Acoustic Emission Source Location 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.

Investigation of Lamb wave modes recognition and acoustic emission source localization for steel plate based on golden jackal optimization VMD parameters and CWT

Accurate identification of Lamb wave modes in acoustic emission(AE) signals propagating on steel plates is crucial for precise source localization. In this paper, we propose a novel method that optimizes variational mode decomposition(VMD) parameters using golden jackal optimization(GJO) and identifies Lamb wave modes on steel plates through continuous wavelet transform(CWT). The Hsu-Nielsen source(HNS) is employed as the AE source. The minimum permutation entropy of the AE signal is used as the optimization objective, with GJO adaptively determining the optimal mode number (K) and penalty factor (α) for VMD. The maximum correlation coefficient method is applied to reconstruct the AE waveform, and both A0 and S0 Lamb wave modes are identified in the wavelet time–frequency domain using CWT. Furthermore, an AE source localization algorithm based on the time difference of arrival and the geometric relationship between two sensors is developed, utilizing the group velocity of the S0 mode. The proposed method effectively identifies acoustic wave modes, achieving an average relative localization error of approximately 0.48% for HNS.

A triangular acoustic emission source location method considering its anisotropic propagation behavior on the surface of finger-joint-laminated board

ABSTRACT To understand the anisotropic propagation characteristics of acoustic emission (AE) signals in finger-joint-laminated boards, an improved planar localization method for AE sources is proposed. Initially, the AE source was generated by the pencil-lead break (PLB) at a fixed position on the surface of the glued wood specimen, and two AE sensors were arranged in different directions at a fixed distance of 60 mm, and the AE propagation velocity was calculated according to the time difference of arrival (TDOA). For clarity, the grain parallel direction was defined as 0 degrees, with velocities measured every 10 degrees through a counterclockwise rotation. Using these measurements, two angular anisotropic wave velocity models were developed. Subsequently, three AE sensors arranged linearly on the specimen surface pinpointed the AE source’s location. This source location problem was formulated as a multi-parameter optimization model, constrained by the geometric relationships between the AE source and the sensors. A particle swarm optimization (PSO) algorithm was utilized to estimate the AE source’s angles relative to each sensor. The findings indicate that the located AE sources deviated by 6.0–19.0 mm from the predetermined PLB positions, with inaccuracies largely attributed to the anisotropic AE wave velocity models’ precision.

Acoustic emission source location in complex structures based on artificial potential field-guided rapidly-exploring random tree* and genetic algorithm

Towards more accurate and easy-to-implement damage detection in large-scale complex structures, a novel acoustic emission (AE) source location method is developed based on artificial potential field-guided rapidly-exploring random tree* (APF-RRT*) and genetic algorithm (GA). APF-RRT*, which combines the excellent obstacle avoidance ability of RRT* with the path planning efficiency of APF, is introduced to adaptively estimate the shortest distances from the damage source to AE sensors. The shortest distances are obtained as the actual propagation distances of waves and then embedded into the modified error function, where GA is employed as an optimization scheme to evaluate the source location via iterations. Through the experiment on a full-scale high-strength bolt joint plate with a series of bolt holes, the effectiveness and superiority of the proposed method were validated. It achieved a better source location performance with lower mean absolute error and standard deviation than the time-of-arrival (TOA) method, delta-T mapping method, and machine learning-improved methods based on Gaussian process (GP) and artificial neural network (ANN), respectively. The primary contributions of the proposed method lay in abandoning the straight-wave-propagation assumption of the traditional TOA method by adaptively taking into account the geometric obstacles in complex structures, and removing the need for a large amount of training data and burdensome pencil lead break (PLB) tests required by data-driven location methods.

A hierarchical multistage holistic model for acoustic emission source monitoring in composites

Abstract This paper introduces a multistage smart structural health monitoring (SHM) model for carbon-fiber composites, with a focus on multiple types of acoustic emission (AE) source localization and classification. The SHM model uses time-frequency data from various AE events (such as tool drops, impact, and artificial debonding) across different zones of a composite structure. The SHM strategy demonstrates a robust smart monitoring of composites with high accuracy. Further, a hypothesis testing has been carried out that supports the superiority of a 2-stage identification process, revealing statistically significant higher accuracy and confidence intervals across all zones and AE source types. This research establishes a novel framework for solving a hierarchical multistage holistic damage source identification problem, offering robustness in identifying various damage scenarios and quantifying associated prediction uncertainties.

Localization of AE sources in rocks improved by enhanced arrival time localization

Accurate localization of Acoustic Emission (AE) events in rock structures is crucial for investigating rock failure mechanisms. This paper presents a comprehensive analysis of 3D AE localization in basalt-type rock samples, utilizing a novel combination of three powerful techniques: the Simulated Annealing Algorithm (SAA), an innovative AIC-EEMD picking method, and a velocity-free approach to wave propagation. We employed a localized Hsu–Nielsen source to generate elastic waves, which were captured by six AE sensors strategically placed on the rock surface. To address challenges in identifying P-wave arrivals, particularly in noisy or spurious data, we developed an innovative technique that integrates the Akaike Information Criterion (AIC) with Empirical Ensemble Mode Decomposition (EEMD) to enhance signal clarity. The performance of our proposal was validated achieving an average absolute distance error of 3.74 mm. Compared to traditional methods, our technique demonstrates superior accuracy and offers enhanced robustness in the presence of noise.

