Ultrasonic Echo Research Articles

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.

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.

Обследование скрытых участков стальных труб малого диаметра с применением ультразвуковой дефектоскопии

According to the current regulatory documents, as the established service life of gas pipelines for various purposes expires, they must be inspected in order to assess the current technical condition, make a decision on the residual life and the possibility of further operation at hazardous production facilities, as well as other buildings and structures. The possibility is considered related to using ultrasonic testing for detecting damage of corrosion origin in the sections of such pipelines hidden from the direct observation in interfloor ceilings and passages through the walls of buildings at the facilities in various branches of industries, as well as in gas distribution and gas consumption systems, and water pipelines. In addition, the possibility of identifying layered deposits of various origins in the cavity of small-diameter steel pipes is considered. At energy facilities, as well as in water supply pipelines, such deposits can lead to a significant reduction in the flow area of the pipeline, up to its complete blocking. Deposits inside the pipelines form an anisotropic layered medium, and the passage of ultrasonic waves through it has a number of features that prevent the use of traditional ultrasonic echo testing. For small-diameter pipes, the criterion is given under which it is possible to perform «remote» (waveguide) ultrasonic testing of the hidden sections of pipes at a distance of up to 600 mm. It is illustrated how, from such a distance, it is possible to detect corrosion deposits in pipes by ultrasonic testing methods using phased array antennas. At the same time, it is possible to assess the thickness («power») of layered deposits and their structure even when the passage section of the pipeline is filled with such deposits up to 100 %. Use of the methods presented in the article allows to increase the accuracy of calculating the residual life of the pipeline and reduce the uncertainty in setting safety factors in such calculations.

Application of Iterative Elastic SH Reverse Time Migration to Synthetic Ultrasonic Echo Data

The ultrasonic echo technique is widely used in non-destructive testing (NDT) of concrete objects for thickness measurements, geometry determinations and localization of built-in components. To improve ultrasonic imaging of complex concrete structures, we transferred a seismic imaging technique, the Reverse Time Migration (RTM), to NDT in civil engineering. RTM, in contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, considers all wavefield types and thus, can handle complex wave propagations in any direction with no limit on velocity variations and reflector dip. In this paper, we focused on the development, application and evaluation of a two-dimensional elastic RTM algorithm considering horizontally polarized shear (SH) waves only. We applied the elastic SH RTM routine to synthetic ultrasonic echo SH-wave data generated with a concrete model incorporating several steps and circular cavities. As these features can often be found in real-world NDT use cases, their imaging is extremely important. By using elastic SH RTM, we were able to clearly reproduce almost all reflectors inside the concrete model including the vertical step edges and the cross sections of the cavities. We were also capable to show that more features could be mapped compared to SAFT, and that imaging of complex reflectors could be sharpened compared to elastic P-SV (compressional-vertically polarized shear) RTM. Our promising results illustrate that elastic SH RTM has the potential to significantly enhance the reconstruction of challenging concrete structures, representing an important step forward for precise, high-quality ultrasonic NDT in civil engineering.

Estimation of Impact Strength of Kevlar/Basalt and Kevlar/Glass Interwoven Composite Laminate after High-Velocity Bullet Impact

The application of composite materials has increased so drastically in the aerospace industry. The Impact strength signifies the importance of composite materials when exposed to suddenly applied loads. This paper is focused on describing the behavior of interwoven kevlar/glass-epoxy and kevlar/basalt-epoxy composite laminate under high-velocity bullet impact. The composite lamina of kevlar/glass and that of kevlar/basalt are prepared using three different weaving techniques. The composite laminates are prepared using the compression moulding technique. The laminates have been subjected to high-velocity bullet impact. The velocity range is from 220 m/s to 260 m/s. The impact damage area in the laminate has been assessed through ultrasonic pulse echo submerged nondestructive technique. The impact strength has been calculated using the damaged area derived using the impact energy absorbed by the laminate. The results have shown that the maximum impact which found out to be kevlar/basalt (KB 1 × 1) is 28.24 J/cm2.

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.

