Acoustic Emission Source Location Research Articles

Determine the location of acoustic emission source on wood surface by using a wavelet-energy attenuation method

ABSTRACT To determine the location of wood damage and fracture, an acoustic emission source localization method based on wavelet-energy attenuation law was proposed. High-speed acquisition equipment and LabVIEW software were used to design a three-channel AE signal acquisition system. The wavelet transform is used to decompose the original signals into five frequency levels, and the second, third, and fourth level were selected for reconstruction of the acoustic emission signal. Finally, the reconstructed signals was used to determine the location of the acoustic emission source in accordance with the energy attenuation law. The results indicated that when the distance between the three sensors were 100 and 200 mm, the average location error obtained using the original signals in accordance with the energy attenuation law was 12.6 and 18.4 mm, respectively. However, the average location error obtained using the reconstructed signals in accordance with the energy attenuation law was 2.2 and 6.9 mm, respectively. These results reveal that the proposed method can achieve high-precision localization of acoustic emission source. This research enrich and expand the content of AE technology and wooden building research, provide new ideas and methods for non-destructive testing of similar structures, and promote research progress in related fields..

Three-dimensional acoustic emission source localization method for layered rock considering anisotropic P-wave velocity

Three-dimensional acoustic emission source localization method for layered rock considering anisotropic P-wave velocity

Mode I fracture propagation and post-peak behavior of sandstone: Insight from AE and DIC observation

Microcracks generated at the crack tip, particularly in quasi-brittle materials such as sandstone, give rise to a non-linear region known as the fracture process zone (FPZ), which cannot be analyzed using linear elastic fracture mechanics (LEFM). To gain insights into the nonlinear fracturing behavior of rock, this study conducted three-point bending experiments on sandstone beams with prefabricated notch. Stable mode-Ⅰ fracture propagation during the post-peak stage was achieved by the monotonic increment of crack mouth opening displacement (CMOD) measured by a clip gauge. The acoustic emission (AE) and digital image correlation (DIC) techniques were utilized to visually track the detailed crack propagation process from both micro and macro perspectives. Nonlinear fracture parameters, including FPZ length, were identified by AE and DIC measurements and compared with the theoretical calculation. A nonlinear fracture criterion relating applied load, equivalent crack length, and stress intensity factor were established, from which the theoretical CMOD-P curve at the post-peak stage were obtained and validated with experimental measurement from MTS machine. The results reveal that microcracks formulating the FPZ materialize in the pre-peak stage, with the majority emerging post-peak to facilitate the propagation of the traction-free crack and FPZ in the loading direction, as evidenced by AE locations and DIC displacement gradient. Throughout the crack propagation process, over 65 % of microcracks are estimated to be tensile mode based on both AF/RA and polarity analysis, aligning with anticipated outcomes from macroscopic three-point bending fracture tests. Energy dissipation primarily occurs within the FPZ to facilitate separation of crack faces, with the dissipated energy region advancing towards the beam boundary. By integrating AE source locations and DIC displacement gradient, the FPZ length of the 3-point-bending sandstone specimens is determined to be lp = 8.1 mm, slightly exceeding the theoretical FPZ lengths calculated by the Irwin model (lp = 7.0 mm) and the Dugdale-Barenblatt model (lp = 7.6 mm). Using a non-linear fracture criterion that incorporates the FPZ and employing the in-time equivalent crack length measured by AE and DIC as an intermediate variable, the theoretical applied load-crack length curve during the post-peak stage is calculated, showing close agreement with load cell measurements with a negligible error of approximately 2 %. Furthermore, the theoretical post-peak CMOD vs. load curve aligns with experimentally obtained results, demonstrating a non-linear crack propagation speed at a constant CMOD rate, wherein crack propagation speed decreases as crack length increases.