Two-Step AE Source location for Detecting Tendon Rupture inside PC Box-Girder in-Service

In order to identify tendon rupture inside a prestressed concrete (PC) girder, new acoustic emission (AE) location system is necessary to be practically applied. In PC girder of an in-service highway, PZT sensors are installed at a web and a deck of the PC girder, and elastic waves generated by tendon rupture is simulated by steel ball impact. AE source location assuming the homogeneous velocity distribution is applied in a development view of the box-girder. Then to verify the accuracy of the location results, the effect of elastic-wave propagation at a joined corner between the web and the deck is simulated by FDM. As a result, the attenuation of first-arrival P waves was observed at the joined corner. It implies that wave energy is so attenuated that apparent velocity decreases. Finally, AE locations lead to erroneous solutions in the development view solved with a constant velocity. To solve the problem, a twostep location system is developed. In the first step, two-dimensional plane, where the tendon rupture is to be identified, is selected from the 1st and 2nd arrivals of AE Hits. Then, in the 2nd step, AE locations are determined from remaining AE sensors located on this plane. By applying this system, the location errors are within 5 % at the both web and deck in a 15 m long PC box-girder.

Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

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.

Study on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression in high-performance concrete with full aeolian sand

The issues of environmental degradation due to natural sand overextraction are becoming increasingly severe. In desert regions, utilizing aeolian sand as a substitute for natural sand in construction projects facilitates the resource economization and the effective management of desertification. The concrete industry is expected to significantly contribute to this objective, as high-performance concrete with full aeolian sand (FA-HPC) has demonstrated excellent mechanical properties. However, current researches have yet to establish a rational damage evolution law for uniaxial compression in FA-HPC from the perspective of damage characteristics. In this study, uniaxial compression tests were conducted on FA-HPC with different mixture proportions. Simultaneously, the stress-strain behavior was examined, and acoustic emission (AE) signals were collected and localized. Based on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression, regression analyses of the stress-strain and AE cumulative ringing count for FA-HPC were performed. Damage evolution model for effective stress and damage variable were established and validated with experimental data to confirm their applicability. The scale and shape parameters of the model were predicted using the mixture proportions as independent variables. The results show that the rationality of the damage evolution law assumption was validated through AE source localization. The goodness of fit values for damage variable and effective stress equation range from 0.87 to 0.98, with the ascending segment and the vicinity of the peak point closely matching the experimental data, indicating that the Weibull distribution function-based stochastic damage model for uniaxial compression is applicable for FA-HPC. The predicted values of the damage evolution model parameters based on the mixture proportions align well with the measured values, achieving a goodness of fit of 0.99, thereby guiding the study of FA-HPC's compressive characteristics prior to the formation of primary cracks. Notably, there remains a need to propose corrective equations to enhance the correlation between regression curves and experimental data in the late failure stages of FA-HPC in future research, achieving precise predictions by considering the interactions of multiple factors.

Experimental and Numerical Investigation of Acoustic Emission Source Localization Using an Enhanced Guided Wave Phased Array Method.

To detect damage in mechanical structures, acoustic emission (AE) inspection is considered as a powerful tool. Generally, the classical acoustic emission detection method uses a sparse sensor array to identify damage and its location. It often depends on a pre-defined wave velocity and it is difficult to yield a high localization accuracy for complicated structures using this method. In this paper, the passive guided wave phased array method, a dense sensor array method, is studied, aiming to obtain better AE localization accuracy in aluminum thin plates. Specifically, the proposed method uses a cross-shaped phased array enhanced with four additional far-end sensors for AE source localization. The proposed two-step method first calculates the real-time velocity and the polar angle of the AE source using the phased array algorithm, and then solves the location of the AE source with the additional far-end sensor. Both numerical and physical experiments on an aluminum flat panel are carried out to validate the proposed method. It is found that using the cross-shaped guided wave phased array method with enhanced far-end sensors can localize the coordinates of the AE source accurately without knowing the wave velocity in advance. The proposed method is also extended to a stiffened thin-walled structure with high localization accuracy, which validates its AE source localization ability for complicated structures. Finally, the influences of cross-shaped phased array element number and the time window length on the proposed method are discussed in detail.

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).

