Damage source localisation in complex geometries using acoustic emission and acousto-ultrasonic techniques: an experimental study on clear aligners.

Passive non-destructive evaluation tools such as acoustic emission (AE) testing and acousto-ultrasonics (AU) approach present a complex problem in damage localisation in complex and nonhomogeneous geometries. A novel AU-guided AE frequency interpretation approach is proposed in this research work which aims at overcoming this limitation. For the experimental evaluation, the damage sources from a geometrically complex clear dental aligners are tested under cyclic compression load and their origins are evaluated. Despite the rapid worldwide diffusion of the clear aligners, their mechanical behaviour is poorly investigated. In this work, the frequency characteristics of the artificially simulated stress wave, generated from different dental positions of the clear aligners, are studied using the AU approach. These frequency characteristics are then used to analyse the AE signals generated by these aligners when subjected to cyclic compressive loading. In addition, the time domain characteristics of the AE signals are studied using their Time of Arrival (ToA). The Akaike Information Criterion (AIC) is used to estimate the ToA. These frequency and time domain characteristics of the AE signals are used to estimate the local damage origin in the clear dental aligners. This will help in identifying localised damage sources during the usage period of the aligners. Experimental results revealed significant damages in the left maxillary premolar and right maxillary third molar of the aligners.

Optimizing Building Rehabilitation through Nondestructive Evaluation of Fire-Damaged Steel-Fiber-Reinforced Concrete.

Fire incidents pose significant threats to the structural integrity of reinforced concrete buildings, often necessitating comprehensive rehabilitation to restore safety and functionality. Effective rehabilitation of fire-damaged structures relies heavily on accurate damage assessment, which can be challenging with traditional invasive methods. This paper explores the impact of severe damage due to fire exposure on the mechanical behavior of steel-fiber-reinforced concrete (SFRC) using nondestructive evaluation (NDE) techniques. After being exposed to direct fire, the SFRC specimens are subjected to fracture testing to assess their mechanical properties. NDE techniques, specifically acoustic emission (AE) and ultrasonic pulse velocity (UPV), are employed to assess fire-induced damage. The primary aim of this study is to reveal that AE parameters-such as amplitude, cumulative hits, and energy-are strongly correlated with mechanical properties and damage of SFRC due to fire. Additionally, AE monitoring is employed to assess structural integrity throughout the loading application. The distribution of AE hits and the changes in specific AE parameters throughout the loading can serve as valuable indicators for differentiating between healthy and thermally damaged concrete. Compared to the well-established relationship between UPV and strength in bending and compression, the sensitivity of AE to fracture events shows its potential for in situ application, providing new characterization capabilities for evaluating the post-fire mechanical performance of SFRC. The test results of this study reveal the ability of the examined NDE methods to establish the optimum rehabilitation procedure to restore the capacity of the fire-damaged SFRC structural members.

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.

A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water-Rock Interaction.

The mechanical properties of fissured sandstone will deteriorate under water-rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water-rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF), b value, and the natural/saturated state of fissured sandstone: state. To enhance data utilization, a 10-fold cross-validation method was employed during the model training process. The machine learning models were developed and designed to identify the instability risk of fissured sandstone under the natural and saturated states. The results demonstrated that the established RF model was capable of identifying fissured sandstone instability risks with an accuracy of 97.87%. Feature importance analysis revealed that state and b value exerted the most significant influence on identification results. The Spearman correlation coefficient was utilized to assess the correlation between input features. This study can provide technical support to identify the risk of instability of fissured sandstones under both natural and saturated water conditions. Based on the models developed in this study, it is possible to implement an early warning method for instability in fissured sandstone that meets realistic working conditions. Compared with the traditional empirical and formulaic methods, the machine learning method can more quickly process huge amounts of AE data and accurately identify the damage state of fissured sandstone.

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.

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids.

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.

Study of Acoustic Emission Signal Noise Attenuation Based on Unsupervised Skip Neural Network.

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.

Mechanical properties and acoustic emission characteristics of basalt fiber reinforced cemented silty sand subjected to freeze-thaw cycles.

