Raw Acoustic Emission Signals Research Articles

Thermal damage diagnosis in micro grinding processes based on raw acoustic emission signals and convolution neural networks

A micro-grinding process is usually the last machining operation among the various processes employed in the manufacturing of metallic components. It is generally the first option when the manufactured products require a combination of low surface roughness values and narrow dimensional tolerances. However, one of the main challenges of a micro-grinding process is avoiding high specific energy and the corresponding intense heat generation, as this can damage the workpiece. The energy consumed during micro-grinding is almost entirely converted into heat, making the component susceptible to thermal damage. As the occurrence of this damage compromises the service life of manufactured components, the implementation of monitoring systems is essential to guarantee the quality of the manufactured products improving process efficiency. In this study, a new approach based on convolution neural networks (CNNs) is proposed to predict the occurrence of thermal damage in the micro-grinding process. This approach uses raw acoustic emission (AE) signals as inputs into CNN, which is a more technically feasible approach to performing real-time monitoring. To obtain the AE data, micro-grinding experiments were conducted on N2711 grade steel (which is susceptible to thermal damage) under different cutting conditions (ae = 5–50 µm). The obtained AE signals were labeled based on the offline analyses performed on the workpieces, which identified the occurrence of thermal damage through visual inspections, microhardness, and microstructural analysis. For the conditions employed in this work, thermal damage was obtained after grinding with depth of cut values > 10 µm. Total 3960 AE signals were obtained of which 2200 of them were associated with thermal damage. The results depict that the proposed CNN model was able to successfully classify normal and thermal damage AE signals reaching an accuracy of 98.6% over the dataset (0.4% false positives and 1.0% false negatives).

In situ monitoring of high-temperature creep damage in CrMoV high-strength structural steel using acoustic emission

The accurate detection and evaluation of creep damage in structural steel is essential for ensuring the safety and reliability of high-temperature structures. In this work, the in situ monitoring and identification of high-temperature creep damage in CrMoV high-strength steel under different stress levels was conducted using the acoustic emission (AE) technique. A denoising procedure was proposed and applied to raw AE signals to reduce the noise unrelated to the growth of creep damage. To characterize the creep damage both qualitatively and quantitatively, multiple AE features were extracted from the creep-related signals, including peak amplitude, count, and information entropy. The results showed that a sudden increase in multiple cumulative parameters accompanied by high-entropy and high-count AE signals can accurately identify the transition from the secondary to the tertiary stage and can offer an early warning of the final rupture. In the log-log scale, the quantitative relationship between the AE rate and the minimum creep rate was found to be linear. Additionally, the substantial plastic deformation, and the nucleation, growth and coalescence of creep cavities were the dominating source mechanisms contributing to the generation of numerous high-count and high-entropy AE signals in the tertiary creep stage. Findings from this work will offer an approach for in situ monitoring and evaluation of the state of creep damage of high-temperature structures based on AE.

Modal acoustic emission-based circumferential crack feature extractions for pipeline welds with L-shaped flexible sensor array

ABSTRACT The collection and analysis of acoustic emission (AE) from weld circumferential cracks are crucial for ensuring pipeline safety. However, the unclear propagation characteristics and mode features of AE present challenges in array design and mode identification. In this manuscript, a novel mode feature extraction method for circumferential crack AE signals is proposed. This method involves three primary steps: firstly, a L-shaped flexible sensor array is designed to capture raw AE signals from both axial and circumferential directions. Next, the collected signals are filtered and evaluated to extract effective multi-mode components through the Kalman filtering and recursive plot (RP). Finally, the time–frequency features and mode types are extracted through combining the Hilbert-Huang transform (HHT) and dispersion curves. Results indicate that during the crack growths, both axial and circumferential direction AE signals contain multi-mode components, specifically L(0,1), F(1,1) and F(1,2) modes, with durations spanning 30–500 μs. Additionally, the axial modes predominantly occur within 200–300 kHz range, whereas the circumferential modes span both low and high frequency bands, specifically 40–50 kHz and 200–300 kHz, respectively. Modes across different frequencies indicate distinct structural behaviours, including crack growths and plastic deformations. The proposed method provides attempts for the pipe weld AE monitoring and multi-mode analysis.

A Reliable Pipeline Leak Detection Method Using Acoustic Emission with Time Difference of Arrival and Kolmogorov–Smirnov Test

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.

