Acoustic Emission Descriptors Research Articles

Reduction in the Sensor Effect on Acoustic Emission Data to Create a Generalizable Library by Data Merging.

The aim of this paper is to discuss the effect of the sensor on the acoustic emission (AE) signature and to develop a methodology to reduce the sensor effect. Pencil leads are broken on PMMA plates at different source-sensor distances, and the resulting waves are detected with different sensors. Several transducers, commonly used for acoustic emission measurements, are compared with regard to their ability to reproduce the characteristic shapes of plate waves. Their consequences for AE descriptors are discussed. Their different responses show why similar test specimens and test conditions can yield disparate results. This sensor effect will furthermore make the classification of different AE sources more difficult. In this context, a specific procedure is proposed to reduce the sensor effect and to propose an efficient selection of descriptors for data merging. Principal Component Analysis has demonstrated that using the Z-score normalized descriptor data in conjunction with the Krustal-Wallis test and identifying the outliers can help reduce the sensor effect. This procedure leads to the selection of a common descriptor set with the same distribution for all sensors. These descriptors can be merged to create a library. This result opens up new outlooks for the generalization of acoustic emission signature libraries. This aspect is a key point for the development of a database for machine learning.

A hybrid deep learning method for AE source localization for heterostructure of wind turbine blades

An acoustic emission (AE) and hybrid deep learning networks based damage source localization method for heterostructure of wind turbine blades is proposed in this paper. Firstly, comprehensive data preprocessing is performed, including AE signal denoising, feature extraction, feature selection and normalization. New training features including AE descriptors, features of time, frequency domains and spectral features are extracted. A feature selection method based on Light-GBM and correlation analysis is employed to identify relevant features for AE source localization. Subsequently, two deep learning networks, AM-BiLNN and AM-LCNN, are developed to locate the damage source in two steps. Then, numerical tests are implemented on localized structure of a wind turbine blade to verify the performance of the proposed method and the performance of the selected features and the robustness of the proposed method under noise are investigated. Furthermore, a comparative investigation between the proposed method with long short-term memory neural networks (LSTM), convolutional neural networks (CNN) and the cluster-based method is carried out to demonstrate the superiority of the proposed method. The results highlight the superiority and robustness of the proposed method. Feature selection is shown to effectively enhance coordinate localization performance.

Use of acoustic emission to monitor adipic acid crystallization

Crystallization is regarded as an important unit operation for separation and purification. However, it is still difficult to control or to optimize the crystallization process due to the complexity of the coupled phenomena taking place simultaneously in the liquid and solid phases. In order to overcome such drawbacks, different analytical technologies have been implemented in the literature for monitoring the key crystallization parameters. In our work, a multi-probe monitoring system composed of acoustic emission, spectroscopic and imaging probes was applied to the crystallization of a model system (an aqueous solution of adipic acid). The crystallization was carried out under vacuum or atmospheric pressures. The goal was to demonstrate the usefulness of the multi-probe system for monitoring the crystallization process, with special attention to the information given by the acoustic emission, in terms of absolute energy, and the spectroscopic probes. Firstly, the influence of the crystal load on the absolute energy is demonstrated. Then, it is shown how the absolute energy can capture the modifications in the crystallization dynamics due to the different experimental conditions. Such dynamics can also be observed from other acoustic emission descriptors.

Damage monitoring of carbon fibre reinforced polymer composites using acoustic emission technique and deep learning

In this research work, a deep Convolutional Neural Network (CNN) was trained for image-based Acoustic Emission (AE) waveform classification. AE waveforms from different damage modes of Carbon Fibre Reinforced Polymer (CFRP) composites were used to train the CNN for online damage monitoring. Spectrograms of AE Waveforms from four different damage modes, matrix cracking, delamination, debonding, and fibre breakage, were obtained in their Mel scale and used as the training data and test data for the CNN. The overall prediction accuracy of the CNN is 97.9%, while the fibre breakage and delamination events were able to be predicted with 100% accuracy. Then this pre-trained CNN is used for online damage monitoring of mode I delamination test of CFRP specimens. AE waveforms generated during the mode I test are classified using the trained CNN and the results are analysed in terms of the classified AE descriptors. The classified AE descriptors proved to identify the occurrences of different damage modes, thereby validating the damage classification accuracy of the CNN.

Damage assessment of carbon fibre reinforced plastic using acoustic emission technique: experimental and numerical approach

In this research work, the acoustic emission results obtained from testing double cantilever beam specimens with carbon fibre reinforced plastic laminates are analysed. The acoustic emission descriptors such as amplitude, frequency centroid, counts, duration and risetime are clustered using k-means++ algorithm. An unconventional and innovative way of using the acoustic emission descriptors, after the clustering, is introduced. This method can favourably be used for relating the different damage progression modes in fibre reinforced plastics. Apart from this, the cumulative acoustic energy is used for predicting the crack length of the specimens. The predicted crack length is almost identical to the actual crack length opening recorded in each specimen. Finally, analytical and finite element models are used for validating the experimental results under the mode I delamination. The finite element studies are carried out using cohesive zone modelling in Comsol Multiphysics® platform.