Acoustic emission source location for composite tubes using finite element simulation and machine learning

ABSTRACT Acoustic emission is widely used in engineering structural health monitoring, where damage location based on acoustic emission have great advantages. Currently, most of the acoustic emission location work requires extensive experimental studies. In this study, acoustic emission source location model was proposed based on BP neural network and acoustic emission wave propagation simulation, thus reduced the experiments required for acoustic emission localisation and improved the location accuracy. Initially, finite element simulation was conducted to study the propagation of acoustic emission waves in composite tubes. The calculated findings showed a high level of agreement with the experimental data. Additionally, the first arrival time of the signal was extracted according to the Akaike information criterion, and the neural network was trained and tested using the simulated data, while the trained network model was validated using the experimental data. The results show that the finite element method is a reliable alternative test method. More than 94% of the sound source location results have absolute location error within ± 3 mm, and the all points is less than ± 5 mm. The deviation of the location results showed significant improvement compared to the traditional time difference of arrival method, verifying the feasibility of the method.

Multiple methods for acoustic emission source location in metal waveguides

Research on sound source location, which is widely used in engineering, is an important topic in acoustic emission (AE) technology. Traditional double-ended location methods have certain limitations, such as sound sources being located between two sensors, a single application scenario and possible time delays. In this study, three theoretically feasible location methods were proposed and experimentally verified based on AE propagation theory to test the accuracy of the AE source location and explore single/double-sensor line location in metal waveguides. The experiments measured the propagation speed of longitudinal and transverse waves in the waveguide and verified the feasibilities of the three acoustic source location methods based on waveform analysis, with waveform matching using the wave range and wave speed differences. The experimental results show a location error of less than 3% for the double-ended and double wave-speed methods and less than 6% for the double-waveguide and reflective-surface types.

Experimental characterization on fracture behavior of UHPMC under small-scale sample tensile testing: Acoustic emission monitoring and digital image correlation

Manufactured sand (MS) was used to partially replace the traditional quartz sand (QS) to prepare a novel ultra-high-performance manufactured sand concrete (UHPMC). To better understand the fracture damage mechanism of UHPMC, small-scale sample tensile tests were conducted to investigate the tensile behavior of three types of UHPMC (low strain hardening, low strain softening and high strain softening). Acoustic emission (AE) and digital image correlation (DIC) are employed simultaneously to monitor and assess the damage characteristics of UHPMC. The results indicate that the classical AE characteristic parameters (ringing counts, duration and b-value) provide a satisfactory result in identifying the feature stages of low strain hardening and high strain softening. Based on the Gaussian mixture model (GMM) and support vector machine (SVM), an automatic probability classification algorithm has been developed that achieves the quantitative categorization of three UHPMC cracking types (shear or tensile cracks). Among them, tensile damage is dominant during the entire loading process, whereas shear damage, including aggregate dislocation and fiber debonding and pullout, accumulates during the hardening and softening stages. DIC visualization and AE source localization complement each other in identifying crack distribution, contributing to further revealing the tensile damage mechanism of UHPMC. The results obtained in this study can provide data and theoretical support for fracture damage characterization and health monitoring of ultra-high-performance concrete materials under tensile conditions.

Multi-method comparison and multi-index evaluation of cracking characteristics of asphalt mixture

The micro crack inside asphalt pavement are potential and antecedent factors for macroscopic cracking of the pavement. The position and quantity of micro cracks have a significant impact on the service life and failure mode of asphalt pavement. The purpose of this study was to conduct multi-scale analysis and multi-index evaluation on the whole process of generation, expansion, and transformation of micro-crack inside asphalt pavement (asphalt mixture) based on multiple testing methods. Firstly, synchronous tests were conducted on the cracking process of asphalt mixture by using three-point bending loading method, acoustic emission (AE) monitoring method, and digital image correlation (DIC) analysis method. Secondly, a three-dimensional finite element model of the asphalt mixture beam was established for AE source location simulation. Finally, the advantages and limitations of multi-methods were compared and analyzed. The principal strain points with different sizes were randomly distributed in the tensile area of the asphalt mixture beam bottom at the initial loading stage, and the number of the principal strain points increased with the decrease of the loading rate. The macroscopic main crack of the asphalt mixture beam generated and expanded from the maximum principal strain point (P1) at the later loading stage. The AE dominant-frequency distribution ranges of micro-crack expanding process, macro-crack generating process, and failure process of asphalt mixture beam were 78.125–156.25 kHz, 234.375–312.5 kHz, and 0–78.125 kHz, respectively. Four stages of damage evolution and cracking process inside the asphalt mixture beam were obtained by the AE source location simulation. A comprehensive and systematic evaluation of bending cracking characteristics of asphalt mixture beam was realized by obtaining multiple indexes from multiple scales with multiple methods.

