Ultrasonic Signal Processing Research Articles

Ultrasonic scattering measurements of jet gas–liquid interface fluctuations in confined spaces

Determining jet gas–liquid interfaces in liquid rocket engines is crucial for understanding the mechanisms underlying combustion instabilities. While optical diagnostic methods are commonly employed, they become ineffective when optical access is restricted. In such cases, ultrasonic techniques provide a viable alternative. The present study develops an ultrasonic method to detect jet gas–liquid interfaces in a confined chamber based on ultrasonic scattering principles. The fluctuations of the water jet's gas–liquid interface are precisely captured using this method and validated against high-speed optical imaging. For the optical method, sub-pixel boundary extraction is used to obtain the jet interfaces, while for ultrasonic signal processing, we extract the pulsed scattered waves generated by the jet in the confined space. The experimental results align well with theoretical predictions. Additionally, to facilitate accurate measurement of gas–liquid interface fluctuations, we create a database of ultrasonic scattering results using a set of metal rods to model the liquid jet. This enables high-frequency, high-precision measurements of jet interface fluctuations using ultrasonics. A comparison between the ultrasonic and optical methods reveals a mean measurement error of 70 μ m (3.45% mean relative error) and a maximum error of 250 μ m (12.6% maximum relative error), with the ultrasonic method offering a temporal resolution of 1 kHz. This proposed method provides a novel solution for measuring two-phase flow parameters in confined environments where optical access is limited.

A signal denoising method based on goose optimization algorithm for optimal variational modal decomposition and improved wavelet thresholding function

Ultrasonic flowmeters are widely used in energy and control applications, providing accurate and fast measurement of fluid flow rates. This paper proposes a denoising method based on the goose optimization algorithm, a nature-inspired optimization method mimicking the foraging behavior of goose. GO optimizes the penalty factor and decomposition layer number of variational modal decomposition, resulting in the GO-VMD approach. Decomposed modal components are further denoised using an improved wavelet thresholding method. The algorithm is compared with existing methods, such as high-frequency ultrasonic signal processing, and experimental results show that it improves the signal-to-noise ratio by 8%, reduces root mean square error by 5%, retains more useful information, and achieves significant denoising results.

Advanced Industrial Fault Detection: A Comparative Analysis of Ultrasonic Signal Processing and Ensemble Machine Learning Techniques

Modern condition monitoring and industrial fault prediction have advanced to include intelligent techniques, aiming to improve reliability, productivity, and safety. The integration of ultrasonic signal processing with various machine learning (ML) models can significantly enhance the efficiency of industrial fault diagnosis. In this paper, ultrasonic data are analyzed and applied to ensemble ML algorithms. Four methods for reducing dimensionality are employed to illustrate differences among acoustic faults. Different features in the time domain are extracted, and predictive ensemble models including a gradient boosting classifier (GB), stacking classifier (Stacking), voting classifier (Voting), Adaboost, Logit boost (Logit), and bagging classifier (Bagging) are implemented. To assess the model’s performance on new data during experiments, k-fold cross-validation (CV) was employed. Based on the designed workflow, GB demonstrated the highest performance, with less variation over 5 cross-folds. Finally, the real-time capability of the model was evaluated by deployment on an ARM Cortex-M4F microcontroller (MCU).

Design and Research of a Certain Type of Automatic Alarm Equipment

Rapid detection of oxygen leaks in pipelines is crucial to prevent catastrophic accidents. This paper presents the design of an oxygen leakage detection and alarm instrument that converts ultrasonic signals, generated by gas escaping through small holes, into audible frequencies for headset use. This instrument is particularly suitable for ensuring the airtightness of oxygen supply systems for flight crews, engine startup supplemental oxygen systems, and four-station oxygen supply equipment. The design integrates ultrasonic principles, signal processing, computer control, and anti-jamming technologies to detect even minor leaks from a distance. The modular design includes components such as ultrasonic sensors, preamplifiers, filtering circuits, and microcontrollers, ensuring high detection sensitivity and reliability. The alarm system features light and sound alerts, enhancing maintenance efficiency and safety. This modern precision testing instrument significantly improves upon traditional leak detection methods like soapy water tests.

