Ultrasonic Echo Signals Research Articles

Enhancing Ultrasonic Echo Response of AlN Thin Film Transducer Deposited by RF Magnetron Sputtering.

Accurate measurement of the pretightening stress for bolts has great significance for improving the assembly quality and safety, especially in severe environments. In this study, AlN thin film transducers were deposited on GH4169 nickel base alloy bolts using the RF magnetron sputtering, enabling a systematic investigation into the correlation between structures and the intensity of ultrasonic echo signals. Employing the finite element method resulted in consistency with the experimental data, enabling further exploration of the enhancement mechanism. With the increasing thickness of both the piezoelectric layer and the electrode layer, the intensity of the ultrasonic echo signals saw a great enhancement. The maximum-intensity observed increase is 14.7 times greater than that of the thinnest layers. Specifically, the thicker piezoelectric layer improves its mechanical displacement, while the increased thickness of the electrode layer contributes to better densification. An electrode diameter of nearly 4 mm is optimal for an AlN thin film transducer of M8 bolts. For pretightening the stress measurement, the sample with a strong and stable echo signal shows a low measurement error of pretightening below ±2.50%.

Research on ultrasonic echo signal denoising via integration of adaptive variational mode decomposition algorithm and convolutional neural network

Research on ultrasonic echo signal denoising via integration of adaptive variational mode decomposition algorithm and convolutional neural network

Intelligent identification method for pipeline ultrasonic internal inspection

ABSTRACT In pipeline ultrasonic testing, the accurate identification of defect types is a prerequisite for quantifying defect sizes. To this end, this paper proposes a defect identification algorithm based on the combination of Wavelet Packet Transform (WPT), Distance Evaluation Technique (DET) and GA-SVM to precisely identify defect boundaries. First, the ultrasonic echo signals are processed using WPT to more accurately extract defect information. Then, DET is employed to eliminate redundant feature parameters, which are sorted by sensitivity to determine the input feature parameters for the GA-SVM. On this basis, a self-developed ultrasonic testing experimental system is used to collect ultrasonic echo signals from Q235 steel pipes with cylindrical, spherical, and conical defects at different spatial orientations of the probe and the pipe. After processing and identifying the signals, it was found that the proposed algorithm achieves an average identification accuracy of 99% for different types of defects, thereby realising automatic identification of pipeline defects. The results provide new ideas and methods for the intelligent identification of volumetric defect types in pipeline ultrasonic internal testing and promote the development of industrial-grade ultrasonic internal detectors.

Estimation of ultrasonic flight time based on SHADE algorithm and extended kalman filtering

Abstract Traditional methods of using ultrasound to measure oxygen concentration have the issue of inaccuracy and susceptibility to noise interference when measuring the time of flight (TOF) of ultrasonic echo signals. A proposed algorithm for ultrasonic parameter estimation, utilizing an asymmetric Gaussian model, aims to enhance the precision and reliability of TOF estimation, aiming to reduce the influence of noise interference. The algorithm combines an improved Self-Adaptive Differential Evolution (SHADE) algorithm based on successful historical records with an Extended Kalman Filter (EKF). The improved SHADE algorithm controls the degree of mutation strategy greediness during the mutation operation based on the number of iterations, thereby enhancing the algorithm’s iteration speed. Then, the obtained solution is used as the initial value for the EKF to estimate the model parameters, thus addressing the issue of EKF’s dependency on accurate initial values to obtain precise outputs. Through simulation and experimental measurements of ultrasonic oxygen concentration, several commonly used parameter estimation algorithms are compared with the proposed SHADE-EKF algorithm. Results demonstrate that the SHADE-EKF algorithm exhibits superior performance in estimating the TOF of ultrasonic waves.

