Acoustic Emission Signal Processing Research Articles

Study on subsurface damage behavior in ductile ultra-precision grinding of sapphire based on acoustic emission signal processing

Chemical corrosion, photothermal absorption and acoustic emission were used to multidimensionally evaluate and monitor subsurface damage behavior in ductile ultra-precision grinding of sapphire. The optimum ductile grinding surface can be obtained at different grinding depths when grinding with constant-depth. The density of corrosion dislocation craters and the value of photothermal absorption increased with grinding depth, and the subsurface damage scales under the ductile grinding surface were distinct. Subsurface damage was dominated by cracks and spalling. Severe brittle fractures occurred at greater grinding depths in varied-depth grinding. The corroded grinding surface consisted of the original polishing surface, the ductile grinding surface, and the brittle fracture characteristic. The amplitude and fluctuation of grinding force increased with grinding depth. The original signal and Fourier transform cannot monitor the damage behavior. A signal processing model based on wavelet transform, signal reconstruction and fast Fourier transform was developed. The processed signal characteristics can correspond to the damage behavior under the ductile grinding surface.

Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure

Acoustic emission (AE) signal processing and interpretation are essential in mining engineering to acquire source information about AE events. However, AE signals obtained from coal mine monitoring systems often contain nonlinear noise, limiting the effectiveness of conventional analysis methods. To address this issue, a novel denoising approach using enhanced variational mode decomposition (VMD) and fuzzy entropy is proposed in this study. The denoised AE signal’s spectral multifractal features are analyzed. The optimization algorithm based on VMD with a weighted frequency index is introduced to avoid mode mixing and outperform other decomposition methods. The characteristic parameter Δα of the AE spectral multifractal parameter serves as an early warning indicator of coal instability. These findings contribute to the accurate extraction of time–frequency features and provide insights for on-site AE signal processing.

Microcrack monitoring and fracture evolution of polyolefin and steel fibre concrete beams using integrated acoustic emission and digital image correlation techniques

The use of polymer and steel fibres in plain concrete appears to be an excellent solution for limiting crack propagation and improving the post-ductility performance of concrete structures. Based on this premise, this study investigated the fracture evolution of polyolefin fibre-reinforced concrete (PFRC) and steel fibre-reinforced concrete (SFRC) specimens through the integrated application of two diagnostic techniques, acoustic emission (AE) and digital image correlation (DIC), under three-point bending tests. Based on the processing of AE signals, different AE statistical parameters such as the cumulative number of hits, amplitude distribution, and some representative analysis methods including the b-value method, Ib-value method, and AE intensity analysis methods were selected to analyse the early detection of cracking and post-cracking behaviour in PFRC vs SFRC specimens during mechanical degradation. Simultaneously, the DIC technique was used to validate the fracture evolution of the AE results. Furthermore, to verify the reliability of the AE and DIC results, the damage localisation and fracture evolution of the PFRC versus SFRC specimens were confirmed by integrating the AE fracture energies and DIC outcomes. The tests and analysed results showed that the addition of steel fibres to plain concrete significantly improved the ability to restrict crack propagation and provided higher post-cracking resistance compared to PFRC specimens owing to their stronger fibre–matrix bonding, effective fibre bridging, and crack-arresting mechanism. The present study indicates that the combined AE and DIC techniques are highly effective for the early detection of damage and ductility performance in fibre-reinforced concrete structures.

A Novel Approach Applied to Transient Short-Circuit Diagnosis in TIMs by Piezoelectric Sensors, PCA, and Wavelet Transform

Non-invasive fault diagnosis of three-phase induction motors (TIM) is widely used in industrial applications to ensure the integrity of processes. Among different types of TIM failures, transient inter-turn short circuits (ITSCs) are incipient stator winding faults characterized as short circuits between two or more turns of the coils, that can lead the winding to progressive deterioration, and consequently, the TIM to total failure. In this context, this article proposes a novel approach by using piezoelectric transducers, which performs the transient ITSC detection, phase identification, and magnitude classification by using the Acoustic Emission (AE) technique. To accomplish this analysis, a new statistical index based on the cross-correlation function was proposed to detect the ITSC and classify its magnitude. Besides, Wavelet Transform and Principal Component Analysis (PCA) stood out as promising tools to identify which phase was affected by the short circuits. A TIM was subjected to ITSCs and the experimental results showed that the proposed algorithm successfully performed the transient ITSC detection, phase identification, and evolution classification. Additionally, this work improve the capabilities of traditional AE systems, since no AE signal processing algorithm has ever been proposed for a comprehensive diagnosis of transient ITSC.