Filament extrusion state monitoring in fused filament fabrication using Bhattacharyya distance and K-nearest neighbor

Fused filament fabrication (FFF) is one of the most popular techniques of additive manufacturing. However, product quality issues still limit the further application of FFF technology. Filament extrusion state has a great influence on the quality of FFF fabricated products, since both under-extrusion and over-extrusion can lead to the deterioration of product quality. Therefore, monitoring the filament extrusion states is vital and essential. This paper aims to monitor the filament extrusion state by acoustic emission (AE). To achieve this goal, experiments are conducted on a desktop FFF machine, where the states of under-extrusion and over-extrusion are induced by different extrusion speeds. Original AE signals are collected during the experiments. Confronted with the challenge posed by the susceptibility of AE signals to noise during the complex extrusion process and different conditions, one calculates the statistical distribution of the features defined on the raw AE signals, without the need for noise reduction steps. The k-nearest neighbor algorithm is then adopted to identify the different extrusion states, where the Bhattacharyya distance is employed to measure the distances or similarities of the calculated distributions. The findings demonstrate the successful identification of various extrusion states induced by different extrusion speeds through the presented method. The outcomes of this study pave the way for the development of an affordable in-situ FFF monitoring system with comprehensive capabilities.

A CNN-based transfer learning method for leakage detection of pipeline under multiple working conditions with AE signals

Pipeline leakage detection is a crucial part of pipeline integrity management. Acoustic emission (AE) based leakage detection is widely used in this field. The latest detection methods are combined AE with convolutional neural networks. However, these methods are often confined to the complex signal processing and computing power and only target specific working conditions. To address these issues, this study proposes a convolutional neural network-based transfer learning (CNN-TL) method for pipeline leakage detection under multiple working conditions. Seven AE datasets are collected from pipeline leakage experiments under different work environments, transporting medium, and fluid pressure. The proposed method converted raw AE signals into three-channel images by a novel conversion method, which avoids reliance on expert knowledge and complex signal processing. CNN-TL is investigated by two different approaches, feature-based CNN-TL and parameter-based CNN-TL. The following nine pre-trained CNN models are used to select the optimal CNN-TL model: Alexnet, Squeezenet, Vgg19, Googlenet, Inceptionv3, Mobilenetv2, Xception, Resnet101, and Densenet201. Results show that the proposed feature-based CNN-TL method significantly outperformed parameter-based CNN-TL and traditional CNN methods, especially on two-phase flow datasets. The highest accuracy of seven AE datasets obtained by the feature-based CNN-TL methods are 100.00%, 100.00%, 100.00%, 99.33%, 85.67%, 87.67%, 74.33%, 83.33% respectively. Moreover, the computation time of proposed method is 16.78 s on average by using the best layers in feature-based CNN-TL. It can be concluded that the proposed method does not rely on signal processing, requires less computational power, and can accomplish accurate detection of pipeline leaks under multiple working conditions.

Acoustic Emission Intelligent Identification for Initial Damage of the Engine based on Single Sensor

Detecting the initial damage of the engine precisely is conducive to finding out the failure symptom timely and ensuring the reliable operation of the engine for avoiding malignant accidents. This article proposes a single-sensor acoustic emission (AE) diagnosis method based on deep learning (DL) for structural health monitoring (SHM) and initial damage intelligent identification of the engine. Firstly, the time-frequency domain features of AE signals collected from the engine test bench under different initial damages are extracted by continuous wavelet transform (CWT), and two-dimensional time-frequency images are constructed using the obtained results. After that, the proposed deep convolutional neural networks (DCNN) is employed to directly extract damage features from the converted two-dimensional time-frequency images via a multi-layer fusion and perform initial damage intelligent identification. In addition, the white Gaussian noise with different signal-to-noise ratio (SNR) is added to the raw AE signal, and the robustness of the proposed DCNN method is verified. Finally, to evaluate the performance of the proposed DCNN, the constructed test accuracy Euclidean distance and other indexes including Kappa, microF1 and macroF1 are introduced to compare with other commonly machine learning methods. The results demonstrated the effectiveness of the proposed DCNN model, which lays the foundation for the application of the single-sensor AE diagnosis method based on DCNN in SHM and initial damage intelligent identification of the engine.

A novel acoustic emission source location method for crack monitoring of orthotropic steel plates

As the fatigue problem of orthotropic steel plates becomes increasingly severe in medium- and long-span bridges, structural health monitoring of the plates that could identify cracks timely is in great demand. Considering that orthotropic steel plates are of multiple structural elements and complex geometries, it is challenging to locate the cracks accurately. To this end, a novel acoustic emission (AE) source location method based on empirical wavelet transform and long short-term memory neural network was developed for crack monitoring of orthotropic steel plates. Empirical wavelet transform adaptively decomposed prominent components contained in the raw AE signal, of which the one with distinct dispersive modes was selected as the characteristic component to more precisely determine the arrival time through Akiake information criteria. The long short-term memory model established a more exact relationship between the arrival time differences of AE signals at multiple sensors and the source coordinates. With the help of empirical wavelet transform and long short-term memory model, it significantly eliminated the influence of dispersion, reflection, scattering, multiple propagation paths and noise, and thus guaranteed higher crack location accuracy. Through field experiment on a long-span suspension bridge, where cracks were simulated by artificial AE sources, a comprehensive comparison was carried out among traditional time-of-arrival method, delta-T mapping method, improved delta-T mapping method based on Akiake information criteria and Gaussian process, a counterpart of the proposed method based on empirical wavelet transform and Gaussian process, as well as the proposed method based on empirical wavelet transform and long short-term memory neural network. The location errors and their variations of the proposed method were found far less than those of the other existing methods. The location accuracy could be further improved by increasing the grid resolution of pencil lead break pre-tests. The results demonstrated the feasibility and superiority of the proposed crack location method for field applications on large-scale complex structures like orthotropic steel plates.

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.

Continuous Leak Location in Gas-Filled Steel Pipe Based on Optimum Wavelet Analysis of Leak AE Signal

Abstract This study is investigates how to reconstruct the continuous leak acoustic emission (AE) signal to precisely locate the leak source on a gas-filled steel pipe based on cross-correlation location theory. Thus, in this article, the determination method for the optimum mother wavelet was proposed. The frequency distribution of the continuous leak AE event was firstly extracted through wavelet transform (WT) with optimum mother wavelet. In the considered frequency range, the WT coefficients were further used to reconstruct the raw continuous leak AE signal. The correlation-based location experiments were finally carried out to evaluate the performance of the proposed method. The results show that the location errors of the leak source were decreased by some 10 % compared with those achieved by only using the raw AE signals. Thus, improvement verifies the validity of choice of the optimum mother wavelet. Meanwhile, it is also founded that, whether the reconstructed signals or raw signals were used, the localization accuracy of the leak source using the speed of the T wave is greater than that using the speed of the L wave and theoretical acoustic speed under the same symmetry of the AE sensors with reference to the leak source. Therefore, defining the acoustic speed with the speed of the T wave was recommended to use on-site for detection of the continuous leak source on a gas-filled steel pipe.

Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions

This article uses acoustic emission (AE) analysis to diagnose an industrial-scale and slow-speed wind turbine blade bearing. The main challenge for AE analysis is that the fault signals are mingled with heavy noise. As a result, the objective of this article is to filter the raw AE signals and extract weak fault signals. To achieve this goal, a general linear and nonlinear auto-regressive (GLNAR) model is first developed to exploit the nonlinear characteristics of the AE signals. Then, the sparse augmented Lagrangian (SAL) algorithm is applied to learn the built GLNAR model and filter the raw AE signals. The characteristics of SAL is that it is a novel sparse representation technique which can convert the original filtering problem into a number of suboptimization problems, and these subproblems can be solved separately. Finally, as the blade bearing rotates at fluctuating speeds, the filtered signals are resampled so that the bearing fault type can be diagnosed in the order domain. The proposed diagnostic framework was validated in several experiments under time-varying low-speed and heavy-blade-load conditions. The results indicate that our proposed methods are effective and accurate.

Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis

Wind turbine blade bearings are often operated in harsh circumstances, which may easily be damaged causing the turbine to lose control and to further result in the reduction of energy production. However, for condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings, one of the main difficulties is that the rotation speeds of blade bearings are very slow (less than 5 r/min). Over the past few years, acoustic emission (AE) analysis has been used to carry out bearing CMFD. This article presents the results that reflect the potential of the AE analysis for diagnosing a slow-speed wind turbine blade bearing. To undertake this experiment, a 15-year-old naturally damaged industrial and slow-speed blade bearing is used for this study. However, due to very slow rotation speed conditions, the fault signals are very weak and masked by heavy noise disturbances. To denoise the raw AE signals, we propose a novel cepstrum editing method, discrete/random separation-based cepstrum editing liftering (DRS-CEL), to extract weak fault features from raw AE signals, where DRS is used to edit the cepstrum. Thereafter, the morphological envelope analysis is employed to further filter the residual noise leaked from DRS-CEL and demodulate the denoised signal, so the specific bearing fault type can be inferred in the frequency domain. The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies, which offers a promising solution for wind-farm applications.

Research on Acoustic Emission Source Location Imaging in Aluminum Alloy Plate-type Structure

Abstract The time reversal focusing and imaging technique is often used to locate the acoustic emission (AE) source on a plate-type structure. Because of the computational error caused by the dispersive modes of the raw AE signal, the AE source is sometimes located in the adjacent region of its real position and the pseudo-AE source will be focused in the location image of the AE source. This study is interested in how to effectively avoid the focusing of the pseudo-AE source. Thus, in this article, a Shannon wavelet transform with “Morlet” mother wavelet was used to isolate S0 modes from raw AE signals. The modulus value curves of the isolated S0 modes were treated by time reversal, time delay, and superposition at every grid point in the monitoring area, based on the time reversal focusing principle. The AE source was located at a certain grid point where the peak of the superposition curve is maximal. The monitoring area imaging was realized by defining every grid point’s pixel value with the peak of the superposition curves. The validating experiment was done in a 600 mm by 600 mm monitoring area which is on a 5052 aluminum alloy plate with dimensions of 1,200 mm by 1,200 mm by 3 mm. Comparing with the traditional square location method based on time difference of arrival (TDOA) calculation, the localization accuracy was improved and the maximal radial error is lower than 30 mm. Furthermore, the proposed localization method has a certain ability to overcome the effect of the plate size and local curvature.

Learning the representation of raw acoustic emission signals by direct generative modelling and its use in chronology-based clusters identification

Acoustic emission (AE) is a passive monitoring technique used for learning about the behaviour of an engineered system. The streaming obtained by continuously recording AE transient signals is treated by a four steps procedure: 1) The detection of salient AE signals by distinguishing noise against non-noise signals using wavelet denoising, 2) the statistical representation of randomly selected AE signals using Autoregressive Weakly Hidden Markov Models, 3) an inference phase by applying those models to unknown AE signals and generating a set of novelty scores reflecting differences between signals, 4) the clustering of novelty scores using constraint-based consensus clustering. Compared to the standard way relying on the transformation of all AE signals by manual feature engineering (MFE) before clustering, the main breaktrough proposed in this paper holds in the use of the raw AE signals, with different lengths and various scales, to build high level information and organise the low level streaming data. Validated first on simulated data, we show the potential of this methodology for interpreting acoustic emission streaming originating from composite materials.

Deep Learning Object-Impulse Detection for Enhancing Leakage Detection of a Boiler Tube Using Acoustic Emission Signal

Advances in technology have enhanced the ability to detect leakages in boiler tube components in thermal power plants. As a specific issue, the interaction between the coal fuel stream and the boiler tube membrane generates random and high-amplitude impulses, which negatively affect the measured acoustic emission (AE) signal from leakages. It is essential to detect and practically handle these kinds of impulses. Based on the object detection concept, this paper proposes an impulse detection methodology that employs deep learning flexible boundary regression (DLFBR). First, the shape extraction (SE) preprocessing technique is implemented to yield the shape signal, which contains intrinsic information about the impulse from the raw AE signal. Then, DLFBR extracts and generates both the feature map and the confidence mask from the shape signal to regress a boundary box, which specifies the position of the impulse. For illustration purposes, the proposed algorithm is applied to an experimental leakage detection dataset recorded from a subcritical boiler unit with a tube membrane. Experimental results show that the proposed method is effective for detecting impulses of leakage in a boiler tube testbed, providing 99.8% average classification accuracy.

Diagnostyka pittingu kół zębatych na podstawie surowego sygnału emisji akustycznej w oparciu o głębokie uczenie maszynowe

Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults.

Mode I Interlaminar Fracture of Glass/Epoxy Unidirectional Laminates. Part I: Experimental Studies.