Pipeline Internal Surface Detection based on Convolutional Neural Networks and Code Wave Time-Frequency Transform

This study introduces an innovative approach that integrates principles of coda wave interferometry, wavelet transform, convolutional neural networks, and deep learning for the analysis and detection of contaminants within pipelines. The primary objective of this method is to identify various types of contaminants, enhance contamination detection sensitivity and accuracy, thereby providing an effective solution for monitoring and remediation in the oil and gas pipeline transportation industry. The findings of this research offer valuable insights and serve as a significant reference for improving pipeline cleaning practices. Further optimization can be explored through enhancements in the model architecture, the incorporation of data augmentation techniques, and the exploration of alternative training strategies. However, the performance of this method still requires further study and improvement. The study encompasses the following key aspects: Firstly, ultrasonic analysis is employed to characterize the structural properties of contaminants, enabling the extraction of crucial structural features and performance parameters through the analysis of ultrasonic echo signals. Secondly, wavelet transform is applied to conduct comprehensive time-frequency analysis of the ultrasonic echo signals, facilitating the capture of distinctive features associated with contaminants across different frequency bands and time scales. Thirdly, convolutional neural networks, specifically leveraging the VGG-16 model, are used to extract informative features from the ultrasonic signals and perform classification. Through rigorous training and fine-tuning processes, the model achieves precise identification of contaminant types and estimation of their thickness.

Playing the pipes: acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation.

Objective: In emergency medicine, airway management is a core skill that includes endotracheal intubation (ETI), a common technique that can result in ineffective ventilation and laryngotracheal injury if executed incorrectly. We present a method for automatically generating performance feedback during ETI simulator training, potentially augmenting training outcomes on robotic simulators. Method: Electret microphones recorded ultrasonic echoes pulsed through the complex geometry of a simulated airway during ETI performed on a full-size patient simulator. As the endotracheal tube is inserted deeper and the cuff is inflated, the resulting changes in geometry are reflected in the recorded signal. We trained machine learning models to classify 240 intubations distributed equally between six conditions: three insertion depths and two cuff inflation states. The best performing models were cross validated in a leave-one-subject-out scheme. Results: Best performance was achieved by transfer learning with a convolutional neural network pre-trained for sound classification, reaching global accuracy above 98% on 1-second-long audio test samples. A support vector machine trained on different features achieved a median accuracy of 85% on the full label set and 97% on a reduced label set of tube depth only. Significance: This proof-of-concept study demonstrates a method of measuring qualitative performance criteria during simulated ETI in a relatively simple way that does not damage ecological validity of the simulated anatomy. As traditional sonar is hampered by geometrical complexity compounded by the introduced equipment in ETI, the accuracy of machine learning methods in this confined design space enables application in other invasive procedures. By enabling better interaction between the human user and the robotic simulator, this approach could improve training experiences and outcomes in medical simulation for ETI as well as many other invasive clinical procedures.

Ultrasonic measurement of roller revolution speed for high-speed rolling bearing

The sliding state of the bearing rollers can be identified by measuring the roller revolution speed. At present, the roller revolution speed is mainly approximated by the average speed of the cage, which can be obtained by optical and electric methods. However, there are some bottlenecks in the practical application of these methods. In this study, a non-destructive ultrasonic technique is proposed to measure the high-speed rolling bearings' roller revolution speed. In order to reduce the impact of time-domain noise on the ultrasonic echo pulse signal, autocorrelation analysis and the FFT transform are introduced. The passing frequency of a roller can be more accurately calculated by using the CZT (linear frequency modulation Z transform) spectrum thinning algorithm. This ultrasonic approach's validity is confirmed when compared to the conventional optical method at low speed. Then, in accordance with the theoretical models, the roller slippage at high speed is observed by directly measuring the speed of the roller revolution.

