation and complex damage modes. In this study, AE signals generated by the breakage of C and SiC fibers were monitored at different distances and angles on the C/SiC plate. The attenuation of energy and the frequency spectra were analyzed. The Mel-frequency cepstral coefficient (MFCC) method was used to analyze the waveform data of AE signals and extract MFCC features. To identify the damage caused by C and SiC fiber breakage, AE parameter features and MFCC features were selected as inputs, and a fully connected neural network was constructed to train a supervised pattern recognition model. The results show that the MFCC feature has higher recognition accuracy than the traditional feature when AE is used for damage identification.

Application of Acoustic Emission Technology for Bearing Condition Monitoring on Passenger Ropeway

Rolling bearings are important stressed components of driving wheels and detour wheels on passenger ropeway, and their failure may lead to accidents and casualties. After the ropeway is installed and put into operation, the rolling bearings in the wheels are difficult to disassemble. Once disassembled, the bearing may be damaged. Due to the installation position and the complex operation environment, the health status of the rolling bearings is a blind spot for ropeway inspection and detection. Acoustic emission detection technology is used to monitor the rolling bearings of passenger ropeways. After a large number of field tests, the acoustic emission monitoring program and signal analysis method of rolling bearing on ropeway were proposed. The parameter characteristics of acoustic emission signals obtained in bearing operating were analyzed from two aspects: the ropeway operating cycle and the bearing operating cycle. Spectrum analysis of typical signals was performed, and the spectrum characteristics of typical acoustic emission signals of rolling bearing during the operation of the ropeway were obtained. On-site tests results show that using acoustic emission technology to perform condition monitoring and fault diagnosis of low-speed rolling bearings on passenger ropeways has good application prospects.

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.

Analysis of critical states based on acoustic emission signals during progressive failure of wood.

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.

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.

Characterization of the damage mechanism in CFRP composites under mode I based on comprehensive analysis of AE signals

This study utilises several methods centred on acoustic emission (AE) waveform characteristics to conduct in-depth analysis of the damage mechanism of composite materials under mode I. Combined with the peak frequency statistics and continuous wavelet transform (CWT) of AE signals, three damage modes of laminated composites during loading are identified. The cumulative energy of three damage modes separated by variational modal decomposition (VMD) is used to characterize their damage evolution process. Based on the findings, delamination and fiber–matrix debonding are proven to be the dominant damage mechanisms for mode I. The comprehensive analysis results from AE's accumulated energy, peak frequency distribution, CWT, VMD, and sentry function can accurately detect the onset time of delamination in laminates. The equivalent crack length is calculated by the load value determined at the initial time of delamination, and the strain energy release rate calculated by the load value and crack length at this time is used as the interlaminar fracture toughness value of mode I. This research method will provide a new idea for the damage tolerance analysis of mode I delamination in laminated structures.

Analysis of acoustic emission signal in the tensile process of weld-bonding joint and its damage model

ABSTRACT This paper analysed the acoustic emission signals acquired during the tensile test of DP780 weld-bonding joints, and the Gaussian equation was used to fit the cumulative hits and the load to establish relevant damage models. The process parameters were designed based on the Box-Behnken Design method, and the wavelet packet was used to decompose the collected AE signals. The results showed that, in the elastic stage, no AE signal emitted, and the AE signal was mainly concentrated in the plastic stage, the hardening stage and the failure moment of the weld nugget or the adhesive layer. At the failure instant of the adhesive layer or the weld nugget, the signal amplitude was the largest, and the failure frequency was mainly concentrated in the middle and low-frequency bands. The failure of the adhesive layer and the weld nugget could be distinguished from the energy distribution diagrams, and the established damage model could reflect the damage state of the specimen. The results proved that the acoustic emission technique can provide support for the non-destructive testing of the weld-bonding joint.

The multifractal characteristics of acoustic emission for composite samples subject to simulated field loading condition

ABSTRACT The composite strata structure is a common geological formation in western China. The study of the failure characteristics of the composite structure is of great help to the stability evaluation of stope and the prediction of coal mine dynamic disasters. In order to study the influence of different hard rock coefficients on the instability characteristics of composite samples, the triaxial tests with progressive varying confining pressure simulating the field loading condition were conducted on eight groups of composite samples with different hard rock coefficients. The bearing capacity of different samples was tested using the RMT-150B triaxial compression equipment, while the DS-5 type eight-channel acoustic emission monitoring system was employed for simultaneous monitoring of acoustic emission parameters. Additionally, the multifractal theory in mathematics is applied to the analysis of acoustic emission signals, exploring the multifractal characteristics of the time series of acoustic emission signals. The results show that the peak strength of the composite sample decreases first and then increases as the hard rock coefficient increases, which is consistent with the trend of the multifractal spectrum parameter Δ f α of the acoustic emission time series. A significant increase in the low-frequency and high-amplitude signal ratio can be used as a precursor to early indicate the instability in composite samples. The dynamic changes of multifractal parameters at different stages of the progressive varying confining pressure can reflect the influence of axial pressure and confining pressure on the crack propagation in the composite samples, and the two parameters at the hydrostatic pressure stage can be used as a rule-of-thumb for screening the testing samples. Simultaneously, the multifractal parameters can early warn the rock failure much more ahead of time than the conventional approach associated with the warning point obtained in the acoustic emission curves, and its head of time can be reaching 0.015 failure progress.

Waveform analysis of acoustic emission signals arising from water absorption of cracked concrete

Waveform analysis of acoustic emission signals arising from water absorption of cracked concrete

Investigating a three-dimensional convolution recognition model for acoustic emission signal analysis during uniaxial compression failure of coal

Acoustic emission predicts coal sample failure, vital for early warnings and monitoring. The study emphasizes using acoustic emission to predict coal sample failure patterns and establish a discriminative model for monitoring. It employs a lightweight 3D convolutional model combining DenseNet with Group Convolution (GC) and the "squeeze-and-excitation" (SE) module. Coal samples from Xinglongzhuang Coal Mine underwent uniaxial compression, and a subset of experiments with prominent features was selected. The resulting acoustic emission parameters were then transformed into spatiotemporal image sequences for input to the model. Successfully identifying hazardous coal damage stages, all four network structures (DenseNet, DenseNet + GC, DenseNet + SE, DenseNet + GC + SE) achieved over 98.29% predictive accuracy in verification samples. The model exhibited a recall rate of over 98.02% for high-risk samples, effectively capturing spatiotemporal information. DenseNet + GC + SE showed a probability distribution focusing on different risk levels. By integrating group convolution and SE modules, this model significantly reduced both model and time complexity while preserving precision, enhancing efficiency. It effectively discerns diverse acoustic emission features through group convolution and evaluates their significance using the SE module. Compared to the traditional C3D neural network, this model greatly reduces complexity and time requirements while improving efficiency in distinguishing between various damage stages.

Effect of SiC concentration on the mechanical properties and fracture modes of SiC/Al composites via cluster analysis of acoustic emission signals

Effect of SiC concentration on the mechanical properties and fracture modes of SiC/Al composites via cluster analysis of acoustic emission signals

Enhancing Reliability and Safety in Industrial Applications: Assessing the Applicability of Energy b-Value to Composites.

This study investigates the fracture behavior of glass fiber-reinforced plastic (GFRP) under various loading conditions using acoustic emission (AE) testing. Using fracture tests and time series analysis of AE signals, parameters such as b-value, improved b-value (Ib-value), and energy b-value (be-value) were examined to understand crack initiation, growth, and structural failure. The stress-strain curve revealed distinct responses during tensile and step loading, and time series analysis highlighted variations in amplitude, AE energy, and Kaiser and Felicity effects. Under tensile loading, the Ib-value exhibited a linear decrease, while step loading introduced complexities, including the Felicity effect. The be-value, incorporating energy considerations, fluctuated, providing insights into micro-cracks and macro-cracks. Statistical analysis demonstrated a consistent decrease in the be-value, emphasizing its potential for long-term monitoring. This study provides a comprehensive technique for assessing composite material fracture behavior, enhancing understanding for critical applications in hydrogen storage vessels and pressure pipes as well as advancing reliability and safety in industrial sectors.

Damage Evolution During Rock Dynamic Compression Revealed by Wavelet Analysis of Acoustic Emission Signals

Damage Evolution During Rock Dynamic Compression Revealed by Wavelet Analysis of Acoustic Emission Signals

Identification of Destruction Processes and Assessment of Deformations in Compressed Concrete Modified with Polypropylene Fibers Exposed to Fire Temperatures Using Acoustic Emission Signal Analysis, Numerical Analysis, and Digital Image Correlation.

This article presents the results of tests conducted to identify the failure process and evaluate the deformation of axially compressed concrete specimens modified with polypropylene fibers (PP). The test specimens were previously stored at ambient temperature and subjected to fire temperatures of 300 °C, 450 °C, and 600 °C. Acoustic emission (AE) signals were recorded during loading, along with force and strain measurements. The recorded AE signals were analyzed using the k-means clustering method. The compilation of the test results made it possible to determine the classes of signals characteristic of different stages of the material failure process and to indicate the differences in the failure mechanisms of specimens stored under ambient conditions and subjected to fire temperatures. Digital image correlation (DIC) measurements were conducted during the strength tests. A numerical model of the material (FEM) was also prepared, and a comparison of the obtained results was carried out.