The paper presents experimental tests of unidirectional double cantilever beams made of a glass fiber reinforced (GFRP) laminate. The critical value of the strain energy release rate (c-SERR or GIC), i.e., the mode I fracture toughness of the considered material was determined with three different methods: the compliance calibration method (CC), the modified compliance calibration method (MCC), and the corrected beam theory (CBT). Due to the common difficulties in precise definition of delamination initiation force, the Acoustic Emission (AE) technique was applied as an auxiliary source of data. The failure process was monitored, as well, in order to detect and identify different damage phenomena. This was achieved through a detailed analysis of the raw AE signal subjected to fast Fourier transformation (FFT). The frequency spectra revealed three dominating frequency bands with the basic one described by the average value of 63.1 kHz, revealing intensive delamination processes. This way, not only precise values of the critical SERR, but also the information on damage evolution during propagation of delamination, was obtained.

Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks

Determining the optimal features that are invariant under changes in the rotational speed variations of rolling element bearings is a challenging task. To address this issue, this paper proposes an acoustic emission (AE) analysis-based bearing fault diagnosis invariant under fluctuations of rotational speeds using envelope spectrums (ES) and a convolutional neural network (CNN). The ES extracted from the raw AE signals provides valuable information about the characteristic defect frequency peaks and variations to bearing rotational speeds when faults appear on a bearing. The proposed method employs CNN to automatically extract high quality features and classify bearing defects. In the experiment, a CNN trained on a dataset corresponding to one revolutions per minute (RPM) is used to detect patterns from datasets corresponding to other RPMs to verify that the classification is accurate and invariant under rotation speed fluctuations. The efficacy of the proposed method is verified on AE-based low-speed bearing data under various rotational speeds. The experimental results show that the proposed method is effective at detecting bearing failures, provides an average classification accuracy of about 86% under fluctuating RPM, and outperforms other state-of-the-art algorithms.

A method for reduction of Acoustic Emission (AE) data with application in machine failure detection and diagnosis

Abstract The use of AE in machine failure diagnosis has increased over the last years. Most AE-based failure diagnosis strategies use digital signal processing and thus require the sampling of AE signals. High sampling rates are required for this purpose (e.g. 2 MHz or higher), leading to streams of large amounts of data. This situation is aggravated if fine resolution and/or multiple sensors are required. These facts combine to produce bulky data, typically in the range of GBytes, for which sufficient storage space and efficient signal processing algorithms are required. This situation probably explains why, in practice, AE-based methods consist mostly in the calculation of scalar quantities such as RMS and Kurtosis, and the analysis of their evolution in time. While the scalar-based approach offers the advantage of maximum data reduction; it has the disadvantage that most part of the information contained in the raw AE signal is lost unrecoverably. This work presents a method offering large data reduction, while keeping the most important information conveyed by the raw AE signal, useful for failure detection and diagnosis. The proposed method consist in the construction of a synthetic, unevenly sampled signal which envelopes the AE bursts present on the raw AE signal in a triangular shape. The constructed signal – which we call TriSignal – also permits the estimation of most scalar quantities typically used for failure detection. But more importantly, it contains the information of the time of occurrence of the bursts, which is key for failure diagnosis. Lomb-Scargle normalized periodogram is used to construct the TriSignal spectrum, which reveals the frequency content of the TriSignal and provides the same information as the classic AE envelope. The paper includes application examples in planetary gearbox and low-speed rolling element bearing.

Deformation behavior and acoustic emission response on uniaxial compression of extruded rectangular profile of Mg[sbnd]Zn[sbnd]Zr alloy

The mechanical behavior of MgZnZr alloy in a form of extruded rectangular profile is related to the initial crystallographic texture and the twinning activity. The profile with a thickness of 12 mm was uniaxially compressed in three directions (extrusion (ED), transversal (TD) and normal direction (ND)) at room temperature. Concurrently, acoustic emission (AE) measurements were performed in order to study the deformation mechanisms. The AE events, stemmed from collective dislocation movement and twin nucleation, were identified using statistical analysis of the raw AE signal. The texture evolution was studied by X-ray diffraction on samples deformed in compression up to specific stress levels. Along the ED and TD the dominant deformation mechanism is extension twinning, which is accompanied by intensive acoustic emission and strain hardening increment. In contrast, dislocation slip prevails in ND, which causes deformation curves similar to that usually observed in tension.