Development of an acoustic doppler flow profiler and flow testing

The flow test with acoustic Doppler current profiler (ADCP) is a new flow test method developed and applied only in the last decade or so. However, because it involves many disciplines such as hydroacoustic physics, ultrasonic transducer technology, electronics, and signal processing, most of the ADCPs used at the present stage are imported, and the domestically produced high-precision ADCPs have a lot of space in the domestic market. This paper introduces the measurement principle of ADCP. Based on this principle, we have overcome the weak signal processing circuit and successfully developed a domestic horizontal fixed acoustic Doppler flow profiler. The instrument is used for flow tests in Yixing (South) Hydrological Station, and the results of the comparison and error analysis meet the requirements of the national first-class precision hydrological station in the “Hydrological Data Compilation Specification” to satisfy the needs of the daily test in the station. The “Horizontal Fixed Acoustic Doppler Profile Flow Rate System” was selected as one of the 100 typical cases of popularizing and applying the achievements of the Ministry of Water Resources in 2020, and it can replace the imported instruments.

A generic time-frequency analysis-based signal processing and imaging approach for air-coupled ultrasonic testing

The utilization of air-coupled ultrasonic technology is crucial for examining objects that are challenging to establish contact with or cannot endure coupling. Nevertheless, air-coupled ultrasonic is susceptible to various factors such as impedance mismatch, sound wave attenuation, and environmental noise, consequently leading to a diminished signal-to-noise ratio of the received signal. This reduction in signal quality amplifies the complexity of analysis and imaging processes. In this study, a generic air-coupled ultrasonic signal processing and imaging procedure for ultrasonic inspection is proposed, which capitalizes on time-frequency analysis. This methodology attains a heightened level of stability and superior signal-to-noise ratio imaging through a series of procedural steps, encompassing feature extraction, denoising, and the reconstruction of the A-scan signals. The efficacy of the proposed approach is quantitatively assessed in terms of signal-to-noise ratio, probability of detection, robustness, and generality. This innovative method presents a newfangled resolution for automated air-coupled ultrasonic testing.

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.

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.

Технология оптимальной обработки сигналов в области ультразвука с высоким разрешением

Purpose of the work: To present the technology of processing ultrasonic signals with high resolution. Methods: The basis is the Helstrom substitution, which eliminates the ambiguity of maximizing the likelihood function in terms of reception time, it is shown that the transformed likelihood function and the transformed likelihood ratio functional as a result of Helstrom substitution, mutually complementing each other, allow solving statistical problems in the field of high-resolution signals. Results: Based on model calculations, the result of the Helstrom substitution is presented, eliminating the ambiguity of maximizing the likelihood function. Statistics of amplitudes and reception times of two ultrasonic signals are given depending on the signal-to-noise ratio and depending on the difference in reception times. Estimates of the operating resolution range of two ultrasonic signals and estimates of the signal-to-noise ratio range in which the errors in the estimates of signal parameters do not exceed 10 % are given.

Design and construction of an Ultrasounographic Device Dedicated to the Parietal Rheology Measurement in Human Carotid Artery

We present in this article a detection system of ultrasounographic signal (USG) dedicated to the measurement of parietal rheology in human carotid artery. The aim of this practical work is to develop an ultrasonic method capable of visualizing the localized velocity profile of the carotid artery wall. The device produced makes it possible to take the USG signal from carotid wall of the human neck. It comprises an ultrasonic contact probe applied during the measurement (sensor), a signal shaping circuit, an acquisition and digitization interface in conjunction with an RS232 serial communication port and a local computer terminal hosting a graphical user interface (GUI) implemented in Visual Basic integrated development environment enabling the display, archiving and the digital processing of parietal ultrasonic signal. The aim of this study is to reconnoitering the possibility of using ultrasound for a continuous and non-invasive measurement of the arteries parietal rheology. In this work, a sensor of ultrasound is used to measure the movement and the speed of the carotid artery wall, an image of the arteries parietal rheology.

Investigating the impact of plasma nitriding on Ti6Al4V surface, structural, and mechanical properties and their simultaneous evaluation via laser opto-ultrasonic dual detection (LOUD) approach

Titanium alloys possess exceptional properties, but due to their poor surface characteristics, several engineering processes have been developed to modify surface properties. Surface modification methodologies such as plasma nitriding exhibit a favourable pathway to improve these properties. This study investigates the impact of plasma nitriding on surface properties and their evaluation via the laser opto-ultrasonic dual detection (LOUD) technique. The scanning electron microscope, X-ray diffraction, and tensile testing results showed that with the increase in nitride plasma power density from 1.42 to 10.0 × 102 W/cm2, a substantial variation in microstructure and phase transformation occurs, eventually refining the grain size, increasing the hardness from 310.83-HV5.0 to 745.50-HV5.0 and elastic modulus (E) from 115.26 to 128.35 GPa, respectively. Furthermore, these characteristics were assessed concurrently through ultrasonic and optical signal processing for LOUD detection. The results of G.S and E from the acoustic attenuation coefficient and longitudinal and shear wave velocities are reliable with the results of conventional optical microscopy and tensile testing with (R2 ≥ 0.996). Meanwhile, the optical spectral data were analyzed to determine the hardness. The results showed that the calibration curve of the intensity ratios (Ti-II/Ti-I) and plasma electron temperature exhibited a linear relationship with hardness (R2 ≥ 0.989), which showed a good approximation.