Ultrasonic visualization and quantitative analysis of internal defects in RTV coatings

Room temperature vulcanised (RTV) silicone rubber coatings effectively enhance the insulation properties of electrical equipment. However, RTV coatings are prone to internal defects caused by the coating process and the effects of aging during service, which can lead to debonding of the coatings. Internal debonding defects are challenging to detect and can ultimately lead to accidents due to a reduction in the insulation capacity of the equipment. To visualize the internal defect morphology of RTV coatings and quantify the defect size, an ultrasonic pulse-echo-based method for detecting and imaging debonding defects is proposed. The method involves the development of a finite element model to investigate how ultrasonic waves propagate in RTV coatings and the influence of ultrasonic probes and inspection conditions on defect echoes. Furthermore, an ultrasonic detection system specifically designed for RTV coating debonding defects is constructed. This system utilizes wavelet packets in the time-frequency domain to analyze the echo signals in both normal and defective regions. The three-dimensional reconstruction of the debonding defect morphology is accomplished by integrating ultrasonic echo amplitude and position information. Finally, the size of the debonding defects is quantified using an adaptive threshold segmentation method. The findings indicate that ultrasound waves reflected in RTV materials propagate as spherical waves, with the acoustic energy primarily concentrated near the acoustic axis. As the propagation distance increases, the sound beam disperses along the axis and extends beyond the transducer, resulting in a decrease in the sound field's directionality. The developed visual reconstruction method in this study offers the capability of three-dimensional visualization for defects present within RTV coatings, including their length, width, and depth. The accurate determination of defect size is achieved through the utilization of the adaptive threshold segmentation method, yielding an average error rate of 5.7 % across different defect types. In comparison, the maximal interclass variance method (OTSU) and the fuzzy C-means (FCM) method produced results with error rates of 9.8 % and 7.9 %, respectively. The research presented in this paper enables precise assessment of debonding defect severity and establishes a reliable foundation for on-site inspection, operation, and maintenance of RTV coatings.

A refined energy ratio method based on ultrasonic echoes for measuring short bolt axial stress

Ultrasonic stress measurement methods play an important role in measuring bolt axial stresses. Traditional Time of Flight (TOF) approaches have accuracy limits when applied to short-bolt stress measurement. This paper proposes a Refined Ultrasonic Echo-based Energy Ratio (RUEER) method for bolt stress measurement that considers internal nonlinear scattering attenuation effects, transmission-reflection effects at the interface area, and energy loss in the transition region. The ultrasonic wave inside the stressed bolt is decomposed into different components to better evaluate the energy attenuation of ultrasonic echoes. The proposed method is verified with stress measurement experiments. Results show that the average error of the proposed RUEER method is less than 2.5% for different bolts and clamping lengths. Through comparative testing, the accuracy of the proposed method demonstrates improved performance over the TOF and Energy Attenuation Method (EAM). Additionally, the RUEER method is more robust to variations in equipment performance than the TOF method.

The Influence of the Hardness of the Tested Material and the Surface Preparation Method on the Results of Ultrasonic Testing

Non-destructive ultrasonic testing can be used to assess the properties and condition of real machine elements during their operation, with limited (one-sided) access to these elements. A methodological question then arises concerning the influence of the material properties of such elements and the condition of their surfaces on the result of ultrasonic testing. This paper attempts to estimate the influence of material hardness and surface roughness on the result of such testing study area testing machine or plant components of unknown exact thickness. Ultrasonic testing was carried out on specially prepared steel samples. These samples had varying surface roughness (Ra from 0.34 to 250.73 µm) of the reflection surface of the longitudinal ultrasonic wave (the so-called reflectors) and hardness (32 and 57 HRC). The ultrasonic measures were the attenuation of the wave, estimated by the decibel drop in the gain of its pulses, and the propagation velocity of the longitudinal ultrasonic wave. Ultrasonic transducers (probes) of varying frequencies (from 2 to 20 MHz), excited by a laboratory and industrial defectoscope were used as the source of such a wave. The results of our research provide a basis for the recommendation of two considered ultrasonic quantities for assessing the material properties of the tested element. This is of particular importance when testing machines or plant components of unknown exact thickness and unknown roughness of inaccessible surfaces, which are the reflectors of the longitudinal ultrasonic wave used for testing. It has been demonstrated that by using the ultrasonic echo technique, it is possible to evaluate the roughness and hardness of the tested elements.