Freeze-thaw (F-T) cycling poses a significant challenge in seasonally frozen zones, notably affecting the mechanical properties of soil, which is a critical consideration in subgrade engineering. Consequently, a series of unconfined compressive strength tests were conducted to evaluate the influence of various factors, including fiber content, fiber length, curing time, and F-T cycles on the unconfined compression strength (UCS) of fiber-reinforced cemented silty sand. In parallel, acoustic emission (AE) testing was conducted to assess the AE characteristic parameters (e.g., cumulative ring count, cumulative energy, energy, amplitude, RA, and AF) of the same material under F-T cycles, elucidating the progression of F-T-induced damage. The findings indicated that UCS initially increased and then declined as fiber content increased, with the optimal fiber content identified at 0.2%. UCS increased with prolonged curing time, while increases in fiber length and F-T cycles led to a reduction in UCS, which then stabilized after 6 to 10 cycles. Stable F-T cycles resulted in a strength loss of approximately 30% in fiber-reinforced cemented silty sand. Furthermore, AE characteristic parameters strongly correlated with the stages of damage. F-T damage was segmented into three stages using cumulative ring count and cumulative energy. An increase in cumulative ring count to 0.02 × 104 times and cumulative energy to 0.03 × 104 mv·µs marked the emergence of critical failure points. A sudden shift in AE amplitude indicated a transition in the damage stage, with an amplitude of 67 dB after 6 F-T cycles serving as an early warning of impending failure.

Insight into wave propagation in polyimide films and resistive grid sandwich structures towards a hybrid monitoring of hypervelocity impact.

Micro-Meteoroid and Orbital Debris pose a significant threat to the safe operation of orbiting spacecraft, potentially leading to mission failure in space exploration. Quantitative characterization of hypervelocity impact (HVI) is crucial to ensure the safety and successful completion of on-orbit missions. Firstly, this study designed a three-layer sandwich structure of polyimide film with orthogonally laid resistive wires, combined with piezoelectric and resistive wire sensors, for the simultaneous acquisition of acoustic emission (AE) signals generated by HVI and measurement of perforation dimensions. Secondly, a semi-analytical finite element (SAFE) analysis of wave dispersion properties in the periodic sandwich structure is conducted with Bloch's theorem, together with a hybrid model based on three-dimensional smoothed particle hydrodynamics and finite element methods (SPH-FEM) to comprehensively understand the AE waves and damage characteristics induced by HVI. The resulting anisotropic wave propagation characteristics with SAFE and SPH-FEM are closely matched. Thirdly, a time delay-multiplication (TDM) imaging algorithm considering wave velocity anisotropy is proposed for accurate real-time "visualization" of HVI locations. Lastly, correlations are established between projectile and perforation dimensions. The proposed algorithm for HVI multi-parameter quantification and damage detection helps evaluate the space HVI environment and HVI-induced damage to spacecraft.

A first step towards the detection of damage processes in endodontic Ni-Ti alloy files, using acoustic emission.

Despite major instrumental developments over the last decade, endodontic files are still not infallible. It is well known that NiTi rotary files can break without any visible sign of deformation. Instrument breakage under combined flexion-torsion loading is still common in clinical practice. Unfortunately, breakage of this type of instrument mainly occurs in narrow canals, through pinching in the apical region. When such an incident occurs, the endodontist must adopt a debris retrieval strategy that is both stressful and not guaranteed success. This study proposes a new method for experimental damage detection leading to the fracture of Ni-Ti shape memory alloy endodontic files. It is based on the acoustic emission (AE) technique and mechanical parameters measured in real-time and image analysis. It has been shown that the AE results correlate with the damage observations and torque and force measurements recorded during the tests. Having carried out numerous root canal treatment on resin blocks, it appears that this new detection and analysis technique can be used to analyze and anticipate the first signs of damage leading to endodontic file failure. The technological development of such a method, at the level of the engine itself, associated with the act in service procedure, would constitute a revolution in the field of endodontics.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Intensity Analysis Method of Acoustic Emission Signals for the Damage Monitoring of Post-Tensioned Concrete Beams.

