Acoustic Emission Detection Research Articles

Materials Design and Structural Health Monitoring of Horizontal Axis Offshore Wind Turbines: A State-of-the-Art Review

In recent decades, Offshore Wind Turbines (OWTs) have become crucial to the clean energy transition, yet they face significant safety challenges due to harsh marine conditions. Key issues include blade damage, material corrosion, and structural degradation, necessitating advanced materials and real-time monitoring systems for enhanced reliability. Carbon fiber has emerged as a preferred material for turbine blades due to its strength-to-weight ratio, although its high cost remains a barrier. Structural Health Monitoring Systems (SHMS) play a vital role in detecting potential faults through real-time data on structural responses and environmental conditions. Effective monitoring approaches include vibration analysis and acoustic emission detection, which facilitate early identification of anomalies. Additionally, robust data transmission technologies are essential for SHMS effectiveness. This paper reviews material design strategies, data acquisition methods, and safety assessment techniques for OWTs, addressing current challenges and future directions in the field.

Evaluation of On-Vehicle Bone-Conduct Acoustic Emission Detection for Rail Defects

Evaluation of On-Vehicle Bone-Conduct Acoustic Emission Detection for Rail Defects

Erosion monitoring of Coriolis flowmeter tubes for slurry flow measurement through acoustic emission detection

Erosion monitoring of Coriolis flowmeter tubes for slurry flow measurement through acoustic emission detection

Electrochemically-Resolved Acoustic Emissions Analysis for Li-Ion Battery Materials

Operando non-destructive evaluation (NDE) techniques for Li-ion batteries are the gold standard for gaining physical insights into a cell. These methods have the potential to transform battery formation optimization, electrode and electrolyte characterization, and state-of-health (SoH) and remaining useable lifetime metrics by providing an orthogonal data stream to supplement conventional electrochemical data. A well-known NDE method is acoustic emission (AE) testing. AEs are elastic waves that are generated by a release of energy in a material due to an irreversible change in internal structure. Studies have documented the detection of AEs on the surface of Li-ion batteries during cycling1–3, but no clear connection has been established between AEs and SoH metrics due to low reproducibility from seemingly identical battery systems and poor interpretability of the acoustic activity.Herein, we report the methodology for performing an electrochemically-resolved acoustic emissions analysis on Li-ion batteries to identify specific degradation mechanisms, using graphite and Ni0.8Mn0.1Co0.1O2 (NMC811) as case studies. First, careful identification and elimination of electromagnetic interference allowed for improved reproducibility in the number of AEs and cumulative energy per cycle. Next, our use of cyclic voltammetry as the simultaneous electrochemical method while measuring AEs allowed for a direct correlation between acoustic activity and specific degradation mechanisms, such as gas generation during SEI formation and particle fracture during NMC811 delithiation. The degradation mechanisms were confirmed using complementary destructive and non-destructive methods. Particle fracture was identified with ex-situ SEM imaging of electrodes and operando XRD during cyclic voltammetry. Gas generation was inferred by comparing to online electrochemical mass spectrometry (OEMS) data during battery formation in literature.4 Finally, a careful waveform analysis using the wavelet transform was capable of distinguishing AEs from different degradation mechanisms using multi-resolution features and identified these mechanisms during standard battery operation as well. Overall, acoustic emissions testing of Li-ion batteries represents a unique and promising methodology for identifying degradation mechanisms during normal battery operation and could offer a significant improvement to conventional health and lifetime prediction metrics.(1) Ohzuku, T.; Tomura, H.; Sawai, K. Monitoring of Particle Fracture by Acoustic Emission during Charge and Discharge of Li/MnO2 Cells. J. Electrochem. Soc. 1997, 144 (10), 3496–3500. https://doi.org/10.1149/1.1838039.(2) Kircheva, N.; Genies, S.; Brun-Buisson, D.; Thivel, P.-X. Study of Solid Electrolyte Interface Formation and Lithium Intercalation in Li-Ion Batteries by Acoustic Emission. J. Electrochem. Soc. 2011, 159 (1), A18–A25. https://doi.org/10.1149/2.045201jes.(3) Schweidler, S.; Bianchini, M.; Hartmann, P.; Brezesinski, T.; Janek, J. The Sound of Batteries: An Operando Acoustic Emission Study of the LiNiO2 Cathode in Li–Ion Cells. Batteries & Supercaps 2020, 3 (10), 1021–1027. https://doi.org/10.1002/batt.202000099.(4) Zhang, B.; Metzger, M.; Solchenbach, S.; Payne, M.; Meini, S.; Gasteiger, H. A.; Garsuch, A.; Lucht, B. L. Role of 1,3-Propane Sultone and Vinylene Carbonate in Solid Electrolyte Interface Formation and Gas Generation. J. Phys. Chem. C 2015, 119 (21), 11337–11348. https://doi.org/10.1021/acs.jpcc.5b00072.

Deep Learning-Based Low-Frequency Passive Acoustic Source Localization

This paper develops benchmark cases for low- and very-low-frequency passive acoustic source localization (ASL) using synthetic data. These cases can be potentially applied to the detection of turbulence-generated low-frequency acoustic emissions in the atmosphere. A deep learning approach is used as an alternative to conventional beamforming, which performs poorly under these conditions. The cases, which include two- and three-dimensional ASL, use a shallow and inexpensive convolutional neural network (CNN) with an appropriate input feature to optimize the source localization. CNNs are trained on a limited dataset to highlight the computational tractability and viability of the low-frequency ASL approach. Despite the modest training sets and computational expense, detection accuracies of at least 80% and far superior performance compared with beamforming are achieved—a result that can be improved with more data, training, and deeper networks. These benchmark cases offer well-defined and repeatable representative problems for comparison and further development of deep learning-based low-frequency ASL.

Metal surface crack depth laser acoustic emission detection method based on multivariate feature adaptive extraction and cross-modal interaction fusion

Surface crack depth detection of metal structures is of great significance to ensure the safe of equipment. However, the commonly used detection methods currently suffer from sensitivity and imaging dimension limitations, which make it difficult to ensure accuracy and feature extraction. To this end, a laser acoustic emission crack depth detection method that combines modal decomposition and deep learning is proposed. The new method is more sensitive to elastic wave changes that detect the depth of microscopic cracks and shows higher accuracy than traditional detection methods. At the same time, frequency-adaptive feature mode decomposition of multivariate signals and color multimodal feature fusion are used to solve the difficulty of time–frequency domain feature mining in current research. The crack depth detection network CDDNet is constructed using an attention-based fusion backbone, a novel cross-modal interaction fusion, and a hierarchical information fusion strategy. Its transfer learning achieves a crack detection accuracy level of 0.05 mm and outperforms other models in terms of accuracy, reaching 93.6 %. This paper shows that the proposed transfer learning laser acoustic emission technology and supporting data processing methods can effectively establish a connection between the experimental and simulation datasets, solving the current challenges of crack depth detection in terms of micro-precision and data processing.

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.

Unsupervised weathering identification of grottoes sandstone via statistical features of acoustic emission signals and graph neural network

Weathering features of sandstone heritage can be recognized by using artificial intelligence (AI) based surrogate models, and most models perform classification tasks for types based on precise labels. But there are lack of prior validated knowledge of the weathering or untagged historical data for complex weathering conditions in many cases. To this aim, a unsupervised graph neural network (GNN) based on the statistical features of the acoustic emission (AE) signals is constructed. Firstly, taking unweathered sandstone as a reference, we define 4 weathering levels of sandstone ranging from I to IV based on pore indicators. We selected 11 statistical features that are high correlated with pore of sandstone. Then, this GNN is constructed and trained by 2880 sets of statistical measured AE signals. Compared with AEs, LOF and IF models, GNN achieves the best identification performance among the four evaluation criteria. Each iteration of the GNN network is fitting the feature information of the signals and their neighbors. By data dimensionality reduction techniques, when the GNN stops iterating, it will be easy to distinguish unweathered AE signals from weathered one by comparing the reconstruction error of each signal. Furthermore, when the nearest neighbor’s k gradually increases, the AUC of GNN also gradually increases and then tend to stable when k equals to 50–100. While the hidden layers of the network aggregates less information about the neighborhood features of the signals and cannot distinguish significantly between unweathered and weathered signals when the value of k is small. As the depth of the network deepens, the feature values between signals become more and more similar, their reconstruction errors in the output layer of the network to become more similar, making it difficult to distinguish unweathered AE signals from weathered AE signals via GNN. Meanwhile, GNN adopts more AE features and considers the similarity between each features. This can greatly eliminate various errors caused by wave velocity measurement, greatly improving the robustness of AE detection. Hence, the GNN model presented addresses the limitations of relying solely on P-wave velocity measurements to assess the degree of sandstone weathering at stone cultural heritage.

Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques.

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures.

Cryogenic damage behavior of carbon fiber reinforced polymer composite laminates via fiber-optic acoustic emission

Cryogenic temperatures cause significant changes in the mechanical response and microscopic failure mechanisms of carbon fiber reinforced polymer (CFRP) composites. However, the mechanisms by which cryogenic temperatures affect composites are not yet fully understood due to a lack of adequate in-situ characterization techniques. Herein, the cryogenic damage evolution process of CFRP composites was investigated by constructed fiber-optic acoustic emission (AE) detection system. Tensile damage behavior of woven CFRP composites at 300 K, 153 K and 77 K was evaluated by AE characteristic response and cluster analysis combined with scanning electron microscopy. According to the results, increased damage activity within the composites at cryogenic condition promoted the release of mechanical energy, as well as an increase in the contribution of fiber damage to overall damage, which are the dominant micro-strengthening mechanisms of composite laminates under cryogenic conditions. This study provides a novel explanation for understanding the cryogenic damage behavior of composites.

Pipeline Leak Detection System for a Smart City: Leveraging Acoustic Emission Sensing and Sequential Deep Learning

This study explores a novel approach utilizing acoustic emission (AE) signaling technology for pipeline leakage detection and analysis. Pipeline leaks are a significant concern in the liquids and gases industries, prompting the development of innovative detection methods. Unlike conventional methods, which often require contact and visual inspection with the pipeline surface, the proposed time-series-based deep learning approach offers real-time detection with higher safety and efficiency. In this study, we propose an automatic detection system of pipeline leakage for efficient transportation of liquid (water) and gas across the city, considering the smart city approach. We propose an AE-based framework combined with time-series deep learning algorithms to detect pipeline leaks through time-series features. The time-series AE signal detection module is designed to capture subtle changes in the AE signal state caused by leaks. Sequential deep learning models, including long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and gated recurrent units (GRUs), are used to classify the AE response into normal and leakage detection from minor seepage, moderate leakage, and major ruptures in the pipeline. Three AE sensors are installed at different configurations on a pipeline, and data are acquired at 1 MHz sample/sec, which is decimated to 4K sample/second for efficiently utilizing the memory constraints of a remote system. The performance of these models is evaluated using metrics, namely accuracy, precision, recall, F1 score, and convergence, demonstrating classification accuracies of up to 99.78%. An accuracy comparison shows that BiLSTM performed better mostly with all hyperparameter settings. This research contributes to the advancement of pipeline leakage detection technology, offering improved accuracy and reliability in identifying and addressing pipeline integrity issues.

Effects of polyurethane hardness on the propagation of acoustic signals from partial discharge

AbstractPolymeric insulation is a critical component of high voltage systems. However, exposure to high electric stress can cause partial discharges (PDs) to occur and may result in the deterioration of insulation and lead to dielectric failure. These PD events are accompanied by the production of acoustic pressure impulses in the polymer. Detection of these acoustic pressure impulses can reveal the presence of PDs and locate their source. However, analysing the detected acoustic emission (AE) signal is challenging. The acoustic pressure source's nature and the propagating medium's properties, such as density, viscosity, and elasticity, significantly affect the propagating AE signal. The effects of the hardness of the polyurethane (PU) on the propagating AE signal are reported by the authors based on results obtained from laboratory experiments. It was observed that the decay rate in the magnitude of the acoustic impulse was high in PU at all hardness levels following an exponential behaviour. The analysis of the frequency spectra indicates that the higher frequency components attenuate more strongly with distance. These laboratory results can be valuable for engineers and industries as they provide valuable insight into how the physical characteristics of a material affect the propagation characteristics of AE signals during the detection and location of PD source using the AE detection technique.

Partial discharge monitoring by improved PGC-arctan algorithm

This paper proposes an improved PGC-Arctan demodulation algorithm for detecting partial discharge signals in transformers. Compared with the traditional PGC-Arctan algorithm, the improved algorithm extracts carrier frequency difference and carrier phase delay by fundamental mixing and using low-pass filtering on the interference signal, and compensates the phase modulation depth by high order frequency, reducing THD and enhancing SNR. The algorithm is theoretically analyzed, simulated and verified through experiments. In the 200 kHz acoustic emission signal detection experiment, the SNR and THD of the improved algorithm are 49.63 dB and 1.34%, respectively, which is 13.59 dB higher than the SNR of the traditional algorithm, and the THD is reduced by 76.61%, and the harmonic distortion is well suppressed. The system is used to detect weak ultrasonic signals generated during partial discharge of transformers and compare them with PZT. The results show that the system has the potential to be used to monitor the partial discharge signal in transformers.

