Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

A novel machine learning-based model is introduced in this research paper to forecast tool wear using acoustic emission (AE) signals. Adaptive boosting (AdaBoost) and a sophisticated feature engineering strategy are employed by the model to enhance the precision of its predictions. The proposed model, Machine Learning Tool Wear Prediction (MLTWP), analyzes AE signals generated during machining operations to distinguish between healthy and worn-out tool conditions with remarkable accuracy. The crux of our approach consists of meticulously eliminating and enhancing the temporal and spectral characteristics of the AE signals. We employ the Kolmogorov-Smirnov test to identify the most valuable classification features. We implemented AdaBoost with the objective of progressively enhancing a set of weak classifiers’ ability to identify instances that were incorrectly classified in previous iterations. Utilizing this method increases the model’s sensitivity to minute variations in tool wear conditions and its overall classification precision. The MLTWP model underwent extensive testing on a benchmark data set comprising 25,304 AE signal records from cutting mill tools, using a training tool split of 9,989 worn (positive) and 8,990 benign (negative) instances. The results of our experiments, validated through four-fold cross-validation, indicate that the MLTWP model exhibits superior performance compared to the existing Tool Wear Prediction using Acoustic Emission Signals (TWPAE) model. To provide greater specificity, the MLTWP exhibited the following metrics on average: precision (92.2%), specificity (91.38%), sensitivity (90.42%), accuracy (90.9%), and MCC (81.72%). The fact that these metrics exhibit significant improvement over those of the TWPAE model demonstrates that our method of feature engineering and adaptive boosting is effective at precisely predicting tool wear. This research not only advances the existing understanding of tool wear prediction but also establishes a robust framework for the implementation of machine learning in manufacturing predictive maintenance.

Characterization of piezoelectric wafer active sensor for acoustic emission sensing

This paper is focused on the application of acoustic emission (AE) based monitoring systems to grinding processes. To develop the AE signal model for the process monitoring, a comparison between the AE and power signals is presented. The AE model is developed according to a traditional grinding force model. Based on the developed model, experiment indicates the AE signal is similar to the power of spindle and verifies the proposed AE model can be used to predict the AE root meam square (RMS) signal for monitoring the grinding process.

The new method for discrete fractional cosine transform is proposed, and it was introduced in acoustic emission signal enhancement. According to the characteristic of rub-impact acoustic emission (AE) signal from rotating machinery, two constructions are proposed to enhance AE signal, which based on three cycles discrete fractional cosine transform (FDCT3), and modified transform (MFDCT3). The experiment date was rub impact AE signals sampled from rotating test stand, Gaussian white noise and non-stationary noise were added to simulate the real AE signal which polluted seriously by noise. Then, FDCT3 and MFDCT3 were used into de-noising and AE recognition.The result shown that FDCT3 and MFDCT3 are effective at the de-noising, and those performance is better than DCT.

A multisignal nanosecond synchronous acquisition system to measure acoustic emission (AE) and electromagnetic radiation (EMR) generated during the process of loading and failure of coal and rock samples is established. The correlation between the energy of the AE and EMR signals and the loading stress of outburst coal-rock samples was studied, and the characteristics of the AE and EMR signals during the process of loading and fracturing the outburst coal and rock samples were analyzed. The results show that (1) before the failure of the outburst coal and rock samples, the fluctuation of the AE and EMR signals is the largest, with the same rising and falling trend, and the intensity is not strictly positively correlated, with the phenomenon of low EMR when the AE intensity is high; (2) the EMR and AE deviation degree and frequency exhibit a good response to coal and rock fracturing. The correlation between EMR and stress drop is stronger than that of AE, and the AE signal is richer than the EMR signal. The results show that it is feasible to develop combined AE and EMR early warning technology to improve the early forecasting accuracy of coal and gas outbursts.

