Micro-discharge evolution in constant voltage bipolar PEO process on ZL101 Al-Si alloy monitored by acoustic emission technique.

Acoustic emission characteristics of the brittle-ductile transition for unsaturated rock during drilling process with confining pressures.

Effects of Crude Oil Intrusion on Mechanical Damage Characteristics and Acoustic Emission Behavior of Coal

Damage evolution and acoustic emission behaviors of grouted reinforcement specimens under increasing-amplitude fatigue loading at different frequencies

Application of acoustic emission for flow regime detection in horizontal two-phase pipeline transport

Mechanical performance and life-cycle carbon reduction benefits of 3D-printed permanent-formwork columns filled with low-magnesia concrete

Experimental study on mechanical properties of polypropylene fiber foamed concrete after exposure to high temperatures

Identification of failure precursors in layered rock mass based on temporal-spatial-intensity clustering characteristics of acoustic emission

Machine learning-assisted acoustic emission localization of simulated wire-break events in prestressed strands of high-speed railway box girders

Fusing acoustic emission and deep learning for automatic identification of progressive rock fracture

A comprehensive experimental method for monitoring and characterizing the damage evolution of Reinforced Concrete (RC) beams was conducted under three-point bending load, using a combination of Hilbert-Huang Transform (HHT) based Acoustic Emission (AE) technology and Digital Image Correlation (DIC). AE sensors capture the passive elastic waves emitted during damage progression, and their signals are analyzed using Empirical Mode Decomposition (EMD) to extract key features such as frequency and amplitude. Simultaneously, DIC and strain gauges monitor the deformation and stress distribution on the beam surface and reinforcement. The experimental results demonstrate a clear correlation between AE signal characteristics and the various stages of crack initiation, propagation, and structural failure. AE signals exhibit stage-specific spectral changes, evolving from low-frequency, low-amplitude patterns during micro crack formation to high-frequency, high-amplitude signals during major crack propagation. The DIC strain fields visually capture deformation concentration zones that align well with AE signal trends. This integrated monitoring approach enables the quantitative evaluation of structural damage and offers a reliable method for health monitoring of concrete structures. Subsequently, a database of characteristic parameters for different damage stages is established, providing theoretical support for the application of HHT in analyzing non-stationary signals in Structural Health Monitoring (SHM). • Combined with DIC data, the stress-strain behavior and damage progression of the RC beam under different loading conditions are captured. • Various characteristic vales such as frequency, amplitude, and load during the expansion and damage evolution process are obtained. • The results listed are the achievements of whole-field deformation measurement and damage monitoring throughout the entire process.

Early warning of battery failure via spontaneous acoustic emission: a case study of overcharging in sodium-ion batteries

Mechanical and acoustic emission characteristics of sandstone after high-temperature and water-cooling treatment under true triaxial cyclic loading

ABSTRACT In this study, acoustic emission (AE) is employed to detect incipient damage in polycarbonate, while wavelet synchrosqueezing transform (WSST) enhanced time-frequency resolution, enabling accurate characterization and interpretation of AE signals. Quasi-static mode-I experiments are performed using tapered double cantilever beam (TDCB) specimens with a 25 mm pre-crack in displacement control mode. The test results reveal low-frequency AE signals (25–30 kHz) appeared during elastic loading, while high-frequency components (65–120 kHz) indicated localized yielding near the crack tip. Scattered frequencies emerged just before crack initiation due to fracture processes. In contrast to the continuous wavelet transform (CWT), WSST sharpens the time-frequency representation by limiting energy smearing. The WSST achieves higher time-frequency resolution by squeezing and reconstructing the CWT coefficients along the frequency axis. This improved frequency localisation enables the accurate tracking of transient features, particularly during localised yielding and incipient crack propagation, as observed in this study. To complement the experimental results, finite element analysis (FEA) is performed to simulate the stress and strain fields near the crack tip by using an elastic-perfectly plastic model in ABAQUS. The evolving plastic deformation near the crack tip shows good correlation with cumulative AE energy. This study offers a promising technique to detect incipient failure.

ABSTRACT The performance degradation and safety risks of lithium-ion batteries under low-temperature conditions are closely related to changes in their electrochemical–mechanical behavior. In this study, acoustic emission (AE) techniques were used to investigate a distinctive class of dual-waveform AE signals observed during low-temperature discharge. Wavelet coherence analysis demonstrated strong time–frequency correlations between the paired waveforms, indicating a common or strongly coupled acoustic source. In addition, the time interval between the two waveforms gradually stabilized during discharge, suggesting systematic changes in the internal conditions affecting acoustic wave propagation. Time–frequency energy analysis revealed that decreasing temperature caused the peak frequency of AE signals to shift from the mid-frequency range (100–200 kHz) toward lower frequencies (<100 kHz). Simultaneously, AE activity evolved from multi-band, multi-scale behavior to a response dominated by low-frequency components associated with larger-scale structural processes. These results demonstrate that low-temperature conditions significantly affect AE characteristics and highlight the sensitivity of AE techniques to temperature-dependent internal responses, providing insights into battery condition assessment under low-temperature operation.

ABSTRACT Laser powder bed fusion (LPBF) is widely used for metal additive manufacturing but remains highly sensitive to process parameters, which can cause porosity, lack of fusion, reduced strength, and poor repeatability. This paper proposes a multi-modal deep regression architecture for real-time closed-loop quality control and continuous LPBF process monitoring. Instead of image-based monitoring, the system uses lightweight numerical thermal, acoustic, and machine-level process signals to support computationally efficient edge deployment. An artificial multi-modal dataset is developed using temperature features, acoustic emission features, and key process parameters to represent realistic LPBF operating conditions. A multi-output deep neural network predicts the continuous quality index, porosity risk, and laser power correction for adaptive control. Experimental results show strong predictive performance, achieving an R² of 0.977, with total MAE and RMSE values of 0.00577 and 0.00987, respectively. Compared with single-output deep learning and traditional machine learning baselines, the proposed model reduces MAE and RMSE by 91% and 89%. Closed-loop control further reduces quality-index variance by 21.66%, improving process stability.