Identification method of circumferential declination based on amplitude reduction of pipeline ultrasonic internal inspection signals

ABSTRACT The echo signal of ultrasonic internal inspection is highly sensitive to the incident deflection angle between the probe and the measured surface of the pipeline, which is the key factor affecting the accuracy of ultrasonic inspection. In practical engineering, the echo signal of ultrasonic inspection is easy to be interfered by the external environment, which makes it difficult to identify the deflection angle. The data of defect echo signal under different circumferential declination were collected by the pipeline ultrasonic internal inspection system, and the characteristics of signal variation in time domain under different circumferential declination were analysed. The results show that the existence of circumferential declination reduced the absolute value of the overall amplitude of ultrasonic echo signal. A signal processing method based on amplitude reduction is proposed to reduce the sampling random error. Comparative analysis shows that the circumferential declination can be identified based on amplitude reduction method, and the accuracy of ultrasonic recognition can be improved. By extracting the characteristic value of waveform, the empirical formula is obtained. Within 2°, the maximum deviation is 0.137°. The research results have certain guiding significance for the identification of circumferential declination in practical engineering.

Characteristic parameters measurement and flow prediction of slug flow based on ultrasound technology

Slug flow exists over a wide range of gas and liquid flow rates. For the case of smaller liquid volume fraction, this paper proposes a method for accurately identifying the flow structure of horizontal pipe slug flow based on the ultrasonic inner wall echo signal at the top of the pipe. The arrival and departure time of the slug is determined by analyzing the amplitude of the inner wall echo signal from both the test signal and reference signal and the rate of change of the test signal, respectively. Based on the accurate flow structure identification results, characteristic parameters can be calculated including the length ratio between the liquid film zone and the slug unit (lf/lu) and the average liquid film thickness in the liquid film zone (hf). The system of equations describing the relationship between the two parameters and the two-phase flow rates is constructed. By solving it, the flow rates of gas and liquid can be predicted. Experiments are carried out with the superficial velocity of gas and liquid ranging from 0.8 to 2.5 m/s and 0.1 to 0.4 m/s, respectively, where the pipeline’s inner diameter is 50 mm. The experimental results indicate that the mean absolute percentage errors for the predicted gas and liquid flow rates are 12.9605 % and 6.3517 %, respectively.

Feature extraction method for ultrasonic pipeline defects based on fractional-order VMD

ABSTRACT Ensuring the safety of pipeline transportation is vital for societal well-being. Traditional methods for identifying defects in steel pipelines through ultrasonic echo signal pattern recognition often fail to extract comprehensive and effective features crucial for accurate defect detection. This study introduces an innovative feature extraction method employing a fractional Fourier transform variational modal decomposition (FRFT-VMD), hereafter referred to as the fractional-order VMD algorithm. This method utilises the fourth-order central moment and envelope entropy to optimise several key parameters: the fractional order in the Fourier transform, the number of decomposition layers, and the penalty factor in variational modal decomposition. To evaluate the effectiveness of the proposed method, ultrasonic echo signals from both finite element simulations and experimental platforms were analysed using the FRFT-VMD technique. The features extracted were then classified using a Least Squares Support Vector Machine (LSSVM) to determine defect depths. The results show a recognition accuracy of 95.2% in simulated signals and 89.1% in experimentally measured signals across various defect depths, indicating a significant improvement over existing feature extraction methodologies. The fractional-order VMD algorithm proves to be superior in extracting features that enhance the accuracy of defect identification in steel pipelines.

Analysis and denoising of electric noise in phased array ultrasonic testing system

Phased array ultrasonic testing (PAUT) is an advanced technique used for non-destructive testing (NDT) to detect and assess the integrity of materials or structures. In composite materials with significant acoustic attenuation, electrical noise significantly impacts PAUT detection accuracy, highlighting a crucial issue. This paper presents a comprehensive analysis of electrical noise, explains the relationship between electrical noise and inspection parameters, and proposes recommendations to reduce it. A time-varying filter empirical mode decomposition (TVF-EMD) joint wavelet thresholding method is proposed for denoising electrical noise after time-corrected gain (TCG). The ultrasonic echo signal goes through TVF-EMD decomposition, resulting in intrinsic mode functions (IMFs). Energy entropy is utilized to identify the signal-dominant IMFs, and wavelet thresholding is applied to further reduce noise within these selected IMFs. The proposed method effectively removes noise and ensures signal integrity by validating simulated and experimental signals.