Failure mechanisms and acoustic responses of cylindrical lithium-ion batteries under compression loadings

To understand the crashworthiness safety of lithium-ion battery (LIB) comprehensively, acoustic emission (AE) testing technology is introduced to explore the mechanical response and failure mechanism. In this paper, the compression process and failure behavior of 18650 LIBs under plane compression, local indentation and three-point bending are experimentally investigated. The effects of indenter diameter, state of charge (SOC) and capacity of the battery on the force-voltage-temperature responses are discussed in detail. The relation between the failure behavior and acoustic response of LIB is studied in combination with AE detection technique. The results show that the deformation of the battery can be divided into three stages before the failure, including the stress on the shell, compression on the internal gap and electrolyte, and stress on the whole. The failure inside the battery is caused by fold, shear fracture, local buckling and tensile break under different loading forms. The smaller the diameter of the indenter is, the lower bearing capacity at the point of failure is. High-SOC batteries experience higher forces and displacements at the failure point. High-capacity batteries have poor bearing capacity and are more prone to thermal runaway. AE signal can be detected at special points during the compression. Combined with the acoustic response of the battery, the whole deformation process is divided into five stages. The researches will provide a guidance for the failure detection and safety evaluation of LIBs.

Measurement of CO2 leakage from pipelines under CCS conditions through acoustic emission detection and data driven modeling

CO2 leakage from carbon capture and storage (CCS) networks may lead to ecological hazards, bodily injury and economic losses. In addition, captured CO2 often contains impurities which affect the leakage behavior of CO2. This paper presents a method for continuous and quantitative measurements of CO2 leakage flowrate and the volume fraction of impurities by combining data-driven models with acoustic emission (AE) and temperature sensors. Three data-driven models based on artificial neural network (ANN), random forest (RF), and least squares support vector machine (LS-SVM) algorithms are established. The outputs from the three data-driven models are then integrated to give improved results. Experimental work was conducted on a purpose-built CO2 leakage test rig under a range of conditions. N2 was injected to the CO2 gas stream as an impurity medium. Results show that the integrated model yields a relative error within ±4.0 % for leakage flowrate and ±3.4 % for volume fraction of N2.

Classification modeling of valve internal leakage acoustic emission signals based on optimal wavelet scattering coefficients

In order to achieve acoustic emission detection in valve internal leakage, it is essential to extract features, and establish an accurate mathematical model. Current valve internal leakage acoustic emission signal (VILAES) classification models mostly rely on human experience for selecting features, resulting in low accuracy for small leakage conditions. This paper combines the wavelet scattering transform (WST), Relief-F algorithm and AdaBoost.M1 algorithm, and proposes to use the optimal wavelet scattering coefficients as features to establish the VILAES classification model accurately for small leakage. Firstly, the first three order wavelet scattering coefficients of the VILAES are automatically extracted using WST and are transformed into one-dimensional features. Secondly, the Relief-F algorithm is employed to select the optimal feature subset. Finally, the optimal wavelet scattering coefficients and pressures are used as inputs to establish a classification model for VILAES and achieve an accuracy over 96.80% for small leakage.

Progress of MEMS acoustic emission sensor: a review

PurposeBased on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments.Design/methodology/approachThis paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future.FindingsPiezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential.Originality/valueIn this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures.

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.

Alternating load failure analysis under the high-temperatures vibration of thermal barrier coatings

During thermal barrier coatings (TBCs) service, high-temperature and vibration are environmental factors that cannot be ignored. The vibration fatigue damage mode of TBCs under operating conditions that couple vibrations with high temperatures is unclear. In this paper, a high-temperature vibration coupled environment simulator was developed to investigate the evolution of high-temperature vibration fatigue damage modes of LaZrCeO/7-YSZ(LZC/YSZ) TBCs under alternating loads by employing acoustic emission detection. The results show that in the initial fatigue stage, the damage appears as discrete micro-cracks formed by the initial defects in the TBCs. With the increase in the number of cycles, the initiation of transverse microcracks began to occur inside the LZC layer and at the LZC/YSZ interface. When the transverse microcracks accumulate to the critical value, the applied alternating loads accelerate the propagation of opening interfacial cracks. Finally, the columnar crystal structures of the ceramic layers fracture at different heights under cyclic loading by high-temperature vibrations.

Research on a Corrosion Detection Method for Oil Tank Bottoms Based on Acoustic Emission Technology.

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks which is aimed towards specialized places such as oil storage tanks with high explosion-proof requirements, such as cave oil tanks and buried oil tanks. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. An independent channel AE detection system is designed, which can store the collected data in a piece of independent explosion-proof equipment, and can analyze and process the data in a safe area after the detection, solving the problems of a short signal acquisition distance and the weak safety protection applied to traditional AE instruments. A location analysis of the AE sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open-tank tests confirms that using AE technology effectively captures corrosion signals from oil storage tanks' bottoms. The feasibility of carrying out online inspection under the condition of oil storage in vertical steel oil tanks was verified through a comparison with open inspections, which provided a guide for determining the inspection target and opening order of large-scale oil tanks.