When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)

Acoustic Emission (AE) is an effective method to monitor and control the quality in different technical processes and phenomena, including tribology and fracture mechanics. However, in highly dynamic processes such as Laser Additive Manufacturing (LAM) of metal, the processing of AE signals is very burdensome. At the same time, artificial intelligence (AI) has been considered as a new and powerful tool to overcome the complexity of the large data processing with a reasonable computational time. In this contribution, we have summarized our prior works. To start with, we demonstrated that the combination of AE with state-of-the-art signal processing including AI makes it possible to differentiate several process conditions, workpiece quality and materials. Then, we present some limitations when changing process parameters and materials. Finally, we also introduce alternative AI methods to reduce the amount of data needed to train the AI algorithms as well as transfer the knowledge from one material to another one. This will give the reader an overview of the advances of monitoring the LAM process combining AE with AI techniques to make a significant step forward in in situ and real-time process monitoring and quality control. In particular,

Prospects on application of elastic metamaterials in acoustic emission sensing

This paper discusses challenges, limitations and problems arising in currently employed acoustic emission testing systems, sensors and signal processing algorithms. Main factors influencing and compromising accuracy and reliability of the classically used methods are briefly analyzed. Next, metamaterials and their dynamic properties are outlined with the aim and focus on their potential in manipulating energy flow. Several applications of metamaterials in acoustic emission are presented that indicate their potential in acoustic emission. Finally, possibilities of designing a new type of sensor for acoustic emission using metamaterials is discussed. As a consequence, it is expected to increase sensitivity and accuracy of source localization with smaller number of sensors used during measurements.

Investigation of the effect of nanoparticles on wood-bioplastic composite behavior using the acoustic emission method

Acoustic emission (AE) is a non-destructive method capable of detecting various fracture micromechanisms in composite materials. Discrimination of AE signals associated with various fracture modes is of interest when utilizing this technique. Thus, this paper investigates the identification of AE signal processing wavelet packet transform (WPT) failure mechanisms in wood-bioplastic nanocomposites. To this end, nanocomposites with different compositions were prepared and tested according to the ASTM D638 standard. AE signals were collected during the experiment, and analyzed by various methods. The fast Fourier transform method was used to analyze the fracture of polymers and wood and determine the frequency range of fracture. Additionally, a time-frequency analysis of the AE signal generated by the WPT was performed. The energy distribution criterion was employed to determine the main components of the various damage micromechanisms. The results reveal distinct types and percentages of failure micromechanisms in various composites. This method examined the effect of nanoparticles on the matrix’s tensile strength. The scanning electron microscopy method was used to study the morphology of composites and mechanism of failure. The phase composition of the produced materials was examined with X-ray energy dispersive spectroscopy. The results demonstrated the proposed techniques show a good performance in computational damage discrimination.

Comparison of MLP and RBF neural networks for bearing remaining useful life prediction based on acoustic emission

In this research, the efficiency of multilayer perceptron (MLP) and radial basis function (RBF) neural networks in estimating the remaining useful life (RUL) of angular contact ball bearing based on acoustic emission signals are investigated. To capture the bearing acoustic emission signals, an appropriate laboratory setup is used. Acoustic emission signal processing is carried out in the time and frequency domain and 102 different features are extracted. Prognostic feature selection have been used to reduce the dimension of the extracted features. Applications of the different training algorithms in MLP neural network are compared for bearing RUL prediction. The results indicate that acoustic emission is a good method for bearing RUL prediction. Mobility, Square-mean-root, and Count are the best time domain features based on the used feature selection method. Also, the Frequency center, Signal power, and F60 are the best frequency domain features. It was shown that between different backpropagation training algorithms for MLP neural net, Levenberg Marquardt has the lowest SSE error of 7.86 for the prediction of bearing remaining useful life based on frequency domain features. Moreover, comparison of RBF and MLP neural networks shows that RBF neural networks presents the best performance with SSE error of 2.85.

Acoustic emission signal processing for the assessment of corrosion behaviour in additively manufactured AlSi10Mg

Acoustic Emission (AE) technique is used for characterizing the corrosion behaviour of the AlSi10Mg specimens prepared from recycled feed-material using the Selective Laser Melting (SLM) Process. The corrosion behaviour is studied by exposing the specimens to a simulated salt attack for a period of 240 h. AE signals are recorded for the entire duration and are analysed in their time-frequency domain. Initially, noises are observed in the recorded AE signals. A methodology based on waveform entropy is developed to denoise the signals. The characteristics of the noise are studied to retain any useful information. The results show that some of the low amplitude signals could be misidentified as noise signals; it is rectified by studying the features of the noise such as peak frequency, frequency centroid and peak amplitude. The time-frequency characteristics of the AE signals are then studied along with the morphological features of the specimens exposed to different stages of corrosion. This provides a correlation between the corrosion behaviour such as pit formation, corrosion product formation and the corrosion crack formation and the time-frequency characteristics of AE signals. This information can identify the corrosion behaviour of the AlSi10Mg specimens intuitively.