Acoustic emission (AE) is well suited for the real-time monitoring and detection of damage in reinforced concrete structures. In this study, loading/unloading cycles up to failure were applied on three different full-scale beams, each with varying defect morphologies. An intensity analysis method was employed to assess the damage sensitivities of the defective structures under stress conditions. Specifically, the calm ratio, load ratio, severity, and historical index were identified as statistical parameters that can provide global information on the damage level. Consequently, they can be easily used as damage evolution indexes for reinforced concrete structures. Correlations between these parameters were investigated to better discriminate between their potentials and identify critical levels that might not be evident using parametric analysis. The AEI chart helps locate damaging areas, aiding focused repairs. For defected beams with broken strands, at low load, HI and SI fall in zone B (damage detected). At cycles 4 and 6, with significant deflection, they fall into a critical zone, E (severe damage). Comparing post-tensioned beams revealed defects correlating with damage susceptibility. B3 beams with diffused defects displayed high activity at higher loads. Applying a load-calm ratio chart, initial minor damage worsened progressively. Severe damage was prominent in defective B2 and B3 beams, reaching zone 3. The variation in the acquired parameters over time can then be considered as an affordable and reliable indicator of damage progression.

Influence of Gamma-Phase Aluminum Oxide Nanopowder and Polyester-Glass Recyclate Filler on the Destruction Process of Composite Materials Reinforced by Glass Fiber.

Recycling of composite materials is an important global issue due to the wide use of these materials in many industries. Waste management options are being explored. Mechanical recycling is one of the methods that allows obtaining polyester-glass recyclate in powder form as a result of appropriate crushing and grinding of waste. Due to the fact that the properties of composites can be easily modified by adding various types of fillers and nanofillers, this is one of the ways to improve the properties of such complex composite materials. This article presents the strength parameters of composites with the addition of fillers in the form of polyester-glass recyclate and a nanofiller in the form of gamma-phase aluminum nanopowder. To analyze the obtained results, Kolmogorov-Sinai (K-S) metric entropy was used to determine the transition from the elastic to the viscoelastic state in materials without and with the addition of nanoaluminum, during a static tensile test. The tests included samples with the addition of fillers and nanofillers, as well as a base sample without any additives. The article presents the strength parameters obtained from a testing machine during a static tensile test. Additionally, the acoustic emission method was used during the research. Thanks to which, graphs of the effective value of the electrical signal (RMS) were prepared as a function of time, the parameters were previously identified as extremely useful for analyzing the destruction process of composite materials. The values obtained from the K-S metric entropy method and the acoustic emission method were plotted on sample stretching graphs. The influence of the nanofiller and filler on these parameters was also analyzed. The presented results showed that the aluminum nanoadditive did not increase the strength parameters of the composite with recyclate as a result of the addition of aluminum nanofiller; however, its addition influenced the operational parameters, which is reflected in a 5% increase in the UTS value (from 55% to 60%).

Research on Acid Aging and Damage Pattern Recognition of Glass Fiber-Reinforced Plastic Oil and Gas Gathering Pipelines Based on Acoustic Emission.

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues in steel pipes, the demand for glass fiber-reinforced plastic (GFRP) pipes is increasing in oil and gas gathering and transmission systems. However, the medium that is transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-, Ca2+, Mg2+, SO42-, CO32-, and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped defect GFRP pipes with depths of 2 mm and 5 mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.

Acoustic Features of the Impact of Laser Pulses on Metal-Ceramic Carbide Alloy Surface.