−252.8 °C Liquid Hydrogen Acoustic Emission Experiment in Simulated Aerospace Fuel Tank

With the increase of space activities, the safety detection of equipment in supercold environment becomes more and more important. However, the safety detection of equipment in liquid hydrogen environment is an unsolved worldwide problem. This paper presents a new method to detect acoustic emission (AE) signals in liquid hydrogen environment, which can be used to confirm the safety and reliability of the equipment working in liquid hydrogen. First, the principle of mandrel-based fiber-optic ring (MBFOR) AE sensor and the corresponding demodulation method are introduced. Second, the effects of physical parameters such as the length of the sensing fiber, the diameter and material of the mandrel, and the number of fiber winding layers on the sensor's sensitivity are studied by experiment. Finally, the AE source localization accuracy test of the FOR AE detection system at low temperatures and the adaptability test of the proposed method in -252.8 °C liquid hydrogen are carried out. The results show that the proposed MBFOR sensor and the multi-channel AE detection system can complete the defects detection of aerospace pressure vessel in liquid hydrogen. Certainly, it can be used for safety detection for other spacecraft equipment in harsh environments.

Improved Method for Acoustic Emission Source Location Based on DFB Laser Sensor and Particle Swarm Optimization Algorithm

Improved Method for Acoustic Emission Source Location Based on DFB Laser Sensor and Particle Swarm Optimization Algorithm

A hybrid deep learning method for AE source localization for heterostructure of wind turbine blades

An acoustic emission (AE) and hybrid deep learning networks based damage source localization method for heterostructure of wind turbine blades is proposed in this paper. Firstly, comprehensive data preprocessing is performed, including AE signal denoising, feature extraction, feature selection and normalization. New training features including AE descriptors, features of time, frequency domains and spectral features are extracted. A feature selection method based on Light-GBM and correlation analysis is employed to identify relevant features for AE source localization. Subsequently, two deep learning networks, AM-BiLNN and AM-LCNN, are developed to locate the damage source in two steps. Then, numerical tests are implemented on localized structure of a wind turbine blade to verify the performance of the proposed method and the performance of the selected features and the robustness of the proposed method under noise are investigated. Furthermore, a comparative investigation between the proposed method with long short-term memory neural networks (LSTM), convolutional neural networks (CNN) and the cluster-based method is carried out to demonstrate the superiority of the proposed method. The results highlight the superiority and robustness of the proposed method. Feature selection is shown to effectively enhance coordinate localization performance.

Error distribution and influencing factors of acoustic emission source location for sensor rectangular network

The arrangement of acoustic emission (AE) sensors would influence the location accuracy. This study focuses on two-dimensional source location using four sensors, which are placed at the four corners of a rectangle. To assess the influence of sensor arrangement, numerous simulation experiments were conducted with different aspect ratios of the rectangle. The results reveal that there is a highly accurate location area at the central region of the rectangle and four regions with pronounced location errors along the symmetry axis. This means that sensor arrangement should be strategic to make the interested monitoring area not at the above range. Additionally, as the aspect ratio increases, the location accuracy decreases. When the aspect ratio surpasses 7:1, the location results would be seriously distorted. Furthermore, the observed error distribution pattern is validated through pencil-lead breaking experiments and the corresponding explanation is provided to clarify these findings.