Comparative study of linear and nonlinear ultrasound applied to the detection of hydrogen damage in 7N01 aluminum alloy

7N01 aluminum alloy samples with different hydrogen damage degrees were prepared by electrochemical hydrogen charging technology. 7N01 aluminum alloy samples with different degrees of hydrogen damage were characterized by metallographic observation, hardness test and XRD test. The results show that the hydrogen content increases with the increase of hydrogen charging time. The surface of aluminum alloy is exfoliated and pits appear. The more severe the hydrogen damage, the greater the depth of pits. The microhardness of the 7N01 aluminum alloy decreases after hydrogen damage, which only occurs near the surface. After electrochemical hydrogen charging, AlH3 appears in the structure of 7N01 aluminum alloy, which is the result of increased hydrogen concentration. The ultrasonic echo signals of hydrogen damaged samples were obtained by a high frequency longitudinal probe ultrasonic detection device, and the results of linear and nonlinear ultrasonic detection were compared. Traditional linear ultrasonic detection parameters such as sound velocity and attenuation coefficient do not change significantly in the early stage of hydrogen damage, but increase significantly in the late stage of hydrogen damage. Due to the change of microstructure, the nonlinear coefficient increases approximately linearly in the early stage of hydrogen damage and decreases in the late stage of hydrogen damage. This study demonstrates the potential for combining linear and nonlinear ultrasonic measurements in hydrogen environment to more comprehensively study hydrogen damage.

Bolt load looseness detection for slip-critical blind bolt based on wavelet analysis and deep learning

Bolt preload is a crucial factor in high-strength bolt applications, particularly for the Slip-critical blind bolts (SCBBs). This study presents a novel nondestructive approach that leverages ultrasonic echo waves to accurately detect the state of bolt looseness, addressing a significant research gap in the field of blind bolt looseness detection. The proposed technique is uniquely suitable for blind bolted connections as it only necessitates access to a single side of the connection. Nine types of SCBBs were tested and obtained approximately 4000 ultrasonic echo signals with varying degrees of looseness. These signals have been transformed into image-based representations using wavelet analysis, and deep learning techniques were used to classify and predict the looseness level accurately. The performance of various damage assessment criteria, CNN models, and dataset sizes were evaluated and compared. The proposed method was validated by classifying 400 datasets with a validated accuracy of 97.30%.

Noninvasive Monitoring of Tissue Temperature Changes Induced by Focused Ultrasound Exposure using Sparse Expression of Ultrasonic Radio Frequency Echo Signals.

Noninvasive therapies such as focused ultrasound were developed to be used for cancer therapies, vessel bleeding, and drug delivery. The main purpose of focused ultrasound therapy is to affect regions of interest (ROI) of tissues without any injuries to surrounding tissues. In this regard, an appropriate monitoring method is required to control the treatment. This study is aimed to develop a noninvasive monitoring technique of focused ultrasound (US) treatment using sparse representation of US radio frequency (RF) echo signals. To this end, reasonable results in temperature change estimation in the tissue under focused US radiation were obtained by utilizing algorithms related to sparse optimization as orthogonal matching pursuit (OMP) and accompanying Shannon's entropy. Consequently, ex vivo tissue experimental tests yielded two datasets, including low-intensity focused US (LIFU) and high-intensity focused US (HIFU) data. The proposed processing method analyzed the ultrasonic RF echo signal and expressed it as a sparse signal and calculated the entropy of each frame. The results indicated that the suggested approach could noninvasively estimate temperature changes between 37°C and 47°C during LIFU therapy. In addition, it represented temperature changes during HIFU ablation at various powers, ranging from 10 to 130 W. The normalized mean square error of the proposed method is 0.28, approximately 2.15 on previous related methods. These results demonstrated that this novel proposed approach, including the combination of sparsity and Shanoon's entropy, is more feasible and effective in temperature change estimation than its predecessors.

Time Delay Study of Ultrasonic Gas Flowmeters Based on VMD-Hilbert Spectrum and Cross-Correlation.