An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures

An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures

Acoustic emission source localization for complex structure with multi-holes based on heuristic adaptive pathfinding and gradient descent method

Space debris poses a safety hazard to the operation of spacecraft, and the study of impact localization for spacecraft structures is of significance for the timely discovery of the impact location and the safeguarding of spacecraft and personnel safety. However, existing studies have mainly focused on continuous structures, and discontinuous structures of spacecraft with portholes and orifice-containing valves have been understudied. Therefore, in this article, the shortest propagation path algorithm of the signal and the acoustic emission source localization method applicable to multi-holes discontinuities structures are proposed. After calculating the signal arrival time difference, the localization method calculates the shortest propagation path of Lamb wave in complex multi-holes structure by the heuristic adaptive path-finding algorithm, which calculates distance differences traveled by the signal propagation to each sensor. The heuristic adaptive path-finding algorithm first constructs a map set based on the geometrical features of the structure, after which it searches for each partial path in the final path by heuristic computation to identify the shortest signal propagation trajectory. The objective function is established based on the target structural characteristics and sensor position parameters, of which the parameters of the equations are optimized based on the new travel difference-of-distance values generated from the results of the heuristic adaptive pathfinding calculation. Finally, the acoustic emission source coordinates are updated with a gradient descent strategy, which has an average localization error of 0.956 cm in 252 experiments and can effectively realize the impact localization calculation of multi-holes complex structures.

Combined Passive and Active Ultrasonic Stress Wave Monitoring of Concrete Structures: An Overview of Data Analysis Techniques and Their Applications and Limitations

Combined passive [or acoustic emission (AE)] and active ultrasonic stress (US) wave monitoring has been shown to provide a more holistic picture of ongoing fracture processes, damage progression, as well as slowly occurring aging and degradation mechanisms in concrete structures. Traditionally, different data analysis techniques have been used to analyze the data generated from these two monitoring approaches. For AE data analysis, for instance, signal amplitudes, hit rates, source localization, and b-value analysis have been used to detect and locate cracking. On the other hand, amplitude tracking, magnitude squared coherence (MSC), and coda wave interferometry (CWI) are examples that have been employed for US data analysis. In this presentation, we explore these data analysis techniques and show where their respective applications and limitations might be. After providing an overview of the monitoring approach and the different data analysis techniques, results and observations from select laboratory experiments, as well as an in-service structure are discussed. Finally, suggestions for further work are proposed.

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.

Research on features and localization of AE signals from the mechanical body of industrial robots based on WD-EMD

Abstract In order to reveal the propagation characteristics of acoustic emission (AE) signals in the body of industrial machinery, the characteristic frequencies and wave speeds of AE signals propagated on the interior and exterior surfaces (IS and ES) of the body were extracted. Subsequently, an algorithm utilizing characteristic frequency and time difference of arrival (TDOA) is proposed for the identification and localization of AE sources. Initially, an AE source is induced on the IS and ES of the body using the pencil-lead break method in accordance to ASTM standards, and the AE signal is captured by two piezoelectric sensors at a sampling frequency of 3 MHz. Then, to avoid the limitations of wavelet decomposition using self-selected wavelet scale and the problem that a single indicator cannot properly evaluate the correlation between independent mode functions (IMFs) with the original signal, this paper will conduct 4-layer wavelet decomposition of the original signal according to the response frequency range of the sensor, and select the wavelet details within the stable range of the response frequency of the sensor for preliminary reconstruction,and then the empirical mode decomposition (EMD) method is used to decompose the de-noised AE signal into 7 IMFs, and the AE waveform is reconstructed by the combined information of correlation coefficient and variance accounted for. Secondly, the reconstruction method combined with EMD analysis and a single index is compared with the proposed method in this paper to verify the reliability of the proposed method. In addition, the frequency domain characteristics of AE signal propagation process on the IS and ES of the body are extracted. Finally, based on the TDOA principle, the propagation speed of AE signal on the IS and ES of the body is calculated. Based on the geometric relationship between the AE source and two sensors, an algorithm for the location of the AE source is proposed. The results show that the proposed signal reconstruction method can effectively extract the features of AE signals, and the average positioning accuracy of the localization algorithm based on characteristic frequency and TDOA reaches 0.86%.

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 novel method for acoustic emission source location in CFRP-concrete debonding using ΔT mapping and DBSCAN algorithm

Concrete-CFRP composite structures exhibit a stiff behavior, leading to sudden failure. Monitoring the brittle nature of this failure is crucial, and the acoustic emission (AE) technique provides a promising tool for locating and classifying damage. However, the conventional source location methodology assumes that the acoustic wave travels in a straight path with constant velocity. This assumption does not hold true for concrete-CFRP composite structures, where the presence of microcracks and CFRP layers induces anisotropy and defects, altering the wave speed and traveling direction. This, in turn, introduces errors in the source location. To address these challenges, this research presents a novel source location method using ΔT maps and achieving convergence through the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The methodology demonstrates significant improvement in AE source location when applied to concrete-CFRP composite specimens in the presence defects with varying magnitudes. Additionally, the proposed method shows effectiveness in monitoring damage progression when applied to single-lap shear tests performed on concrete specimens with attached CFRP laminate.