Separation of multi-echo overlapping ultrasonic signals for increasing the axial resolution using a neural network

Abstract The ultrasonic pulse-echo method plays a significant role in ultrasonic thickness measurement. However, when the measured thickness is relatively thin, it is universal for the reflected ultrasonic echoes to overlap in the time domain which constrains the achievable resolution using the conventional pulse-echo method. A deep learning network for multi-echo overlapping ultrasonic signals separation under the constraint of an ultrasonic transducer with specific center frequency and bandwidth was proposed to enhance the axial resolution. With this model, the adaptive separation of overlapping signals with an unknown number of echoes is achieved by extracting detailed features of the overlapping signals. An overlapping signal training dataset based on truncated Nakagami functions is constructed to train the network. Simulation experiments under different degrees of overlap and noise levels were conducted for the performance analysis. Simulation results manifested the performance advantage of proposed separation method over some traditional separation methods. Further, ultrasonic thickness measurement experiments were performed on thin-walled aluminum alloy plates with various thicknesses, demonstrating feasibility and practicability of the network over traditional separation methods.

An Ultrasonic RF Acquisition System for Plant Stems Based on Labview Double Layer Multiple Triggering.

Ultrasound is widely used in medical and engineering inspections due to its non-destructive and easy-to-use characteristics. However, the complex internal structure of plant stems presents challenges for ultrasound testing. The density and thickness differences in various types of stems can cause different attenuation of ultrasonic signal propagation and the formation of different echo locations. To detect structural changes in plant stems, it is crucial to acquire complete ultrasonic echo RF signals. However, there is currently no dedicated ultrasonic RF detection equipment for plant stems, and some ultrasonic acquisition equipment has limited memory capacity that cannot store a complete echo signal. To address this problem, this paper proposes a double-layer multiple-timing trigger method, which can store multiple trigger sampling memories to meet the sampling needs of different plant stems with different ultrasonic echo locations. The method was tested in experiments and found to be effective in acquiring complete ultrasonic RF echo signals for plant stems. This approach has practical significance for the ultrasonic detection of plant stems.

ULS4US: universal lesion segmentation framework for 2D ultrasound images

Objective. Deep learning (DL) methods have been widely utilized in ultrasound (US) image segmentation tasks. However, current DL segmentation methods for US images are typically developed only for lesion segmentation of specific organs; e.g. breast or thyroid US. So far, there is currently no general-purpose lesion segmentation framework for US images that can be implemented across various organs in computer aided diagnosis scenarios. Considering that most lesion locations in US images have abnormal ultrasonic echo intensities or patterns that may be visually distinct from surrounding normal tissues or organs, it is thus possible to develop a universal lesion segmentation framework for US images (named as ULS4US), focusing on effectively identifying and segmenting lesions of various sizes in different organs. Approach. The proposed ULS4US framework comprises three components: (1) a multiple-in multi-out (MIMO) UNet that incorporates multiscale features extracted from the US image and lesion, (2) a novel two-stage lesion-aware learning algorithm that recursively locates and segments the lesions in a reinforced manner, and (3) a lesion-adaptive loss function for the MIMO-UNet that integrates two weighted components and one self-supervised component designed for intra- and inter-branches of network outputs, respectively. Main Results. Compared to six state-of-the-art segmentation models, ULS4US has achieved superior performance (accuracy of 0.956, DSC of 0.836, HD of 7.849, and mIoU of 0.731) in a unified dataset consisting of two public and three private US image datasets, which include over 2200 images of three specific types of organs. Comparative experiments on both individual and unified datasets suggest that ULS4US is likely scalable with additional data. Significance. The study demonstrates the potential of DL-based universal lesion segmentation approaches in clinical US, which would substantially reduce clinician workload and enhance diagnostic accuracy.

Estimation of the Azimuth Angle of the Arrival Direction for an Ultrasonic Signal by Using Indirect Determination of the Phase Shift

The paper presents and discusses a method of azimuth determination of ultrasonic echo arrival in air. The basis of the presented approach is the assumption that the received signal is a narrowband one. In this way, the direction of the signal arrival can be determined based on its phase shift using two receivers. When the distance between the receivers exceeds half of the wavelength of the received signal, a problem of ambiguity in determining the angle of arrival arises. To solve this, a method using multiple pairs of receivers was used. Its robustness and temperature dependence is analysed. The most important advantages of the presented approach are simplified computations and low hardware requirements. Experimental data made it possible to show that for strong echoes, the accuracy is higher than 0.5X. In the case of weak echos, it is reduced to about 2X. Because the method is based on phase shift measurement, the ultrasonic sonar that uses this method can be compact in size. Moreover, owing to the theoretical analysis, certain properties of the mutual location of the receivers were found and formally proved. They are crucial for determining proper receivers’ inter-distances.