The accuracy of ultrasonic flowmeter time delay measurement is directly affected by the processing method of the ultrasonic echo signal. This paper proposes a method for estimating the time delay of the ultrasonic gas flowmeter based on the Variational Mode Decomposition (VMD)-Hilbert Spectrum and Cross-Correlation (CC). The method improves the accuracy of the ultrasonic gas flowmeter by enhancing the quality of the echo signal. To denoise forward and reverse ultrasonic echo signals collected at various wind speeds, a Butterworth filter is initially used. The ultrasonic echo signals are then analyzed by Empirical Mode De-composition (EMD) and VMD analysis to obtain the Intrinsic Mode Function (IMF) containing distinct center frequencies, respectively. The Hilbert spectrum time-frequency diagram is used to evaluate the results of the VMD and EMD decompositions. It is found that the IMF decomposed by VMD has a better filtering performance and better anti-interference performance. Therefore, the IMF with a better effect is selected for signal reconstruction. The ultrasonic time delay is then calculated using the Cross-Correlation algorithm. The self-developed ultrasonic gas flowmeter was tested on the experimental platform of the gas flow standard devices using this signal processing method. The results show a maximum indication error of 0.84% within the flow range of 60-606 m3/h, with a repeatability of no more than 0.29%. These results meet the 1-level accuracy requirements as outlined in the national ultrasonic flowmeters calibration regulation JJG1030-2007.

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Real-time monitoring of wellbore status information can effectively ensure the structural safety of the wellbore and improve the drilling efficiency. It is especially important to recognize the wellbore fractures and identify their parameters, which motivates us to propose a wellbore fracture recognition and parameter identification method using piezoelectric ultrasonic and machine learning. To realize a self-model emission detection, we innovatively utilize a single transducer to act as both an actuator and a sensor, allowing for the efficient acquisition of ultrasonic echo signals of the wellbore. For fracture recognition, we use the wavelet packet transform to extract features from the ultrasonic echo signal, while constructing a convolutional neural network model for fracture recognition. Then, we establish the relationships between the fracture width-depth parameter and the echo signal, including the peak value as well as the arrival time difference. The experimental results show that the proposed method effectively recognizes the fractures from the ultrasonic echo signal of the wellbore. At the same time, the established function truly reflects the relationship between the fracture parameters and the echo signal. Therefore, the proposed method can provide an identification function for quantitative monitoring of wellbore fracture parameters. Moreover, the functions can be used as a reference for other structural health monitoring, which has good application prospects.

Ultrasound signal processing based on joint GWO-VMD wavelet threshold functions

To overcome the problem of noise interference in the ultrasonic echo signal received by the probe in ultrasonic nondestructive testing, a method of ultrasonic signal processing using the jointed wavelet threshold function of GWO-VMD (Grey Wolf Optimisation, Variational Mode Decomposition) is proposed. The method first uses the GWO algorithm to optimise the VMD parameters to find the optimal parameter combination penalty factor α and decomposition modulus number K, and then uses the correlation coefficient method to differentiate between the desired signal and noise components after decomposition to obtain the intrinsic mode function (IMF) components. Secondly, the noise components are processed by combining the improved wavelet threshold function algorithm, and lastly, each modal component is restored to produce a denoised signal. Experimental results show that the method improves the signal-to-noise ratio (SNR) by 2.55%, reduces the root mean square error (RMSE) by 3.8%, preserves more useful information, and is more effective in denoising.

Material Classification based on Non-contact Ultrasonic Echo Signal Using Deep Learning Approach

Non-contact ultrasonic sensors are generally used for range measurement and object detection. In addition to the shape and size of the target object, the identification of the material types plays a vital role in robotic navigation and autonomous vehicle applications. Ultrasonic echo signals contain a significant amount of information and can be used to recognize and categorize different materials. Echo signals can help robots detect the obstacle's material on the path and comply with its behavior accordingly. The emergence of deep neural networks has shown great promise, offering cutting-edge performance for a wide range of signal-processing tasks. This work uses the non-contact ultrasonic echo signal from a set of materials (glass, wood, metal plate, sponge, and cloth) for classification. The main idea is to classify the materials using the embedded information of reflected echo signals. Hilbert transform is used to get the envelope from the raw echo ultrasonic signal. In this paper, a novel architecture for one-dimensional convolutional neural network (1D-CNN) has been proposed to accomplish the classification task. The CNN model takes raw echo signals as input to detect and classify materials accurately. The performance of the classifier model is evaluated using accuracy, precision, recall, and F1-score. The proposed 1D-CNN deep learning-based multi-class classifier model can classify the different types of materials with an accuracy of 96%, precision of 95%, recall of 95%, and F1-score of 95%.