Visualization of Movement and Expansion of Coal Reaction Zone by Acoustic Emission Monitoring in Underground Coal Gasification System

Underground coal gasification (UCG) is the process of directly recovering energy as combustible gases such as hydrogen and carbon monoxide by combusting unmined coal resources in situ. The UCG process is an invisible phenomenon, in which fracturing activity at high temperature (>1000 °C) in coal seams expands the gasification zone and increases the combustible components of the product gas. However, excessive expansion of the gasification zone may cause environmental problems such as gas leakage, deformation of the surrounding ground, and groundwater pollution. Therefore, visualization of the gasification zone of UCG is required for both improving gasification efficiency and developing UCG systems with low environmental impact. In this study, the large-scale model UCG experiments conducted on a laboratory scale (size: 625 mm × 650 mm × 2792 mm (H × W × L)) were carried out to discuss the visualization of the gasification reaction zone of coal in UCG by Acoustic Emission (AE) technique with uniaxial and triaxial acceleration transducers. As the results of temperature monitoring and AE source location analysis show, AE sources are located near the high-temperature zone (>1000 °C). In addition, the located AE sources move and expand with the movement and expansion of the high-temperature zone. AE measurement can be a useful technique for monitoring the progress of the UCG reaction zone. AE measurement with triaxial sensors is also useful to predict a high-temperature zone though the measurable range, which has to be considered.

Deep residual learning for acoustic emission source localization in A steel-concrete composite slab

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Two-dimensional acoustic emission source localization on layered engineered wood by machine learning: a case study of laminated veneer lumber plate structure

Engineered wood or mass timber has gained increasing popularity in building construction, and layered engineered wood is a major category of mass timber design since it enables manufacturing structural members with a wide range of geometry. Thus, there is a potential rising demand for structural health monitoring on engineered wood-based structural members and buildings. This study investigates the feasibility of using an important and practical acoustic emission (AE) method for damage localization, specifically two-dimensional (2D) AE source localization, in a representative layered engineered wood sample, namely laminated veneer lumber (LVL) plate. While 2D AE source localization is generally straightforward in isotropic materials, the problem becomes challenging for anisotropic materials with angle-dependent wave velocities. It is even more complicated if heterogeneity involves, which turns out to be the case for layered engineered wood. In this study, we rely on the AE feature of difference in time of arrival (dTOA) and develop three methods to address the challenges of 2D AE source localization raised by anisotropy and heterogeneity in an LVL plate. The benchmark velocity profile method (VPM) is first implemented in an LVL plate. With knowledge of the angle-dependent velocity, the source location predictions by the VPM are generally erroneous even with predicted source location outside of the region of interest. Furthermore, the general regression neural network (GRNN) is developed using different combinations of dTOA components, resulting in improved prediction performance. Third, the Gaussian process regression (GPR) is developed by maximizing the marginal likelihood of the training dataset. Moreover, to lessen the computation burden, the lower bound of the logarithm likelihood of the whole models is derived and decomposed through Jensen’s inequality and Bayes’ theorem, providing the theoretical background for training models with different combinations of dTOAs individually.

Acoustic emission source location method for glass fibre reinforced plastics based on virtual loading focusing enhancement technology

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

Spatial-temporal graph convolutional networks (STGCN) based method for localizing acoustic emission sources in composite panels

A novel spatial–temporal graph convolutional networks (STGCN) based method for the regression task of localizing acoustic emission (AE) sources in composite panels is proposed. This is the first time that graph convolutional networks are introduced into the AE source localization task. Data generated by AE sensor networks is represented by a graph structure, in which the temporal features extracted from AE waveforms using one-dimensional convolutional neural networks (1D-CNN) and the spatial information of sensors constitute the node features. An adaptive distance-based adjacency matrix calculation method according to the geographical locations of AE sensors is further developed to represent the connectivity of the graph. Finally, the proposed method is experimentally validated on a glass fiber-reinforced plastic (GFRP) panel, and 5-fold cross-validation was carried out to evaluate the performance of the method effectively. The results show that the proposed STGCN method has a high performance in damage source localization and it significantly outperforms other methods.

Minimum Squared Error Method based on Modal Analysis for Acoustic Emission Source Location using Microfiber Coupler Sensor

This study focuses on an improved modal analysis method to the source location using a microfiber coupler sensor (MFCS). This method uses the Minimum Squared Error(MSE) to reduce the dependence of locating results on frequency selection and automate the locating process of contour threshold modal analysis. First, the secondary enclosed package method of MFCS was studied to ensure that the signal detected by this sensor meets the requirements of the contour threshold modal analysis and can extend the MFCS working life as well. The MSE method was then presented detailed. Second, the directionality experiment of the MFCS was performed to determine the best layout method for sensors in planar localization. The planar source localization method with both two sensors and three sensors are then introduced. Last, both linear and planar location experiments were performed on an aluminium thin plate. The results indicate that the sensor packaged by cuboid glass tube is more suitable for practical ultrasonic testing; the location process is automated by valid contour extraction and initial frequency selection, and the linear error using this method is effectively reduced as well; the best arrangement of the sensor is to place the sensor at the corner of the planar, and the angle between the sensor and the horizontal line is 45°; compared with two sensors, the planar localization with three sensors provides a more accurate and high repeatability results. This study proved that the combination of the MFCS and the MSE method is quite feasible and can be applied to the planar localization in plate-like structures effectively.

Diagnostics using acoustic emission of the MS-21 airframe wing console in the area of artificial damage during design life tests

With the help of acoustic emission (AE), three zones of structural transformations of the structural material of the wingbox are diagnosed, two of which were located along the boundaries of the stringer delamination zone, the third — near the edge of the technological window. The application of the developed methodology for monitoring the dynamics of changes in the weight content of location pulses in the lower, middle, and upper energy clusters made it possible to control the kinetics of micro-, meso-, and macrodamages in the wingbox loading mode and to evaluate the structural stability of the structural material in the region of coordinate location of AE sources. Keywords: damage kinetics, bearing capacity, stress concentrator, delamnation, wingbox, stringer, acoustic emission, location, criterion parameters, defect, ultrasonic testing. chernovdv@inbox.ru

Method of Locating Loose Particles Inside Aerospace Equipment Based on Parameter-optimized XGBoost

The complex internal structure and heterogeneous composition material of aerospace equipment make it difficult to apply traditional acoustic emission source localization methods to the loose particle localization research. In previous studies, the authors transformed the loose particle localization problem into the multi-classification problem in machine learning, trained the loose particle localization model, but there were problems of low classification accuracy and low practicality. In this paper, the authors first introduced the ensemble learning idea into the loose particle detection field, designed a complete loose particle localization scheme, analyzed the above problems from multiple perspectives, and proposed new algorithms or strategies to enhance the superiority and practicability of the loose particle localization model. Specifically, in view of the low classification accuracy, the authors carried out the research on pulse preprocessing, feature engineering and model training from the four perspectives of signal, feature, data set and classifier, respectively. The zero-pulse-filling pulse matching algorithm and the channel-weighting-based feature selection method was newly proposed, Mel-Frequency Cepstral Coefficients features was newly extracted, feature optimization scheme was designed, and XGBoost ensemble classifier was trained. Thus, high-quality loose particle signals, high-quality localization data sets and high-performance loose particle localization models were obtained, respectively. Test results show that, the classification accuracy achieved by the new loose particle localization model was 96.80%, which was a significant improvement compared to the 83.53% achieved in previous studies. In view of the low practicality, the authors built the loose particle localization experimental system, gave the specific implementation steps of the loose particle localization method, as well as the general procedures for applying the loose particle localization model for physical testing. Meanwhile, taking into account the requirements of aerospace engineering applications, the authors added the majority voting strategy to convert classification results into localization results, thus newly proposed the definition of equipment-level loose particle localization accuracy. Several physical testing results show that, the loose particle localization model achieved a localization accuracy of 90.91%, which effectively verified the feasibility and stability of the proposed method. This study is an important supplement to the loose particle detection research, and of great significance in improving the reliability of aerospace systems.