There is considerable interest in techniques for ensuring the quality of the laser joining process. One approach to quality monitoring is through analysis of acoustic and optical emission signals acquired during welding. Preliminary analyses confirm that these signals fluctuate chaotically, suggesting that there may be benefits to a nonlinear signal processing approach. Lyapunov exponents of cleaned signals were calculated to quantify the nonlinear nature of the process. Inspection of Lyapunov exponents calculated from optical and acoustic emission signals corresponding to a variety of weld penetration conditions revealed that no sharp distinction could be found. The Lyapunov exponents were found to depend strongly on the power and travel speed settings of the process. This variation masked any variations due solely to weld penetration. There was some distinction in Lyapunov exponents noted between non-gap and gap butt joint welds. The Lyapunov exponent for butt welds with significant joint gap was higher than for non-gapped welds. It was concluded that monitoring of weld gap might be possible using the Lyapunov exponent of the optical and/or acoustic signals.There is considerable interest in techniques for ensuring the quality of the laser joining process. One approach to quality monitoring is through analysis of acoustic and optical emission signals acquired during welding. Preliminary analyses confirm that these signals fluctuate chaotically, suggesting that there may be benefits to a nonlinear signal processing approach. Lyapunov exponents of cleaned signals were calculated to quantify the nonlinear nature of the process. Inspection of Lyapunov exponents calculated from optical and acoustic emission signals corresponding to a variety of weld penetration conditions revealed that no sharp distinction could be found. The Lyapunov exponents were found to depend strongly on the power and travel speed settings of the process. This variation masked any variations due solely to weld penetration. There was some distinction in Lyapunov exponents noted between non-gap and gap butt joint welds. The Lyapunov exponent for butt welds with significant joint gap was higher...

The Acoustic emission (AE) technique, as one of non-intrusive and nondestructive evaluation techniques, acquires and analyzes the signals emitting from deformation or fracture of materials/structures under service loading. The AE technique has been successfully applied in damage detection in various materials such as metal, alloy, concrete, polymers and other composite materials. In this study, the AE technique was used for detecting crack behavior within concrete specimens under mechanical and environmental frost loadings. The instrumentations of the AE system used in this study include a low-frequency AE sensor, a computer-based data acquisition device and a preamplifier linking the AE sensor and the data acquisition device. The AE system purchased from Mistras Group was used in this study. The AE technique was applied to detect damage with the following laboratory tests: the pencil lead test, the mechanical three-point single-edge notched beam bending (SEB) test, and the freeze-thaw damage test. Firstly, the pencil lead test was conducted to verify the attenuation phenomenon of AE signals through concrete materials. The value of attenuation was also quantified. Also, the obtained signals indicated that this AE system was properly setup to detect damage in concrete. Secondly, the SEB test with lab-prepared concrete beam was conducted by employing Mechanical Testing System (MTS) and AE system. The cumulative AE events and the measured loading curves, which both used the crack-tip open displacement (CTOD) as the horizontal coordinate, were plotted. It was found that the detected AE events were qualitatively correlated with the global force-displacement behavior of the specimen. The Weibull distribution was vii proposed to quantitatively describe the rupture probability density function. The linear regression analysis was conducted to calibrate the Weibull distribution parameters with detected AE signals and to predict the rupture probability as a function of CTOD for the specimen. Finally, the controlled concrete freeze-thaw cyclic tests were designed and the AE technique was planned to investigate the internal frost damage process of concrete specimens.

Utilizing optical fiber technique to structural health monitoring of composite materials is attracting attention recently due to the inherent advantages in optical fiber sensor. However, acoustic emission signals with low energy and high frequency from transverse cracks in carbon fiber–reinforced plastic laminates are difficult to be detected by normal optical techniques. In this work, we present a phase-shifted fiber Bragg grating balanced sensing system to detect acoustic emission signals generated in a carbon fiber–reinforced plastic laminate in a three-point bending test. We demonstrated that the phase-shifted fiber Bragg grating balanced sensing system with a broad bandwidth and high sensitivity can detect high-frequency, low-energy acoustic emission signals caused by transverse cracks. In our experiments, the transverse cracks were identified very well through the detected acoustic emission signals. The relationship between the strain and the acoustic emission signals, and the typical waveforms of the acoustic emission signals were investigated. Furthermore, accurate position identification using acoustic emission detection by the phase-shifted fiber Bragg grating sensor was demonstrated. All the results obtained from the phase-shifted fiber Bragg grating sensor were compared to the results obtained from commercial lead–zirconate–titanate acoustic emission sensors. We found that the phase-shifted fiber Bragg grating balanced sensing system performance was comparable to the commercial lead–zirconate–titanate acoustic emission sensors.

Strain localization peculiarities and distribution of acoustic emission sources in rock samples tested by uniaxial compression and exposed to electric pulses

Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy

In this work, a combination of an acoustic emission (AE) technique and a machine learning (ML) algorithm (Random Forest (RF) and Gradient Boosting Regressor (GBR)) is developed to characterize the particle size distribution in gas-solid fluidized bed reactors. A theoretical approach to explain the generation of acoustic emission signal in gas-solid flows is presented. An AE signal is generated in gas-solid fluidized beds due to the collision and friction between fluidized particles as well as between particles and the bed inner wall. The generated AE signal is in the form of an elastic wave with frequencies >100 KHz and it propagates through the gas-solid mixture. An inversion algorithm is used to extract the information about the particle size starting from the energy of the AE signal. The advantages of this AE technique are that it is a cheap, sensitive, non-intrusive, radiation-free, suitable for on-line measurements. Combining this AE technique with ML algorithms is beneficial for applications to industrial settings, reducing the cost of signal post-processing. Experiments were conducted in a pseudo-2D flat fluidized bed with four glass bead samples, with sizes ranging from 100 μm to 710 μm. AE signals were recorded with a sampling frequency of 5 MHz. The AE signal post-processing and data preparation for the ML process are explained. For the ML process, the AE frequency, AE energy and particle collision velocity data sets were divided into training (60%), cross-validation (20%) and test sets (20%). Two ensemble ML approaches, namely Random Forest and Gradient Boosting Regressor, are applied to predict particle sizes based on the AE signal features. The combination of these two models results in a coefficient of determination (R2) value greater than 0.9504.

Acoustic emission (AE) technique has been proved as a powerful technique for structural health monitoring. AE technique can also be efficiently used for evaluation of corrosion activity in concrete. Despite its advantages, an effective analysis of data recorded by AE technique still founds to be a challenging task demanding an appropriate damage assessment methodology. For quantification of damages, various methods for analysis of AE data have been proposed but not a single method has found to be standardized for specific application. In this paper, a procedure for analysis of AE signals is proposed using modified dimensional analysis method. Many times, it becomes difficult to choose the appropriate AE parameter which can be effectively co-related to the physical feature for development of accurate prediction model. Hence, in the present research work an attempt has been made to develop a model by incorporating primary characteristic AE waveform parameters. A corrosion rate prediction model using modified dimensional analysis of AE signals is developed and compared with the model developed using non-linear regression analysis. The performance of two models is further assessed using different statistical parameters. The study demonstrated that the methodology of modified dimensional analysis indicated improvement in the corrosion rate predictions. Thus, modified dimensional analysis can be implemented as a promising method for analysis of complex AE signal data as well as for development of statistical modelling of corrosion phenomenon in reinforced concrete based on recorded AE parameters.

A stiffener attached to a cylindrical shell strongly interferes with the propagation of the acoustic emission (AE) signal from the fault source and reduces the fault detection accuracy. The interaction of AE signals with the stiffener on the cylindrical shell is thoroughly investigated in this paper. Based on the proposed model of the AE signal propagating inside the cylindrical shell with a stiffener, the installation constraints for the sensor are derived, resulting in the separation of the direct signal, the stiffener scattering signal, and other signals in the time domain. On this basis, combinations of the excitation frequency and the stiffener height are simulated, and the reflection and transmission of the AE signal in each case are quantitatively characterized by the scattering coefficients. The results indicate that there is a “T-shaped” transformation of the signal at the stiffener, which evolves into a variety of other modes. Moreover, the reflection and transmission coefficients of the incident AE signal are displayed as a function of the excitation frequency and the height of the stiffener. In addition, the accuracy of the scattering coefficients obtained from the numerical simulations is verified by experiments, and a good consistency between simulation results and experiment results is presented. This work illustrates the propagation characteristics of AE signals in a cylindrical shell with a stiffener, which can be used as guidance for optimizing the spatial arrangement of sensors in AE monitoring.

Acoustic emission signals from concrete compression damage process are non-stationary and their arrival time is unknown. Therefore, the definition on the acoustic emission event in the time series is not clear. In this paper, the acoustic emission signals associated with concrete specimen under uniaxial compression are acquired by full-digital acoustic emission system. The overlapping phenomenon of acoustic emission signal is presented and discussed by the analysis of time interval between adjacent hits. Acoustic emission signals are classified into three categories according to their waveform characteristics. And the result shows that as the loading process continues, more and more amount of continuous type of acoustic emission signals appeared, indicating the acoustic emission signal does not carry the features of burst type, which will introduce obvious errors in the calculation of acoustic emission event. In contrast, the average signal level used in the analysis of continuous type of acoustic emission signal shows a good regularity with the damage process of concrete.

| openaire: EC/H2020/637422/EU//EVERYSOUND