Crack Signals Research Articles

A Mobius transformation-based liftoff effect reduction method for crack classification and prediction in eddy current testing

In eddy current (EC) defect evaluation, accurate determination of the crack depth is crucial. The lift-off signal on the complex impedance plane is not necessarily horizontal and mostly arc-shaped, which makes it difficult to separate from crack signals, which can have various orientations depending on probe characteristics and frequencies. Phase rotation is normally used to align the lift-off signal to the real axis and defect signal is read from the imaginary axis. However, this approach proves challenging to separate lift-off and defect signals within the complex plane in many cases.To address this issue, this study proposes and implements a Mobius transformation that linearizes the arc-shaped lift-off signals on the complex plane, enabling more accurate extraction of crack signals unaffected by lift-off signal height. Furthermore, it is demonstrated through k-nearest neighbors (kNN) that the transformed crack signals can still be effectively quantified.

Predicting actual crack size through crack signal obtained by advanced Flexible Eddy Current Sensor using ResNet integrated with CBAM and Huber loss function

This study presents an advanced FEC sensor, engineered by arranging coils in a co-directional current configuration. Moreover, boasting a compact design, the FEC sensor showcases significantly enhanced spatial resolution, enabling robust detection of small cracks even at low excitation frequencies and mitigating issues of overlapping in adjacent crack detection. Results indicate successful crack detection through voltage and phase measurements, albeit with phase signals demonstrating variation at specific excitation frequencies, complicating the determination of actual crack sizes. Consequently, a novel model is proposed to forecast actual crack sizes, leveraging experimental data from the FEC sensor system. This model integrates a Residual Neural Network (ResNet) architecture with a Convolutional Block Attention Module (CBAM) and utilizes the Huber loss function to minimize errors during model training. Comparative analysis underscores the superior performance of the proposed model in predicting crack length and depth compared to the standalone ResNet, particularly when utilizing the Huber loss function with a δ value of 1.0. Evaluation metrics, encompassing Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), illustrate an average accuracy surpassing 95 % for crack size predictions. Consequently, the proposed model demonstrates remarkable performance, significantly reducing the time required to ascertain actual crack sizes by leveraging voltage and phase measurements.

Mechanical Robustness Analysis of Semiconductors with a Single Needle Probe Card Using Acoustic Emissions

The combination of indentation testing and acoustic emission (AE) is widely used to analyze the fracture toughness of test substrates. In the manufacturing of semiconductor devices this material parameter also plays an important role. During the so-called wafer testing inside a wafer prober small probe tips are pressed onto the chip surface to check its performance. To prevent damaging the chip by that, the fracture toughness and load limit of it has to be defined in a prequalification step. This paper presents a test system that imitates the wafer testing process as close as possible and uses acoustic emission to detect appearing cracks and thereby also the load limit. The frontend of the test setup consists of a modular single needle probe card which can be placed in a wafer prober. Also, the indenter properties (tip diameter, stiffness, etc.) can be adjusted to the probe used later on during productive wafer testing to ensure realistic probing conditions. A piezoelectric sensor and a full strain gauge Wheatstone bridge are implemented close to the single indenter to measure the AEs and the applied contact force respectively. The signal-to-noise-ratios (SNRs) of both sensors are improved with an own analog amplifier module before recording them with an USB oscilloscope. A developed measurement software (LabVIEW) is used to adjust measurement parameters (trigger limit, record length, conversion factors, etc.) and automatically store and separate measurement data from different acoustic events and imprints. To evaluate the result files a second software tool (MATLAB) reads the files out and clusters the recorded events to separate crack signals from electrical and mechanical disturbances. With a prototype first test results are generated and the functionality and accuracy of the measurement concept is proven.

Investigating crack evolution, and failure precursor warning in sandstones with different water contents from the perspective of tensile-shear crack separation

To investigate the effect of water on the crack evolution and damage mode of sandstone, uniaxial compression experiments were conducted using samples with different water contents. The damage evolution process of sandstones with different water contents was investigated based on acoustic emission (AE) and digital image correlation (DIC) techniques. Further, the crack evolution was conducted using the RA-AF distribution method. The results showed that the brittleness of sandstone weakened with increasing water content, and the uniaxial compressive strength (UCS) and elastic modulus decreased significantly. The percentage of shear cracks increased continuously from 21.1% when dry to 36.53% when saturated and the evolution of these cracks through AE signals correlates closely with DIC observations. However, the influence of water on the development of these cracks differs significantly. Through the separation analysis of the two types of cracks, the damage evolution law of sandstone is clearer. Based on the critical slowing down theory (CSDT), the point at which both tensile and shear crack signals undergo abrupt fluctuation serves as an early warning indicator. The early warning point identification results of the sandstone with different water contents are all in the crack extension stage, the stress levels are all near 0.9 and demonstrate reliable predictive capabilities. Furthermore, the mechanism of water’s impact on the transition of crack types and the differences in damage modes in sandstone during the initial and later loading stages is explained by analyzing the characteristics of tensile and shear crack emergence and expansion.

An acoustic emission identification model for train axle fatigue cracks based on deep belief network

Railway axles are safety-critical components of the railroad rolling stock and the consequences of possible in-service failures can have a huge impact. Axle fatigue cracks are relatively common defects during train operation, but how to intelligently identify axle fatigue cracks in running trains is still a great challenge. In order to identify axle fatigue cracks more intelligently, the problem that needs to be solved is how to overcome the manual extraction of features by manual experience as well as shallow networks. Therefore, in this paper, an acoustic emission signal identification method based on deep belief networks (DBNs) for axle fatigue cracks is proposed. In this method, a DBN model is constructed. The axle fatigue crack acoustic emission signal data were obtained by our designed acquisition experimental setup, and these data were used to verify the accuracy of the constructed DBN network model identification. The experimental results show that the method of identification of axle fatigue cracks based on DBN, compared with the traditional fault diagnosis method, eliminates the operations of data feature extraction, feature screening, feature fusion, etc and makes complete use of all the information contained in the fault data. The method can not only identify fatigue crack signals but also has a high identification rate of fatigue cracks at different stages. In the axle fatigue crack acoustic emission identification field, it can be seen that the proposed method in this paper will be a promising approach.

Crack pattern identification in cementitious materials based on acoustic emission and machine learning

The cracking patterns in cementitious materials before failure are closely related to acoustic emission (AE) monitoring signals. Traditional rise angle-average frequency analysis methods rely on empirical judgment for boundary line determination, lacking effective automated recognition methods to distinguish between tensile, shear, and mixed crack types. This paper presents an unsupervised crack-type identification model based on ensembled clustering with support vector machine (SVM) and a supervised crack-type classification model using graph neural networks (GNN). The study achieves automatic annotation of four crack signal patterns by ensembling the results of k-means++ and GMMSEQ clustering and combining them with SVM. Subsequently, with labeled data obtained, the study encodes AE signals into graphs and uses a GNN for automatic crack-type classification. The effectiveness of these methods is validated on AE test data from cementitious materials. The findings show that the mutation values of AE event interval functions can accurately pinpoint the material fracture moment, and the AE spectrum’s mid-frequency band is associated with cement–river sand interface damage. Different compositions and loading methods in cementitious materials complicate the association between signals and damage. The ensembled clustering and SVM methods can automatically distinguish different types of cracks and provide quantifiable boundary lines. With horizontal visibility graph (HVG) encoding, the supervised GraphSAGE model accurately identifies various crack types, achieving an average accuracy of up to 97.5% on the test dataset. The proposed AE time–frequency domain characterization method offers new analytical tools for understanding the damage evolution process in cementitious materials, promoting the application of machine learning in the recognition of AE signal patterns.

Method for detecting and locating cracks in aluminum plates based on balanced field electromagnetic technique

Aluminum plates are susceptible to environmental and fatigue loads producing randomly distributed cracks and thus compromising safety performance. This paper proposes a detection and location method based on the balanced field electromagnetic technique (BFET) for the detection of cracks on the upper and backside surfaces. The self-zero nature of BFET and the principle of high-sensitivity crack detection are analyzed. The signal characteristics and variation laws are clarified theoretically. The results show that the amplitude and phase of the upper surface crack signal both vary linearly with the crack depth. The signal amplitude of the backside crack decays exponentially with depth and the phase lags linearly. The two-dimensional amplitude phase diagram is rotated at a fixed angle with increasing crack depth and can locate the crack. BFET can detect cracks below the skin depth and provides a reference to increase the detection penetration depth for other electromagnetic detection methods.

Denoising of laser cladding crack acoustic emission signals based on wavelet thresholding method.

Due to the influence of thermal stress and other factors during laser cladding, cracks will appear in the cladding layer. To reduce cladding loss, an effective method is to detect cracks early and adjust fusion parameters promptly. The paper utilized acoustic signal detection technology to collect crack defect signals during the laser cladding process. Subsequently, the collected acoustic signals were subjected to time-frequency analysis and wavelet thresholding denoising. Then by applying a custom thresholding approach for each level of wavelet coefficients, the crack signals were denoised. The results showed that this method achieved better denoising effects compared to other signal denoising methods and preserved the crack information to a greater extent, confirming the feasibility of the custom thresholding denoising approach. It helps to achieve online detection and acoustic signal analysis of cracks during the cladding process.

Experimental Study on the Identification and Diagnosis of Dynamic Crack Propagation in the Piston Rods of Process Gas Compressors in Underground Gas Storage

Ensuring the reliability of process gas compressors is critical for underground gas storage, as piston rod fractures can lead to serious accidents, such as natural gas leaks or explosions. On-time monitoring and early detection play a vital role in preventing catastrophic consequences, minimising costs, and reducing production losses due to unplanned downtime. This study presents a novel accelerated life-testing method designed to replicate the fracture events of reciprocating compressor piston rods. By accelerating the induced crack initiation and propagation to the final fracture, comprehensive analyses of the fracture results are performed to reveal the piston rod fracture mechanism and the resulting secondary damage to the unit. The research further presents an innovative approach for identifying piston rod crack propagation by means of acoustic emission. Through kinetic analysis and time–frequency domain analysis, the study elucidates two mechanisms responsible for triggering crack signals during the compressor operation: the contact impact between the crosshead pin and the bearing due to the piston rod load reversal, and crack propagation occurring before the maximum tensile load is reached. In addition, the study identifies the piston rod crack expansion signal frequency band and achieves a high-sensitivity identification of crack dynamic growth by extracting signal sub-band features associated with crack propagation. Then, a prediction model of the fatigue crack growth rate was established based on the AE energy release rate, which provides a quantitative assessment of dynamic crack propagation during compression. This method aims to provide a maintenance strategy for piston rod fractures, thereby increasing the operational safety of critical dynamic equipment in underground gas storage.

동아시아 평화의 초석으로서 한일 문화적 사유 고찰

According to the traditional Balance of Power theory, the emergence of the great power results in the emergence of an anti-defeat alliance between the powers. Furthermore, there is an opinion that it is unnecessary along with the necessity of forming an anti-defeat coalition of the overwhelming one-pole system. In addition, some argue that deepening interdependence between countries through trade suppresses war, but realism's position is strong that this interdependence is not a sufficient condition for peace.
 During the Cold War, the two systems of the United States and the Soviet Union were converted to a one-nation system, but the instability of the system was revealed everywhere, and this Russian invasion of Ukraine was the trigger for a new order reorganization as a full-fledged crack signal in the world order that had been maintained since the dissolution of the Soviet Union. The Korean Peninsula is in a position to openly reject China economically and politically due to the geographical conditions sandwiched between China and the Japanese archipelago and the increased proportion of trade since diplomatic relations with the communist Chinese Communist Party began with northern diplomacy. In addition, the U.S. and Japan are demanding that Korea leave the situation behind, press Korea with the logic of its own sentiment, and leave China. This is not only a tension with the United States, but also a forceful and powerless approach to the Chinese satellite state. Specifically, it can be linked to the territorial claim of Hong Kong, Taiwan, and the Ryukyu Kingdom, which was formerly a subordinate state of China.
 At this point, we approached the cultural reality of Korea-Japan relations related to the East Asian Tunnel, focusing on the understanding of Korea-Japan cultural sentiment (情緖) and the Japanese archipelago's favor for Baekje, which reproduced the path of peace between the Korean Peninsula and the Japanese archipelago.

Impact of the cloud zenith angle of the pickup coil upon the sensitivity of a flexible rosette eddy current sensor

Bolt links and riveted joints, although widely used in aerospace and other mechanical systems, are susceptible to stress concentrations and fatigue cracks, which are undesirable. The flexible rosette eddy current sensor is typically used for the structural monitoring of bolt connections and riveted joints. This study proposes a method to improve the sensitivity of a flexible rosette eddy current sensor by reducing the cloud zenith angle of the pickup coil. First, the effect of the cloud zenith angle upon the sensitivity of the flexible rosette eddy current sensor is analyzed theoretically; the results show that a larger cloud zenith angle corresponds to a larger background signal. This diminishes the relative rate of change of the crack signal when the sensitivity of the sensor is evaluated based upon the maximum rate of change of the output. Therefore, the sensitivity is optimized by reducing the cloud zenith angle. Subsequently, through simulation and experimental methods, the relationship between sensor sensitivity and cloud zenith angle was studied. The results indicate that the cloud zenith angle significantly affects the sensitivity and that reducing the cloud zenith angle effectively reduces the background and improves the sensitivity.

Cracks Classification using Acoustic Emission Technique on a Reinforced Concrete Beam

The usage of non-destructive techniques for structure's state assessment is highly desirable because they offer the possibility to monitor continuously and in a non-invasive manner the sign of failures of any structure. Among the several techniques proposed over the years, Acoustic Emission (AE) seems very promising. Such a technique, which applies to various materials, consents to the evaluation of micro-fractures generated during failure by detecting their acoustic emission through sensors located on the surface of the material itself. Concrete beam failure is suitable for such kind of procedure. It has been widely investigated through AE, but despite the many literature contributes, there still is a scarcity of practical applications on real structures, and no consensus on protocols. In this work, an AE technique has been paired with a four-point bending test to try and find a robust and reliable experimental setup for AE application on concrete beams. Results show a proper classification of the occurring cracks, which change according to different zones of the concrete beam and different load values. Axial crack signals exhibit different features concerning shear crack ones. In such a way, the categorization of damages can be performed and crosschecked with the theoretical expectations. All these results suggest that the proposed setup can be used as a possible protocol for practical AE measurements.

The effect of eddy current probe configurations on crack signal magnitude: Consideration of excitation coil direction

The configuration of an eddy current (EC) probe is a critical factor that has consistently drawn the attention of researchers during the development of EC probes. This is because the configuration directly impacts the magnitude and distribution of the ECs generated on the test sample, which, in turn, determines the ability to detect defects in the sample. In this study, we have considered three EC probe configurations with similar excitation and detection coil dimensions that have been oriented pancake and tangential to the test specimen surface. Our primary aim was to evaluate the ECs generated on the test specimen and the output crack signal magnitude. To achieve this, we employed finite element analysis to compare and evaluate the efficiency of EC generation on its surface across the three configurations. We then conducted experiments using the three EC probes to validate the simulation results and evaluate the effectiveness of detecting four artificial cracks on an aluminum plate.

Development of a novel real-time AE source localisation technique using ANN for health monitoring of rail section: an experimental study

Almost all countries are using railways to transport passengers and freight. Due to heavy load and environmental exposure, these rail sections are prone to damage. Thus, structural health monitoring of the rail section is an essential topic of research to ensure safety and predict the remaining useful life of the track. Consequently, the detection and localisation of damages/cracks in rail section are crucial. It also helps in taking corrective action in time. In this context, the acoustic emission (AE) technique is found to be a powerful non-destructive testing technique which has great potential to localise cracks in rail sections. Actually, the initiation or growth of a damage/crack in a structure emits AE waves, which may be captured using AE sensors mounted on different locations of the structure. The damage itself acts as an AE source. Subsequently, in this study, a novel approach is developed to localise the AE source in a rail section using the artificial neural network (ANN). The ANN model is based on the experimental crack signals captured through a single AE sensor by placing it at different regions of the rail section. For each position of the sensor, cracks are simulated using pencil lead break at different locations along the length and depth of the rail section. It is observed that the developed ANN model predicts the location of the AE source with better accuracy in comparison to the existing localisation methods. The minimum error is found to be less than 1%. Therefore, it can be concluded that the developed ANN model is a suitable option for predicting potential crack locations during real-time monitoring of rail sections.

Differential Electromagnetic Acoustic Probes for Quantitative Detection of Pipeline Cracks

The circumferential 0-mode shear horizontal (CSH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) guided waves have great advantages for the detection of cracks in pipelines due to the small attenuation of acoustic signals. However, the CSH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> guided wave detection methods are confused by the signal mixing, low signal-to-noise ratio and poor quantitative accuracy of cracks. In this paper, the novel detection method which includes three circumferential uniformly distributed probes are proposed for quantitative detection of pipeline cracks. The signals received by two receiving probes constitute a couple of differential signals. A full physical field coupled finite element model is developed to study the effect of CSH guided wave-defect interactions. The differential signal enhancement algorithm can separate the crack signal from the overlapped signal, which greatly improves the accuracy of crack location. The characteristic coefficient method is introduced to improve the quantitative accuracy of the crack sizes. The localization and quantification methods are verified through experiments. The results show that the defect localization method proposed in this paper can improve the signal-to-noise ratio of the crack signal from -15 dB to 2.4 dB. The localization error of crack is less than 6.75%. The quantitative error of the crack sizes is less than 8.75% based on the characteristic coefficient method.

Processing methods of the pipeline crack detection signal by a balanced field electromagnetic technique based on phase characteristics

The balanced field electromagnetic technique is an effective way of in-line inspection to detect cracks in pipelines. A signal demodulation method based on phase characteristics is proposed for the problem of interference signals generated by the sensor tilt shaking during the detection, which affects the judgment of the cracks. The method uses a reference signal whose phase is orthogonal to the signal generated by the sensor shaking to demodulate the detection signal to eliminate the shake interference. The generation principles of crack detection signals and interference signals generated by sensor shaking are analyzed, and the influence of sensor lift-off on detection is compared. A demodulation model is established based on the characteristic of that same frequency and different phases of crack and shake signals. The feasible conditions of the method are analyzed by simulation, and the phase value of the reference signal in the demodulation method is determined. The platform detection experiment and pulling tests at different speeds are carried out, respectively, to verify the effectiveness of the proposed method. The results show that there is a significant phase difference between the signals generated by the sensor shaking and the crack. For carbon steel pipelines, the signal phase of different shake angles is −4°. When the sensor structure and excitation frequency in this study are used, the reference signal phase is chosen to be 86°. The method preserves the detection signal characteristics before processing and enables the linear output responses to be obtained for different depths of cracks.

Automatic identification and location method for shaft-hole interference fit crack based on deep learning

Cracking on the shaft of the interference fit joint surface is a common failure form for the shaft-hole interference fit. Because the crack is small and is located at the shaft edge, the crack signal is affected by echo and reflection signals and noise when traditional ultrasonic guided wave methods are used to detect the crack. When the crack is small-sized, the crack signal may even be submerged completely in the echo and reflection signals. Therefore, the traditional detection method is unable to obtain accurate information about the crack locations easily and thus cannot evaluate the overall safety performance of the interference fit accurately. To solve these problems, this paper presents an on-axis crack monitoring system for interference fit joints using laser ultrasound and an improved YOLOv5 network that integrates early warning, detection, positioning, and analysis functions. The system detects axial cracks at the shaft-hole interface via laser ultrasonic guided waves on the workpiece, and captures the crack information by deep learning; then, the captured crack information is analyzed to extract the essential crack location information. Finally, the method is verified experimentally. The experimental results show that this method can not only identify axial cracks at the shaft-hole interference fit surface automatically, but also can locate these cracks accurately and obtain both the number of cracks and the distance between pixels.

Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

On the condition of variable speeds, the blade crack signal generated by the single-mode sensor is an inherent risk of being misled by noise. Besides, it is challenging to reach the required integrity of information through feature extraction in single analysis domain, leading to the loss of detection accuracy for blade cracks. Therefore, a blade crack detection method based on domain adaptation and autoencoder (AE) of multidimensional vibro-acoustic feature fusion is proposed to extract more complete fault information from multi-sensor and multi-analysis domains. This method extracts one-dimensional (1D) and two-dimensional (2D) features from the frequency domain and smoothed pseudo-wigner-ville distribution (SPWVD) spectrum of the vibro-acoustic signal. Firstly, the 1D and 2D features are extracted without supervision through Stacked Denoising Autoencoder (SDAE) and Convolution Denoising Autoencoder (CDAE), respectively. Then, the extracted multidimensional features are fused and classified through the fully connected network. Finally, for insufficient diagnosis accuracy and data imbalance under certain working condition models with variable speed and combined with inductive transfer mechanism and domain adaptation, the stable diagnosis model under specific speed is applied for transferring and learning the unsupervised networks of other models compensating the weak generalization ability and robustness of the model. The experimental results show that this method is characterized by higher accuracy and robustness compared with the diagnosis method of single-domain feature for the single-mode sensor. Moreover, the proposed migration mechanism can well solve the data imbalance under different working conditions and improve the generalization ability of the few-shot model.

Influence of curvature radius on the axial crack signal of the magnetic flux leakage detection for tapered roller bearing rings

Tapered roller bearings are widely used in heavy machinery, railway transportation, aviation, and other fields. Their quality and reliability are related to the operational safety of mechanical equipment. In the axial crack magnetic flux leakage (MFL) detection of the bearing ring, the MFL signals obtained by the sensor from different curvature radius of the surface are inconsistent, affecting the detection accuracy of cracks and subsequent quantitative analysis. In order to address the above problems, the finite element simulation is performed to analyze the influence of the surface curvature radius of the bearing ring on the magnetic field distribution inside the workpiece and the MFL signal in the circumferential magnetization. Through the parallel magnetic circuit, the difference in curvature radius is identified as the basic reason for non-uniform magnetization. On this basis, the compensation method based on the normalization of surface magnetization is proposed. Furthermore, the effectiveness of the compensation method is verified by experiments. The relative change in the amplitude of the crack MFL signal is reduced from 30% to 5%.

A Crack Detection Method for Pipelines Using Wavelet-Based Decision-Level Data Fusion

A novel crack detection method using wavelet-based decision-level data fusion is proposed and verified by simulations and experiments. In this work, we established a crack signal model using wavelet functions that fits all crack signal scales without need of windowing, and showed that the signal-to-noise-ratio (SNR) difference between the independent signals to be fused has a significant effect on the overall detection performance. Based on this observation, we designed four wavelet-based decision-level data fusion rules. We then presented a detection method where wavelet processing results of individual NDT input are accepted or rejected based on said rules to produce optimized estimation accuracy. To evaluate the proposed method, the first group of simulations were implemented to show the proposed method identifies the inner and outer-surface cracks with good estimation accuracy; the second and third groups of experiments showed the proposed method does improve upon individual detection methods alone, and has better detection performance than three state-of-the-art methods; finally, the pull-rig experiments verified on our own pipeline inspection gauge (PIG) that the proposed method does improve the detection probability beyond that of individual detection methods, in an actual high-speed pipeline inline inspection (ILI).