Simulation Study on the Performance of Ultrasonic Pulse-Echo Detector for Ice Thickness Identification

In-flight icing detectors are important for the flight safety of aircraft. Detectors based on ultrasonic pulse-echo can be used for detecting ice thickness. To study the influencing factors of ultrasonic pulse-echo detection performance, a simulation model of elastic wave-piezoelectric coupling was established, which was used to analyze the influence of different types of piezoelectric ceramics, matching layers with different acoustic impedances, and different substrate materials on ultrasonic pulse-echo signals. It was found that when aluminum is used as the substrate material, the ultrasonic echo signal has a higher signal-to-noise ratio. Furthermore, the influence of aluminum substrate materials with different thicknesses on ultrasonic pulse-echo was analyzed. The ice thickness can be identified by measuring the time of flight between the aluminum-ice interface echo and the ice-air interface echo. The results indicate that when the thickness of the aluminum substrate is 25mm, the upper detection limit of ice layer thickness can reach about 10mm. Therefore, the detection upper limit of ice layer thickness can be extended by appropriately increasing the thickness of the aluminum substrate material.

Ultrasonic scattering model and identification experiment of polycrystalline materials

Ultrasonic identification has an important application value for national defense, military affairs, aerospace, nuclear facilities and other high-tech fields. Ultrasonic waves can be used to identify any metal material. At present, the researches focus on algorithms for identifying the difference in ultrasonic signal among materials, but the study on the corresponding identification theory is lacking. In this work, 10 primary models of the microstructure of 2A12 aluminum alloy are established as analogies to the complex microstructures of polycrystalline metallic materials. The grains of these models are different from each other in size, separation distance, shape, arrangement directions and orders. The time-domain ultrasonic echo signals of different microstructures are calculated by making use of the finite element method. The grass-like signals between two echoes are ultrasonic backscattering signals, which are sensitive to any change of microstructure. The backscattering signals between the primary echo and the secondary echo in the ultrasonic echo time domain signals are extracted as ultrasonic fingerprints. The feature difference <i>Q</i> is defined to quantify the difference in ultrasonic fingerprint of each sample. The results show that the slight variation in microstructure will lead to difference in ultrasonic signal, and the difference caused by the variation in grain size is more distinct. And then, an ultrasonic identification algorithm is proposed, and the identification experiments are conducted on four 2A12 aluminum alloy samples with the same shape. The identification results show that the target sample can be accurately identified by using ultrasonic fingerprints and the ultrasonic fingerprints of the target sample are distinctly different from those of the other samples. The microstructure morphologies of the samples are examined by using scanning electron microscopy (SEM). The SEM results show that there are significant differences in grain size, separation distance and densification between samples although they are the same material. The features of the microstructure in the proposed ultrasonic scattering model in this work are confirmed by the actual y micromorphologies observed in the SEM images. The identification experiments and SEM results demonstrate that the established ultrasonic scattering model is effective. This work can provide a reference for theoretically studying ultrasonic identification and present an idea for developing some new identification algorithms in future.

Development of a High‐Frequency Ophthalmic Real‐Time Ultrasound Imaging System Based on a 20 MHz Annular‐Array Transducer

Objective. Ophthalmic ultrasound imaging is a widely used diagnostic method for examining ophthalmic diseases in the clinic. A single‐element transducer is adopted in the traditional ophthalmic ultrasound imaging systems. Although full depth focusing can be achieved using a linear‐array transducer, it is unsuitable for ophthalmic imaging due to its inability to be tightly coupled to the eyeball and its higher cost. Annular‐array‐based systems provide an alternative, striking a balance between image quality and cost. Methods. Here, we present a newly developed high‐frequency ophthalmic real‐time ultrasound imaging system based on an annular‐array transducer. This system uses a custom‐made five‐element, 20 MHz annular‐array transducer encapsulated in a stainless steel housing and mounted in a commercially available handheld mechanical probe designed specifically for clinical ophthalmic imaging. The system uses a printed circuit board scheme and FPGA as the core to complete the hardware system design and realize the ultrasonic echo signal processing and system timing management. Full depth dynamic focusing of each scanning beam was achieved by designing the transmit and receive beamforming. Combined with advanced integrated circuits, the miniaturization and low cost of the overall system are realized. Results. Extensive tests, including hardware, wire phantom, and tissue mimicking phantom measurements, were conducted to demonstrate good performance of the system. The results showed that the designed system can effectively improve the imaging resolution and enhance the depth of field of the image, particularly reducing the blind area of the near‐field. Conclusion. The high‐frequency ophthalmic real‐time ultrasound imaging system based on an annular‐array transducer meets the needs of clinical diagnosis and has a good clinical application prospect.

Use of backscattering ultrasound parameters for iron ore varieties recognition

Purpose. Development of the method for recognizing the main mineral-technological varieties of iron ore in the deposits being developed by selecting an analytical model for the spectral characteristics of the received ultrasonic echo signals and quantitative assessment of their parameters. Methodology. The work uses methods for modeling the processes of propagation of ultrasonic waves in a randomly heterogeneous medium. The process of backscattering of ultrasound in mineral structures formed by inclusions of iron ore of various varieties and associated rock was considered. The estimated parameters of the spectral characteristics of the inversely scattered probing ultrasound pulse were studied. Findings. A method for recognizing the main mineral and technological varieties of iron ore of the deposit being developed, based on the parameters of the propagation of ultrasonic waves in the studied samples, was proposed. This is achieved by selecting an analytical model for the spectral characteristics of the received echo signals and quantifying their parameters. The amplitude of the echo signal and its spectral properties depends on the size and concentration of the scatterers, i.e., the structural and textural features of the iron ore sample under study. Taking into account these factors, the extracted parameters of the model were used to identify the main mineralogical and technological varieties of iron ore of the studied deposit. Originality. The proposed method for recognizing mineral-technological varieties of iron ore differs from the known ones in that the amplitude, central frequency, and bandwidth of the amplitude spectrum of the Gaussian parametric model of the measured echo signals are used as evaluation parameters. Practical value. The proposed scientific and technical solution allows for operational non-destructive control of the main mineralogical and technological types of iron ore in the process of its extraction and processing.

Ultrasonic Thickness Measurement Method and System Implementation Based on Sampling Reconstruction and Phase Feature Extraction.

The existing ultrasonic thickness measurement systems require high sampling frequencies for echo signal acquisition, leading to complex circuit designs and high costs. Moreover, extracting the characteristics of ultrasonic echo signals for accurate thickness measurement poses significant challenges. To address these issues, this paper proposes a method that utilizes conventional sampling frequencies to acquire high-frequency ultrasonic echo signals, overcoming the limitations of high-frequency data acquisition imposed by the Nyquist-Shannon sampling theorem. By employing an improved sampling reconstruction technique, the multi-cycle sampling signals are reconstructed and rearranged within a single cycle, effectively increasing the equivalent sampling frequency. Additionally, a combination of coarse estimation using fast Fourier transform (FFT) and precise phase extraction using the moving sine fitting algorithm is proposed for accurate thickness measurement, resolving the limitations of common thickness measurement methods such as peak detection, envelope detection, and Hilbert autocorrelation in terms of low measurement accuracy. Experimental results obtained from thickness measurements on 45 steel ultrasonic test blocks within the range of 3 mm to 20 mm indicate a measurement error of ±0.01 mm, while for thicknesses ranging from 1 mm to 50 mm, the measurement error is ±0.05 mm.