Acoustic emission descriptors for the mechanical behavior of selective laser melted samples: An innovative approach

Three different groups of AlSi10Mg specimens are built using Selective Laser Melting (SLM) technique. Each of these groups represents the orientation of the specimens with respect to the building plane. The mechanical characteristics of the specimens are evaluated under uniaxial tensile loading. The Acoustic Emission (AE) technique is used for characterizing the failure modes of the specimens under the uniaxial loading. An innovative parameter, the slope coefficient bAE, is formulated for directly relating the different failure modes in the tested specimens with the acoustic results. The slope coefficient bAE proves to be a powerful tool for understanding the damage characteristics of the specimens. In addition to that, the AE signals are recorded during the tests and are analysed in the Hilbert Spectrum. The signals are first decomposed using Empirical Mode Decomposition (EMD) and then reconstructed after removing the noise. The instantaneous frequencies obtained from the Hilbert Spectrum of the reconstructed signals are used for analysing the damage modes. From the results obtained, it is safe to say that the AE technique is powerful for characterizing the failure modes in materials during their entire loading history.

Bonding Characteristics of Single- and Joggled-Lap CFRP Specimens: Mechanical and Acoustic Investigations

Two different configurations of adhesive-bonded carbon fiber-reinforced plastic (CFRP) specimens, joggled lap-joint specimens and single lap-joint specimens, are mechanically tested. The mechanical tests show that the joggled lap specimens have lower strength than the single lap specimens. The damage modes in both the specimens are analysed by the Acoustic Emission descriptors recorded during the mechanical tests. The acoustic data as cumulative counts and cumulative energy show the critical points of failure in both the specimen groups under loading. Moreover, they also show that the damage modes in both the specimens are dissimilar. Finally, the data provided by acoustic emission descriptors are verified by fractographic analysis on the failed surface.

Experimental wavelet analysis of acoustic emission signal propagation in CFRP

In this research work, the characteristics of acoustic wave propagation were studied for different Carbon Fiber/Epoxy laminated composites (CFRP). Piezoelectric transducers were used to simulate the acoustic wave propagation through the materials and the possible effects of ply number, fiber orientation and the material thickness of the CFRP on the propagation of acoustic signals were studied. The relative loss of the Acoustic Emission (AE) signals has been parameterized in terms of variations in the peak amplitude, acoustic energy and duration of the recorded acoustic signals. The effect of the material properties on the relative loss in AE descriptors has been assessed. Waveform analysis was performed for the simulated and received acoustic signals to identify changes in amplitude and waveform. Wavelet analysis for the recorded acoustic waveforms were performed by the Continuous Wavelet Transform. The effect of material properties on the propagation of the acoustic signal was characterized through AE descriptors, Waveform and Wavelet Analysis.

Acoustic characterization of damage and healing of microencapsulation-based self-healing cement matrices

Self-healing of cracks in cementitious composites is of great significance to improve the serviceability of concrete structures. In this study, poly urea–formaldehyde (PUF) microcapsules enclosing epoxy resins were synthesized. The damage- and healing process of cement paste incorporating microcapsules was in situ detected by acoustic emission (AE) technique, in which passive AE and active AE were combined to provide complementary information about the damage and crack formation. The two representative AE signals, i.e., matrix cracking and debonding of the interface, were used as calibration for further AE post-processing analysis. The effects of the concentration of microcapsules and the level of pre-damage were investigated. The results revealed the distinguished cracking mechanisms according to the differentiated feature of the signals in terms of the temporal and spectral AE descriptors. The plot of average frequency (AF) versus RA index (rise time/amplitude) confirms that the cracking modes contribute to the characteristic spectrum.

Acoustic emission investigation of the mechanical performance of carbon nanotube-modified cement-based mortars

Concrete’s inherent brittleness, low tensile strength and premature micro-cracking phenomena can be improved by embedment, in the bulk material, of carbon nanotubes (CNT), the 1-D allotrope of carbon exhibiting a remarkable combination of mechanical and transport properties. The present study reports on the flexural and compressive mechanical performance of mortars reinforced with variable loadings of multi-walled carbon nanotubes. Four point bending tests were performed with simultaneous in situ monitoring of the acoustic emission (AE) activity of the mortars, a technique herein applied for the first time in nanotube-reinforced cement, and the data were used to evaluate the efficiency of the non-destructive method in assessing the sensitivity of the mortars’ properties on tube loading. A CNT concentration of 0.4% relative to cement mass, was found related to optimal improvements in flexural strength. Impressively, two key AE descriptors, namely average frequency and the rise time over amplitude ratio were independently found to simultaneously maximize at the same tube loading while their temporal variation revealed a transition in the material’s fracture mode with tube loading. Complications due air entrapment in the materials facilitated by nanotube presence are discussed in the text and a viable resolution is suggested.