Analysis of the optical quartz lens centering process based on acoustic emission signal processing and the support vector machine

A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission–sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of < 2 μm, a lens circularity error of < 0.01 mm, a crack length of < E0.1, and an optical axis error of < 150 μrad.

Effect of Preliminary Irradiation of 321 Steel Substrates with High-Intense Pulsed Ion Beams on Scratch Test Results of Subsequently Deposited AlN Coatings

The paper presents the effect of irradiation of 321 steel substrates with a high-intense pulsed ion beam (HIPIB) on changes in functional properties of the surface layers and tribological characteristics of AlN coatings subsequently deposited above by the reactive magnetron sputtering method. The morphology of the modified surface layers, their microhardness and free surface energy levels are presented for different HIPIB energy densities. HIPIB irradiation of the substrates caused variations in the results of scratch tests combined with the acoustic emission signal processing. Their analysis has enabled concluding that the crack initiation threshold could be at least doubled for the studied coating/substrate system due to preliminary HIPIB irradiation. Finally, the obtained data were discussed, and future research directions were proposed.

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals.

There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.

Cluster-based acoustic emission signal processing and loading rate effects study of nanoindentation on thin film stack structures

This paper presents a high-resolution, in-situ material testing system that integrates acoustic emission (AE) testing with a nanoindentation system for crack generation and detection in thin film stack structures. This is used to find the critical contact load during wafer probing of crack-sensitive backend-of-line (BEOL) structures in semiconductor integrated circuits. Scanning electron microscopy (SEM) and load–displacement curve analysis were used to confirm the formation and propagation of cracks in the multilayer structures. In order to improve the manual classification performance and understand the physical meaning of AE signals, this paper introduces a machine learning based signal processing approach based on a k-means clustering algorithm applied on collected AE signals. To obtain the optimal number of k-means clusters, Davies–Bouldin, Dunn, and Silhouette indices were calculated, and the individual ratings were cumulated based on a voting scheme. Multiple feature extraction methods, including raw time-domain AE signals, conventional AE extracted parameters, short-term signal energy, and representation features learned by the autoencoder, were used and evaluated by manually labeled clusters and binary confusion matrices. A supervised learning technique, the k-nearest neighbors algorithm, was also utilized on different AE signal datasets using different loading rates to further investigate the damage processes during nanoindentation and the physical meaning of different AE signals. The influences of loading rates on AE signals have been investigated, and loading rate effects on the critical load were observed – higher loading rates led to higher critical loads. This integrated test system and signal processing approach provides a high-resolution mechanical testing platform for studying and enabling automatic crack detection in wafer probing.

Dynamic feature evaluation on streaming acoustic emission data for adhesively bonded joints for composite wind turbine blade

Damage mode identification and premature failure prevention for composite structures by acoustic emission have drawn a great deal of attention. Feature evaluation on streaming acoustic emission data is one of the significant issues in research of acoustic emission signal processing. This work conducts dynamic feature evaluation on 15 conventional acoustic emission features so as to seek a deeper insight into different features with damage accumulation. First, the procedure of dynamic feature evaluation is presented based on three basic algorithms. Second, the streaming acoustic emission data are collected from the adhesively bonded composite single-lap joint subjected to quasi-static tensile loads. Third, further efforts are made so as to explore the information contained as well as to interpret the effect of damage accumulation. It is found that different conventional acoustic emission features show distinctive functions, including damage mode identification, damage process indication, and both of them. Informative features for damage pattern recognition are independent on damage accumulation. Useful features for damage process description show sensitive dynamic characteristics with damage accumulation, especially before the complete failure of the specimen. Furthermore, dynamic feature evaluation can be used to detect singular signals.

Experimental Design of Acoustic Emission Detection for Micro Leakage in Marine Compressed Gas System

In order to detect the micro leakage of marine compressed gas pipeline system, an experimental platform for detecting the micro leakage of compressed gas is designed by using acoustic emission technology. The results show that the experimental platform can effectively simulate the two typical marine compressed gas pipeline leakage problems: pipe wall damage and valve internal leakage. At the same time, these leakage problems can be effectively detected by using acoustic emission signal acquisition and processing system.

Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing

In this paper, the estimation of the remaining useful life (RUL) of angular contact ball bearing using time-domain signal processing method is discussed. An experimental setup based on acoustic emission (AE) signal is used to extract and collect the desired data. The residual life test is performed on the SKF 7202 BEP angular contact ball bearing. Sixty-time domain features have been introduced and used for fault detection. Improved Distance Evaluation (IDE) method has been used for feature dimensionality reduction and the best 10 features have been selected. K-Nearest Neighbors (KNN) algorithm has been used to investigate the classification accuracy of IDE based on selected features for classifying healthy and faulty bearings. The results show that the IDE method enables natural fault detection in bearings with high precision. To validate the performance of the KNN classifier, performance indices such as accuracy, precision, and specificity are applied. The results show that kurtosis, FM4, k factor, energy, and peak are the best features and kurtosis has the highest KNN rank with accuracy, precision, and specificity of 97%, 93%, and 94%, respectively.

An Experimental Analysis for Damage Monitoring in Glass Fiber/Epoxy Composites During Fatigue Tests by Acoustic Emission

As a non-destructive technique, Acoustic emission (AE) can be employed to detect the damage inside the material passively and then locate the damage source. However, fatigue loading poses challenges to AE signal acquisition and processing. In this paper, AE monitoring is performed on glass fiber/epoxy composite laminates under fatigue loads. Due to the intrinsic noise, wavelet packet decomposition is used for noise elimination. Results show that the noise components in original AE signals can be effectively eliminated by the wavelet analysis. Based on the difference of the arrival times, the line positioning method is shown to locate AE sources appearing in the laminates successfully. The peak frequency characteristic of each AE signal is utilized for damage mode classification. The fracture of the laminate is governed by delamination and fiber/breakage, followed by fiber/matrix interface debonding.

Seismic Vulnerability Analysis of a Coupled Tank-Piping System by Means of Hybrid Simulation and Acoustic Emission

In order to shed light on the seismic response of complex industrial plants, advanced finite element models should take into account both multicomponents and relevant coupling effects. These models are usually computationally expensive and rely on significant computational resources. Moreover, the relationships between seismic action, system response and relevant damage levels are often characterized by a high level of nonlinearity, which requires a solid background of experimental data. Vulnerability and reliability analyses both depend on the adoption of a significant number of seismic waveforms that are generally not available when seismic risk evaluation is strictly site-specific. In addition, detection of most vulnerable components, i.e., pipe bends and welding points, is an important step to prevent leakage events. In order to handle these issues, a methodology based on a stochastic seismic ground motion model, hybrid simulation and acoustic emission is presented in this paper. The seismic model is able to generate synthetic ground motions coherent with site-specific analysis. In greater detail, the system is composed of a steel slender tank, i.e., the numerical substructure, and a piping network connected through a bolted flange joint, i.e., the physical substructure. Moreover, to monitor the seismic performance of the pipeline and harness the use of sensor technology, acoustic emission sensors are placed through the pipeline. Thus, real-time acoustic emission signals of the system under study are acquired using acoustic emission sensors. Moreover, in addition to seismic events, also a severe monotonic loading is exerted on the physical substructure. As a result, deformation levels of each critical component were investigated; and the processing of acoustic emission signals provided a more in-depth view of the damage of the analysed components.

A Review on Fault Diagnosis and Condition Monitoring of Gearboxes by Using AE Technique

Gearboxes have a significant role in the operation of rotating machinery. Several techniques exist for the diagnosis of fault in gearboxes. This work presents the techniques for the condition monitoring and fault diagnosis of the gearboxes based on Acoustic Emission (AE). The review describes the application of AE to detect the various types of fault in the gearboxes for different types of gear, the AE parameters used for the gearbox fault diagnosis, AE signal processing techniques, feature extraction techniques for the fault identification of gearbox, modelling of AE generated in gearbox and fault location detection using AE technique. After detailed description of the various techniques, some potential research issues are also discussed which is associated with condition monitoring and fault diagnosis of gearboxes using AE.

Smart machining: Monitoring of CFRP milling using AE and IR

In aerospace industry, polymers reinforced with carbon fibre offer several advantages, such as weight/strength ratio and corrosion resistance. However, the manufacturing process is complex, especially the machining of composite materials, due to their specific properties and characteristics. The detection and prediction of surface finishing and occurrence of defects during manufacturing using non-destructive techniques is industrially useful, in terms of fomenting automated manufacturing systems to increase productivity and quality control. Acoustic emission signal processing is being successful in mechanical tests for determining material defects, more recently used as a method for manufacturing evaluation of machined parts. This paper focused in the machining process of CFRP plate, in terms of surface finishing evaluation with comprehension of process phenomenology using analyses of temperature and acoustic emission signal features. The aim of this paper is to show the possibility of online monitoring of the cutting process and to understand mechanism behind variables of control, with comparisons of different milling parameters and focusing in their impacts in acoustic emission signals and surface finishing, with suggestions of evaluation and prediction methods of machined surface quality using mainly acoustic emission signals.