Technologies associated with using concentrated energy flows are increasingly used in industry due to the need to manufacture products made of hard alloys and other difficult-to-process materials. This work is devoted to expanding knowledge about the processes accompanying the impact of laser pulses on material surfaces. The features of these processes are reflected in the acoustic emission signals, the parameters of which were used as a tool for understanding the accompanying phenomena. The influence of plasma formations above the material surface on self-oscillatory phenomena and the self-regulation process that affects pulse productivity were examined. The stability of plasma formation over time, its influence on the pulse performance, and changes in the heat flux power density were considered. Experimental data show the change in the power density transmitted by laser pulses to the surface when the focal plane is shifted. Experiments on the impact of laser pulses of different powers and durations on the surface of a hard alloy showed a relationship between the amplitude of acoustic emission and the pulse performance. This work shows the data content of acoustic emission signals and the possibility of expanding the research of concentrated energy flow technologies.

A smart look at monitoring while drilling (MWD) and optimizing using acoustic emission technique (AET).

Monitoring while drilling (MWD) is a crucial task in mining operations. Accurately measuring drill and rock-related operating parameters can significantly reduce the cost of drilling operations. This study explores the potential of monitoring drilling specific energy (SE) and optimizing drilling operations by processing vibroacoustic signals generated while drilling. For this purpose, 30 samples of different rocks, are used for drilling tests. During the drilling process, the acoustic and vibration signals are recorded and analyzed in the time, frequency, and time-frequency domains., and parameters related to the resulting spectra are extracted. After obtaining the vibroacoustic parameters for drilling, the relationship between them and the drilling SE was investigated. There is evidence that the progression of SE contributes to the magnitude of rock drilling vibroacoustic features, which could be employed to indicate energy conditions during drilling. Results obtained in this study have the potential to be used as the basis for an industrial monitoring system that can detect excessive energy consumption and advise the user of the end of the bit's useful life. This method can be an intelligent technique for measuring the behavior of real-time drilling operations based on the SE simply by installing vibroacoustic sensors on the drilling machines.

Discrimination of doubled Acoustic Emission events using neural networks.

In observatory seismology, the effective automatic processing of seismograms is a time-consuming task. A contemporary approach for seismogram processing is based on the Deep Neural Network formalism, which has been successfully applied in many fields. Here, we present a 4D network, based on U-net architecture, that simultaneously processes seismograms from an entire network. We also interpret Acoustic Emission data based on a laboratory loading experiment. The obtained data was a very good testing set, similar to real seismograms. Our Neural network is designed to detect multiple events. Input data are created by augmentation from previously interpreted single events. The advantage of the approach is that the positions of (multiple) events are exactly known, thus, the efficiency of detection can be evaluated. Even if the method reaches an average efficiency of only around 30% for the onset of individual tracks, average efficiency for the detection of double events was approximately 97% for a maximum target, with a prediction difference of 20 samples. Such is the main benefit of simultaneous network signal processing.

An Experimental Study of the Acoustic Signal Characteristics of Locked-Segment Damage Evolution in a Landslide Model.

Three-section landslides are renowned for their immense size, concealed development process, and devastating impact. This study conducted physical model tests to simulate one special geological structure called a three-section-within landslide. The failure process and precursory characteristics of the tested samples were meticulously analyzed using video imagery, micro-seismic (MS) signals, and acoustic emission (AE) signals, with a focus on event activity, intensity, and frequency. A novel classification method based on AE waveform characteristics was proposed, categorizing AE signals into burst signals and continuous signals. The findings reveal distinct differences in the evolution of these signals. Burst signals appeared exclusively during the crack propagation and failure stages. During these stages, the cumulative AE hits of burst signals increased gradually, with amplitude rising and then declining. High-amplitude burst signals were predominantly distributed in the middle- and high-frequency bands. In contrast, cumulative AE hits of continuous signals escalated rapidly, with amplitude monotonously increasing, and high-amplitude continuous signals were primarily distributed in the low-frequency band. The emergence of burst signals and high-frequency AE signals indicated the generation of microcracks, serving as early-warning indicators. Notably, the early-warning points of AE signals were detected earlier than those of video imagery and MS signals. Furthermore, the early-warning point of burst signals occurred earlier than those of continuous signals, and the early-warning point of the classification method preceded that of overall AE signals.

Research on a Method for Classifying Bolt Corrosion Based on an Acoustic Emission Sensor System.

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions.