Acoustic emission monitoring of lithium-ion battery aging and failure mode transition under low-temperature conditions

An extraction method of damage modes in composite laminates from acoustic emission (AE) signal based on ensemble empirical mode decomposition (EEMD) and decorrelation algorithm is proposed. Based on previous works about damage modes identification of composite laminates using AE, the peak frequency of AE signal could distinguish various damage modes effectively. Energy generated by several damage modes can be included in a single AE signal, the empirical mode decomposition (EMD) could decompose AE signal into a set of intrinsic mode functions (IMFs), the peak frequency of each IMF was chosen to identify damage modes, but the analysis show that a single IMF would include components of dramatically disparate scales. Based on the above problems, the decorrelation ensemble empirical mode decomposition (DEEMD) method is put forward, DEEMD is the more effective solution for extracting all damage modes existing in a single AE signal than EMD, can eliminate mode mixing, would provide practical application guidance for composite laminates structure damage monitoring using AE.

This article presents a classification of Acoustic Emission (AE) signals from AlSi10Mg specimens produced via Selective Laser Melting (SLM). Tensile tests characterized the mechanical properties of specimens printed in different orientations (X, Y, Z, 45°). Initially, a study quantified damage modes based on the stress-strain curve and cumulative AE energy. AE signals for each specimen (X, Y, 45°, Z), across deformation stages (elastic and plastic), and damage modes were analyzed using continuous wavelet transform to extract time-frequency features. A novel convolutional neural network, based on artificial bee colonies and fuzzy C-means, was developed for scalogram classification. Data augmentation with Gaussian white noise enhanced the approach. Cross-validation ensured robustness against overfitting and suboptimal local maxima. Evaluation metrics, including the confusion matrix, precision-recall curve, and F1 score, demonstrated the algorithm's high accuracy of 92.6%, precision-recall curve of 92.5%, and F1 score of 92.5% for AE signals based on printing direction (X, Y, 45°, Z). The study highlighted the potential for improving AE signal classification related to elastic and plastic deformation stages with 100% accuracy. For damage modes, the algorithm achieved a confusion matrix accuracy of 90.6%, a precision-recall curve of 90.4%, and an F1 score of 90.5%. This approach demonstrates high accuracy in classifying AE signals across different printing orientations, deformation stages, and damage modes of AlSi10Mg specimens manufactured through SLM.

Discrimination of acoustic emission (AE) signals related to different damage modes is of great importance in carbon fiber-reinforced plastic (CFRP) composite materials. To gain a deeper understanding of the initiation, growth and evolution of the different types of damage, four types of specimens for different lay-ups and orientations and three types of specimens for interlaminar toughness tests are subjected to tensile test along with acoustic emission monitoring. AE signals have been collected and post-processed, the statistical results show that the peak frequency of AE signal can distinguish various damage modes effectively. After a AE signal were decomposed by Empirical Mode Decomposition (EMD) method, it may separate and extract all damage modes included in this AE signal apart from damage mode corresponding to the peak frequency. Hilbert-Huang Transform (HHT) of AE signals can clearly illustrate the frequency distribution of Intrinsic Mode Functions (IMF) components in time-scale in different damage stages, and can calculate accurate instantaneous frequency for damage modes recognition to help understanding the damage process.

In this study, various damage modes in bending unimorph piezoelectric composite actuators with a thin sandwiched PZT plate during bending fracture tests have been evaluated by monitoring acoustic emission (AE) signals in terms of waveform and peak frequency as well as AE parameters. Three kinds of actuator specimens consisting of woven fabric fiber skin layers and a PZT ceramic core layer are loaded with a roller and an AE activity from the specimen is monitored during the entire loading using an AE transducer mounted on the specimen. AE characteristics from a monolithic PZT ceramic with a thickness of 250 ㎛ are examined first in order to distinguish different AE signals from various possible damage modes in piezoelectric composite actuators. Post-failure observations and stress analyses in the respective layers of the specimens are conducted to identify particular features in the acoustic emission signal that correspond to specific types of damage modes. As a result, the signal classification based on waveform and peak frequency analyses successfully describes the failure process of the bending piezoelectric composite actuator exhibiting diverse failure mechanisms. Furthermore, it is elucidated that when the PZT ceramic embedded actuators are loaded mechanical bending loads, the failure process of actuator specimens with different lay-up configurations is almost same irrespective of their lay-up configurations.

In order to explore the influence of wood’s anisotropic characteristics on Acoustic Emission (AE) signals’ propagation, the law of AE signals’ propagation velocity along different directions was studied. First, The center of the specimen’s surface was took as the AE source,then 24 directions were chose one by one every 15º around the center,and 2 AE sensors were arranged in each direction to collect the original AE signals. Second, the wavelet analysis was used to denoise the original AE signals, then the AE signals were reconstructedby Empirical Mode Decomposition (EMD). Finally, time difference location method was utilized to calculate AE signals’ propagation velocity. The results demonstrate that AE signals’ propagation velocity has obvious feature of quadratic function. In the range of 90º, as the angle of propagation direction increases, the propagation velocity of the AE signals presents a downward trend.

Crack rubbing or clapping in metallic structures generates acoustic emission (AE) signals. Such AE signals need to be distinguished from AE signal due to fatigue crack growth event. AE signal due to crack rubbing or clapping of fatigue generated crack was studied for a plate specimen. 20 mm fatigue crack was generated in a 1 mm thick aluminum plate specimen. Vibration-induced excitation was performed on the specimen to induce crack faying surface-motion for AE signal generation. Various specimen resonances have different crack faying surface motions, which were studied from FEM analysis. Modeshapes and crack faying surface motions of the specimen are studied at 35 Hz and 180 Hz specimen resonances. AE signals at various specimen resonances were recorded by piezoelectric wafer active sensors (PWAS) and the recorded waveforms are analyzed to obtain AE signatures. At various specimen resonances, AE signals have different signatures due to the change in crack faying surface motions. AE recording was done by using multiple PWAS sensors placed at various distances from the crack. The difference in AE signals close to crack and distant from crack as well as the geometric spreading of AE signals originating due to crack rubbing was studied from multi-sensor experiments.

The observed acoustic emission (AE) signals of PZT transducers during glass capillary breaking were compared with the theoretical AE signals based on the AE system approach. The AE system composed of (1) an AE source: a glass capillary breaking, (2) a propagating medium: a glass plate, (3) a detector: a PZT transducer and (4) a signal processing unit: a digital oscilloscope was nearly constant in order to minimize the diculty of the AE system approach, but the thickness of a PZT transducer with a consistent diameter was adjusted to change the resonant frequency. The theoretical AE signals were obtained by using the Mason equivalent circuit and a point loading on a glass plate with a ramped functional dependence for a force strength of 10 N and a rise time of 280 ns. The observed AE signals compared with the theoretical AE signals had pulse-type AE signals and continuous-type AE signals with low frequencies. The pulse-type AE signals may originate from the thickness mode of the PZT transducer and the continuous-type AE signals may originate from the radial mode of the PZT transducer.

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.

This paper presents a real-time tool breakage detection method for small diameter drills using acoustic emission (AE) and current signals. Using the transmitted properties of the AE signal, apparatus for detecting the AE signal for tool breakage monitoring was developed for a machine centre. The features of tool breakage were obtained from the AE signal using typical signal processing methods. The continuous wavelet transform (CWT) and the discrete wavelet transform (DWT) were used to decompose the spindle current signal and the feed current signal, respectively. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent real-time capability and a high success rate for the detection of the breakage of small diameter drills using combined AE and current signals.

Mechanical structures, such as pressure vessels and pipes, need careful inspection and monitoring to avoid serious corrosion failure. Detecting and identifying corrosion damage from acoustic emission (AE) signals is of significant importance for the safety and reliability of engineering structures in structural health monitoring. The identification accuracy largely depends on how well the damage features are being used. This paper presents a new approach for extracting effective damage features and accurately identifying different damage from AE signals during corrosion monitoring. Specifically, the proposed approach combines ensemble empirical mode decomposition and linear discriminant analysis to analyze the AE signals generated from an intergranular corrosion process. The results show that three damage modes, including environmental noise, intergranular corrosion, and the formation and propagation of cracks can be successfully detected and identified from complicated AE waveforms. The proposed approach is capable of providing reliable, direct and visualized corrosion damage detection and identification in structural health monitoring. Results from this study will guide complementary efforts aimed at detecting and identifying different damage from AE signals, and providing supporting knowledge regarding the industrial application of AE monitoring.

Identifying the different damage modes of wood is of great significance for monitoring the occurrence, development, and evolution of wood material damage. This paper presents the research results of the application of acoustic emission (AE) technology to analyze and evaluate the mapping relationship between the damage pattern of wood in the fracture process and the AE signal. For the three-point bending specimen with pre-crack, the double cantilever beam specimen, the single fiber tensile specimen, and the uniaxial compression specimen, the bending tensile compression test and the AE monitoring were performed, respectively. After the post-processing and analysis of the recorded AE signal, the results show that the peak frequency of AE is an effective parameter for identifying different damage modes of wood. In this study, the empirical mode decomposition (EMD) of the AE signal can separate and extract a variety of damage modes contained in the AE signal. The Hilbert–Huang transform (HHT) of the AE signal can clearly describe the frequency distribution of intrinsic mode function (IMF) components in different damage stages on the time scale, and can calculate instantaneous energy accurately, which provides a basis for damage mode recognition and lays a foundation for further accurate evaluation of the wood damage process.