Crack Growth Monitoring in Ceramic Matrix Composites by Combined Infrared Thermography and Acoustic Emission

The current study proposes a novel methodology for measuring crack growth in composite materials using combined infrared thermography (IRT) and acoustic emission (AE). The technique is tested across a SiC‐fiber‐reinforced ceramic matrix composite while no apparent factor is limiting its usage on composite materials of different nature as well. Compact tension specimens were loaded in tension with unloading/reloading loops and the thermally dissipated energy due to crack growth and other damage mechanisms was captured by IRT with a 100 Hz sampling rate. Crack growth was established by identifying the time instances where the maximum temperature, hence also damage, occurred and then quantifying, by means of control lines, the damage span within the thermograph corresponding to the specific instance. The high accuracy of the proposed technique was validated against optical measurements of crack length. The theoretical crack length predicted by the elastic compliance technique was found to overestimate the experimental findings by at least 25%. Knowledge of the critical level of damage accumulation for material structural health was made possible from AE descriptors such as activity during the unloading part of the cycles. In this study, AE was particularly successful in closely following the actual crack growth measured by IRT, an observation that brings out the potential of the technique for quantitative measurements.

Classification and identification of damage mechanisms in polyethylene self-reinforced laminates by acoustic emission technique

The objective of present study is to classify and identify damage mechanisms in polyethylene(PE) self-reinforced composites by acoustic emission (AE) technique. Model specimens including LDPE resin, [90°]laminate, single fiber composite, fiber bundle composite, and [±45°] laminates are fabricated to obtain expected damage mechanisms during tensile testing. First, mechanical behaviors and corresponding AE response of model specimens are studied to validate damage mechanisms in UHMWPE/LDPE laminates. Second, relationship among AE descriptors is investigated by hierarchical cluster analysis, and AE signals are classified by k-means cluster analysis. Correlations between damage mechanisms and AE are established in terms of amplitude, duration, and peak frequency of AE signals. Finally, an artificial neural network is created and trained by various optimal algorithms to identify damage mechanisms. The results reveal that typical damage mechanisms in PE self-reinforced composite can be classified in terms of the similarity between AE signals and identified by trained artificial neural network. POLYM. COMPOS., 2011. © 2011 Society of Plastics Engineers

Analysis of Properties of Chosen Acoustic Emission Descriptors Describing Acoustic Signals Measured by Means of Acoustic Emission Method within Oil Transformers

Analysis of Properties of Chosen Acoustic Emission Descriptors Describing Acoustic Signals Measured by Means of Acoustic Emission Method within Oil Transformers F. Witos∗ and A. Olszewska Department of Optoelectronics, Silesian University of Technology, Krzywoustego 2, 44-100 Gliwice, Poland Authors of the paper present investigation results concerning properties of ADP, ADC and ADNC descriptors which have been obtained during measurements made within three oil transformers and then tested also by other methods (electric and dissolved gas analysis ones). Methodology which makes easier an analysis and identification of acoustic emission signals generated by partial discharges is presented. Results obtained by acoustic emission method have been referred to results coming from other measuring methods.

Properties of Acoustic Emission Signals Coming from Partial Discharges Caused by Modeled Sources Immersed in Transformer Oil

Investigation results concerning acoustic emission signals coming from partial discharges generated by modeled sources are presented in the paper. These sources have been made using different bushing ends (without any extra elements and with a thread or a pike) situated directly in the oil, but without screens typical for partial discharge investigations in a bushing. Measurements have been carried out using own measuring acoustic emission system DEMA-COMP and — in a parallel way — computer-aided partial discharges measuring system TE 571 (produced by the firm Haefely Trench). Fundamental and advanced analysis of acoustic emission signals has been made. These signals were recorded in 20 measuring situations which need to multiple installation of acoustic emission sensors. Conclusions resultant from own originate advanced analysis of signals content description of properties revealed by defined acoustic emission descriptors.

Using acoustic emission to assess shear strength degradation in FRP composites due to constant and variable amplitude fatigue loading

Acoustic emission was used to assess static shear strength degradation, due to fatigue, in unidirectional fibre-reinforced composites. According to standard specifications, [±45] S specimens were used. Coupons, made of Gl/Ep material, were subjected to tensile constant or variable amplitude loading, at several stress level and life fraction combinations, to investigate influence of various loading configurations on damage accumulation. Two acoustic emission descriptors were introduced and respective engineering models, estimating residual strength, were developed. The performance of the former scheme was excellent, although knowledge of the maximum loading stress value was required in the implementation. In the second scheme, on the other hand, no additional information on the specimen load history was needed. The potential of both models was also validated on two independent verification sets: one consisting of specimens loaded under a variable amplitude spectrum and another of an alternative matrix system, subjected to constant amplitude fatigue.

Quality assessment of electric low-power motor insulation on the ground of partial discharge investigations using the calibrated acoustic emission method

Investigation of partial discharge (PD) within the models representing winding insulation of low-power induction motors have been carried out. Apparent electric charge introduced by PD sources has been measured basing on the electric method, whereas the acoustic one has been used to determine properties of acoustic emission signals in the time, discrimination threshold and time-frequency domain. Acoustic emission descriptors (signified by acronyms ADC and ADP, constructed in discrimination threshold domain) distinguish and classify the models connected with different impregnation technologies of winding insulation.