Retracted: Detection and Analysis of Man-Machine Interactive Software Vulnerabilities Based on Ultrasonic Data Acquisition and Signal Processing Algorithms

Retracted: Detection and Analysis of Man-Machine Interactive Software Vulnerabilities Based on Ultrasonic Data Acquisition and Signal Processing Algorithms

Determining the Elastic Constants of Isotropic Materials by Measuring the Phase Velocities of the A0 and S0 Modes of Lamb Waves.

In this study, a new method for determining the elastic constants of isotropic plates using Lamb wave fundamental modes is presented. This method solves the inverse problem, where the elastic constants (Young's modulus and Poisson's ratio) of the plate were estimated by measuring the phase velocities of the Lamb wave using the Rayleigh-Lamb equations to find the solution and determining the phase velocities of the A0 and S0 modes using a new method. The suitability of the proposed method for determining the elastic constants was evaluated using simulated and experimental signals propagating on an aluminum plate. The theoretical modeling on the aluminum 7075-T6 plate shows that the proposed method allows the determination of the Poisson ratio with a relative error not exceeding 2% and Young's modulus with a relative error not exceeding 0.5%. The experimental measurements of an aluminum plate of known thickness (2 mm) and density (2685 kg/m3) confirmed the suitability of the proposed method for the measurements of elastic constants. In the proposed method, the processing of ultrasonic signals can be performed in real-time, and the values of the elastic constants can be obtained immediately after scanning the required distance.

Research on Sparse Decomposition Processing of Ultrasonic Signals of Heat Exchanger Fouling

Ultrasonic time-domain reflectometry has the advantages of real-time and accurate detection of fouling in heat exchange tubes, but owing to the complex detection environment, the received ultrasonic is noisy, and there are problems such as mutual interference between echoes. This article presents a new method for sparse decomposition based on selecting the atomic dictionary with a high degree of matching. The imperialist competitive algorithm is used to optimize the atomic search process of the matching pursuit. Additionally, the global optimization ability of the imperialist competitive algorithm is improved by improving the moving mode and adding the update strategy. This way, problems such as low matching accuracy and slow searching speed of sparse decomposition are improved. This article reports on the characteristic reconstruction experiment run on the ultrasonic detection signal for heat transfer fouling to test the signal processing effect of the improved sparse decomposition method.

An approach to characterize the compositional, structural and mechanical properties of thermally processed titanium (Ti) alloy simultaneously via novel in-situ laser opto-ultrasonic dual detection (LOUD)

A keen interest is developed in improving heat treatment methods of Ti alloy to meet the desirable mechanical properties due to its enhanced physical and chemical features. Structural and mechanical properties are important parameters as they are significantly affected by heat treatment and process conditions. However, most metal manufacturer relies on separate post-process analysis, which is usually time-consuming, costly, and destructive. Therefore, this study investigated a novel in-situ laser Opto-ultrasonic dual detection (LOUD) approach for real-time analysis of the elemental composition, grain size distribution, and hardness simultaneously of heat-treated Ti6AL4V alloy. The X-ray diffraction (XRD) and scanning electron microscope (SEM) results showed that with the increase in annealing temperature and cooling rate, a pronounced change in microstructure and phase transition occurs, eventually increasing grain size and hardness, respectively. The elemental composition, grain size distribution, and hardness variations were evaluated simultaneously via the processing of ultrasonic and optical signals in in-situ LOUD detection. The obtained results of average grain size from the ultrasonic acoustic attenuation of all the investigated samples are consistent with the results of optical microscope (OM) maps with a good regression coefficient value (R2 ≥0.991). Meanwhile, the optical spectral processing data for hardness analysis exhibited that the calibration curve of the emission intensity ratio (Ti-II/Ti-I) and plasma electron temperature (Te) is linearly dependent on hardness with (R2 ≥0.994). The linear correlation results of ultrasonic acoustic attenuation with grain size, calibration curve, and Te with the hardness investigated via the in-situ LOUD technique shows good approximation, ensuring quality control and reliability.