Failure of composite materials often involves more than one damage mode, such as matrix cracking, fibre breaking, fracture of the fibre-matrix interface, delamination and fibre pull-out. Splitting of a thin ply unidirectional reinforced material along its fibre direction [1] is possibly the most simple damage mode to be found in a real composite. The composite will fail by a single damage mode, i.e. matrix cracking, as long as the reinforcing fibres are perfectly aligned and the crack propagation is confined in a direction at zero degrees to the fibre axis. Slight deviation of the crack path or of the fibre alignment will cause the crack front to intercept the fibre-matrix interface. If the interface is weak, fracture of the fibre-matrix interface will occur as a second damage mode in the failure process. The crack front will then continue to propagate within the fibre-matrix interface or kink out back into the matrix. This phenomenon will cause matrix cracking and fracture of the fibre-matrix interface to alternately dominate the failure process. The crack front may also kink out from the fibre-matrix interface into the adjacent fibre to cause fibre breaking. In this case the failure process will be governed by a combination of three damage modes, i.e. matrix cracking, fibre-matrix fracture and fibre breaking. If the fibre-matrix interface is strong enough, the crack front which intercepted the interface will continue to propagate into the fibre to cause fibre breaking. Thus failure of the composite will be contributed to by combination modes of matrix cracking and fibre breaking. Understanding the failure process of the composites is necessary for designing and working with these materials. More important than that, monitoring the integrity of a composite under service will only be possible when the above-mentioned damage modes are fully understood. Acoustic emission is a promising technique which is receiving increasing attention for monitoring the integrity of components as well as investigating the failure mechanism of the materials, The ability of the technique to provide information on damage progression in real time gives it advantages [2] over other non-destructive techniques for continuous monitoring. It is a common aim among researchers and engineers to establish a correlation between damage modes and the characteristics of their acoustic emission signals. Peak amplitude is the most frequent acoustic emission parameter which has been manipulated [3-5] in order to distinguish the

Externally bonded (EB) carbon fiber reinforced polymer (CFRP) is widely used in structural strengthening and retrofitting. Premature debonding of the FRP severely limits the efficiency of CFRP utilization. The application of CRRP anchorage system offers a solution to the debonding problem. However, the understanding of damage mode identification of this combined system still remains elusive. Acoustic emission (AE) technique is employed to identify the damage mode of this CFRP anchorage system, due to its high sensitivity and the ability to detect damage in real-time. The objective of the current study is to identify the failure mechanisms of CFRP-strengthened beam by applying advanced pattern recognition techniques to the collected AE data. Firstly, four-point test of CFRP-strengthened beam was carried out until failure with simultaneous recording of AE signals. Then, correlation analysis was adopted to select the AE characteristic parameters, and principal component analysis (PCA) was used for dimensionality reduction. Lastly, the AE signals of the CFRP-strengthened beam was clustered to track the evolutionary behavior of the different damage modes by Gaussian mixture model (GMM) algorithm. Three main damage modes of CFRP-strengthened beam were identified by GMM clustering: concrete cracking, debonding of CFRP sheet and fracture of CFRP sheet. This study explores the damage evolution mechanism of combined system and provides a basis for achieving health monitoring of CFRP-strengthened structures.

Acoustic emission (AE) is a non-destructive testing technique, and establishing correlations between AE signals and material damage modes is one of its primary challenges. However, it is difficult to identify damage modes in ceramic matrix composites (CMCs) due to AE signal attenuation occurring after propagation and complex damage modes. In this study, AE signals generated by the breakage of C and SiC fibers were monitored at different distances and angles on the C/SiC plate. The attenuation of energy and the frequency spectra were analyzed. The Mel-frequency cepstral coefficient (MFCC) method was used to analyze the waveform data of AE signals and extract MFCC features. To identify the damage caused by C and SiC fiber breakage, AE parameter features and MFCC features were selected as inputs, and a fully connected neural network was constructed to train a supervised pattern recognition model. The results show that the MFCC feature has higher recognition accuracy than the traditional feature when AE is used for damage identification.

Tensile fatigue behavior and damage evolution of z-pinned carbon/phenolic woven composite using in-situ monitoring technology