With the rapid development of structural health monitoring, intelligence has become an important trend in cement-based materials. Recycled carbon fibers (RCF) feature favorable electrical conductivity. This study investigates the effects of varying fiber contents on the piezoresistive properties of cement mortar. The relationship between electrical signals and stress under failure and cyclic loading was analyzed. Piezoresistive performance was evaluated in terms of linearity, sensitivity, repeatability, and hysteresis, and the damage-sensing capacity was explored via acoustic emission tests. The results reveal that the incorporation of RCF significantly enhances the piezoresistive properties of cement mortar, exhibiting a favorable linear correlation between the fractional change in resistance and stress. Acoustic emission signals and resistivity variations can effectively characterize the internal damage evolution of mortar. Under cyclic loading, recycled carbon fiber cement mortar (RCFCM) presents good repeatability and stability. With rising loading amplitude, the irreversible damage of the conductive network aggravates, and the hysteresis effect increases.

Coal-rock composite structures are common in deep roadway roofs and floors, and their instability is strongly affected by excavation-induced unloading and coal-rock relative thicknesses. To clarify their fracture characteristics and failure mechanisms under realistic stress adjustment paths, laboratory true triaxial loading-unloading tests were conducted on 100mm cubic specimens with coal-rock ratios of 0:1, 1:2, 1:1, 2:1, and 1:0, combined with acoustic emission (AE) monitoring and PFC3D simulations to investigate their mechanical response, damage evolution and energy characteristics. The results show that with increasing coal-rock ratio, the failure mode gradually transitions from relatively stable splitting-shear failure in rock-dominated specimens to abrupt coal-dominated instability, and composite specimens with intermediate ratios exhibit the most significant interface-controlled X-shaped or semi-X-shaped conjugate shear damage. Due to the mismatch in elastic modulus and Poisson's ratio between coal and mudstone, distinct AE precursor peaks appear during the unloading and stress readjustment stages, which are stronger in composite specimens than in pure coal or pure rock specimens. The core quantitative findings are that the cumulative absorbed energy first increases and then decreases with increasing coal-rock ratio, reaching a maximum at a coal-rock ratio of 67%, whereas the peak strength decreases monotonically. This indicates that rock burst proneness is governed more directly by energy accumulation and release than by strength alone. Numerical simulations well reproduce the fracture process of specimens, and demonstrate that interface bond breakage promotes the conversion of stored strain energy into kinetic energy during final instability. These findings provide a mechanistic basis for evaluating dynamic instability and optimizing support strategies in deep composite strata.

Partial Discharge (PD) is one of the most critical factors contributing to the degradation of insulation systems in power transformers. Early detection, accurate localization, identification of discharge types, and assessment of discharge severity play a significant role in improving system reliability and reducing maintenance costs. In recent years, various approaches based on electromagnetic waves, acoustic emissions, frequency-domain analysis, wavelet transforms, and artificial intelligence techniques have been proposed for PD monitoring and diagnosis. However, most existing studies focus on a single task and rarely address multiple diagnostic objectives within a unified framework. This paper proposes a novel framework, termed PD-IntelliFusionNet, for simultaneous PD type diagnosis and severity assessment. The proposed framework integrates acoustic and electromagnetic sensing, physical modeling, multi-scale feature extraction, and multi-task deep learning into a unified architecture. First, acoustic emission and ultra-high-frequency signals are collected from multiple sensors. Subsequently, time-domain, frequency-domain, and wavelet-based features are extracted and combined with deep representations learned automatically by neural networks. A multi-task learning architecture equipped with an attention mechanism is then employed to perform PD type classification and severity assessment simultaneously. Furthermore, the generated diagnostic outputs can be utilized as inputs to an intelligent reinforcement learning-based decision-making system for condition monitoring and maintenance planning. Previous studies have demonstrated that the fusion of acoustic and electromagnetic information improves diagnostic accuracy and robustness. In addition, multi-task learning architectures enhance severity assessment performance by exploiting shared knowledge among related tasks. The proposed PD-IntelliFusionNet framework provides a comprehensive and scalable solution for intelligent condition monitoring of power transformers and offers a promising direction for next-generation PD diagnostic systems.

ABSTRACT This study investigated the damage characteristics of graphitised cathode carbon blocks under different electrolysis durations (0 h, 1 h, 2 h, and 3 h). By integrating high-temperature mechanical loading, acoustic emission (AE) monitoring, and three-dimensional (3D) X-ray micro-computed tomography (micro-CT), the mechanical response, AE signal evolution, and microstructural porosity changes of the blocks were systematically examined. The results demonstrated that prolonged electrolysis significantly reduced the peak compressive strength of the cathode blocks. The number of AE events decreased with increasing electrolysis duration, declining from 1,571 in the control group to 299 in the 3-hour group, representing an 80.97% reduction. Distinct variation trends were observed in both b-values and S-values, while the RA-AF distribution indicated that electrolysis reduced the proportion of shear cracks from 70.53% to 20.07%. Micro-CT analysis revealed that after 3 hours of electrolysis, the total porosity decreased from 13.84% to 8.16%, and the proportion of interconnected pores decreased from 87.65% to 82.13%. Meanwhile, the electrolyte permeability increased from 17.26% to 49.32%. Similar changes were observed in the contact area. These findings elucidate the damage evolution of graphitised cathode carbon blocks during electrolysis and provide a basis for online condition monitoring and service life prediction.