Auto-Diagnosis of Time-of-Flight for Ultrasonic Signal Based on Defect Peaks Tracking Model

With the popularization of humans working in tandem with robots and artificial intelligence (AI) by Industry 5.0, ultrasonic non-destructive testing (NDT)) technology has been increasingly used in quality inspections in the industry. As a crucial part of handling ultrasonic testing results–signal processing, the current approach focuses on professional training to perform signal discrimination but automatic and intelligent signal optimization and estimation lack systematic research. Though the automated and intelligent framework for ultrasonic echo signal processing has already exhibited essential research significance for diagnosing defect locations, the real-time applicability of the algorithm for the time-of-flight (ToF) estimation is rarely considered, which is a very important indicator for intelligent detection. This paper conducts a systematic comparison among different ToF algorithms for the first time and presents the auto-diagnosis of the ToF approach based on the Defect Peaks Tracking Model (DPTM). The proposed DPTM is used for ultrasonic echo signal processing and recognition for the first time. The DPTM using the Hilbert transform was verified to locate the defect with the size of 2–10 mm, in which the wavelet denoising method was adopted. With the designed mechanical fixture through 3D printing technology on the pipeline to inspect defects, the difficulty of collecting sufficient data could be conquered. The maximum auto-diagnosis error could be reduced to 0.25% and 1.25% for steel plate and pipeline under constant pressure, respectively, which were much smaller than those with the DPTM adopting the cross-correlation. The real-time auto-diagnosis identification feature of DPTM has the potential to be combined with AI in future work, such as machine learning and deep learning, to achieve more intelligent approaches for industrial health inspection.

Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning.

In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. The proposed model transmits ultrasound signals through a pipe using a custom-designed array of piezoelectric transmitters and receivers. We propose to use the Zadoff-Chu sequence to modulate the input signals, then utilize its correlation properties to estimate the pipe channel response. The processed signal is then fed to a DNN that extracts the features and decides whether there is a diversion or not. The proposed technique demonstrates an average classification accuracy of 90.3% (when one sensor is used) and 99.6% (when two sensors are used) on 34 inch pipes. The technique can be readily generalized for pipes of different diameters and materials.

Application of Bispectrum Dimensionality Reduction Method in Ultrasonic Echo Signal Processing

Processing ultrasonic echo signals to obtain high-precision residual thickness information of the pipeline wall is the key to nondestructive testing of corrosion of a long-distance pipeline. The traditional power spectrum estimation method assumes that an analyzed echo signal is Gaussian, and the useful information is insufficiently extracted, which leads to errors in the processing results. In this paper, to solve this problem, the bispectrum, which requires the least amount of computation in higher-order spectral estimation, is proposed to process an echo signal with a non-minimum phase and non-Gaussian characteristics. The bispectrum is projected onto a one-dimensional frequency space using the dimensionality reduction method, and one-dimensional diagonal slices of the bispectrum are extracted to analyze the characteristics of the echo signal, which significantly improves the intuitiveness of data processing. The experimental results show that the bispectrum dimensionality reduction method has high accuracy in processing ultrasonic echo signals, and the relative error of the residua wall thickness is below 2%. A C-scan image displaying the shape, size, depth, and other characteristics of pipeline corrosion obtained by the proposed method is much better than that using the traditional power spectrum estimation method. Therefore, the proposed method is suitable for nondestructive testing of corrosion of long-distance pipelines.

Electromagnetic ultrasonic signal processing and imaging for debonding detection of bonded structures

This paper investigates an electromagnetic ultrasonic method for debonding detection of adhesive structures. Considering the non-stationary and nonlinear characteristics of the electromagnetic ultrasonic reflection signals, a joint adaptive noise reduction and reconstruction (JANRR) method, namely, complete ensemble empirical mode decomposition(CEEMDAN) and singular spectrum analysis(SSA) based on continuous mean square Error(CMSE) are proposed, in which, mirror extension is used to suppress the endpoint effect of decomposition, and the order of SSA noise reduction is determined based on singular entropy theory. For two side debonding detection of the adhesive layer, a back propagation (BP) neural network classification model is developed, and five characteristics such as signal skewness factor are taken as the network inputs based on Chi square test. Experiments were carried out for validation, which show that the proposed method can distinguish both signals of the upper and lower interfaces of the adhesive layer under different bonding states.

Phase Coherence Imaging: Advantages of a New Ultrasonic Technique

Abstract Phase coherence imaging is a new approach for processing ultrasonic signals generated during nondestructive testing. This article shows how it compares with amplitude-based focusing methods for detecting hydrogen-related microfissures, creep-induced damage, and stainless-steel weld voids.