FPGA based real‐time inference machine for A‐mode ultrasonic echo pattern recognition

AbstractThis brief proposes a field programmable gate array (FPGA) based real‐time inference machine for A‐mode ultrasonic echo pattern recognition. The proposed scheme can aid a probe positioning of single‐transducer based ultrasound devices by detecting specific echo patterns on a scanline. The inference machine is based on a combination of a support vector machine and a finite state machine. The proposed inference machine utilizes a minimal pre‐processing for a feature extraction, considering a nature of distinctive echo patterns. In addition, a primary computation of linear support vector machine is performed sequentially, and the finite state machine for robust binary classification is relatively compact. As a result, the overall structure of the inference machine can be concisely implemented on FPGA. The proposed inference machine was assembled with a customized A‐mode ultrasound scanner. The maximum utilization percentage of look‐up‐tables in FPGA was less than 1.5%. In evaluation of inferences, the resultant sensitivity and the specificity were 96.2% and 99.87%, respectively.

Measurement of axial stress in high-strength short bolts using ultrasonic attenuation

Based on the scattering attenuation theory of loaded polycrystalline metals, a bolt axial stress measurement method using ultrasonic echo attenuation is proposed for the problem that the conventional time of flight (TOF) method is difficult to measure the stress of high-strength short bolts. Firstly, the attenuation coefficients of body-centred cubic crystalline materials in the Rayleigh scattering range are described, and the mathematical model between the ratio of the peak of the two bottom echoes and the axial stress of the bolt is further derived. Then, the effects of the transducer wafer diameter, mounting eccentricity and bolt thread on the ultrasonic signal were analysed by finite element simulation, to provide a strategy for transducer selection and mounting. In addition, a magnetic ring clamping device was designed to ensure a tight fit between the transducer and the bolt, and to improve the alignment of both. Finally, a bolt axial stress ultrasonic measurement system was built, and a comparison experiment between TOF method and attenuation method was conducted to predict the axial stress of bolts. The experimental results shown that the average percentage measurement error of the attenuation method proposed in this paper is 4.42%–5.76% lower than that of the conventional TOF method, which is more suitable for measuring the axial stress of bolts.

Surrounding Object Material Detection and Identification Method for Robots Based on Ultrasonic Echo Signals

Material perception is one of the several essential abilities required by robots, and it also poses significant challenges in real-world field applications. In harsh environments, the existing material recognition methods have the problem of low accuracy. In this paper, ultrasonic technology is applied to the field of material recognition, which is derived from the predatory strategy of echolocation animals. We propose a novel noncontact material recognition method of surrounding objects for robots based on ultrasonic echo signals, which can be used in external extreme environments. This method primarily adopts the 16-dimensional feature vector extracted from intrinsic mode functions that we gain from empirical mode decomposition as inputs of machine learning algorithms to recognize different materials, and we use K-nearest neighbor, decision tree, and support vector machine algorithms on the feature vector set to decide the best classifier, and its acoustic theoretical model is established additionally. The experimental results validate the accuracy of the acoustic theoretical model and the effectiveness of the proposed method. Compared with the existing methods, the proposed method improves the low accuracy and the poor recognition effect in ultrasonic material recognition. This method provides a new idea for robots to recognize and perceive materials in extreme environments.

A denoising method for ultrasonic testing of rubber composites based on improved symplectic geometric mode decomposition

Ultrasonic nondestructive testing is widely used in industry due to its wide detection range and high detection efficiency. However, for multi-layer stacked rubber materials, ultrasound is easily affected by noise interference, it is a challenge work to reduce or separate the noise via signal processing. Therefore, a new ultrasonic echo denoising method based on symplectic geometry mode decomposition is proposed. It can fully decompose the useful components and noise components in the signal, and has a good screening effect on the useful components and noise components. In order to verify the effectiveness and robustness of the method, we conduct experimental research on the model with simulated and measured signals. The analysis results show that our method outperforms other noise reduction methods.