Search

Fatigue damage in a bone occurs in the form of micro-scale cracks with the lengths as small as to a few microns. The evaluation of cracks in bones has recently been a hot topic. However, the current most frequently used method is based on traditional linear ultrasound, which is just sensitive to gross damages rather than micro cracks. Nonlinear ultrasonic technique, which is capable of detecting micro-scale damages, has been widely used in metallic structures. However, few study has been directed to employing second harmonic generation of nonlinear ultrasound to evaluate fatigue damages in bones. In this study, a preliminary study is conducted on the interactions between nonlinear guided waves and a single crack in bone materials motivating to the evaluation of micro cracks in long bones. Considering the symmetry, asymmetry, location, orientation, length and width of the crack in bone materials, their influences on second harmonic responses are discussed in detail. Our results are presented as follows. Firstly, not only the primary S0 but also A0 mode Lamb wave generates the second harmonic component of S0 mode after interacting with a symmetric crack. Secondly, along the thickness direction, the primary S0 mode Lamb wave possesses almost the same detection sensitivity, no matter where the crack is located. Thirdly, the amplitudes of S0 mode second harmonic component are increased slightly when the oblique angle of the crack is set from 0°to 45°, while the amplitudes increase dramatically when the oblique angle changes to 67.5° and 90°. Lastly, the second harmonic amplitude and the relative acoustical nonlinear parameter are increased with the length of the crack, while they possess a monotonically decreasing relationship with the width of the crack. Our preliminary studies will provide priori knowledge when using nonlinear ultrasonic guided waves for detecting micro-scale cracks in long bones in future clinical inspections.

Real-world multiclass classification problems involve moderately high dimensional inputs with a large number of class labels. As well, for most real-world applications, uncertainty has to be handled carefully, unless the classification results could be inaccurate or even incorrect. In this paper, we investigate a binary hierarchical partitioning of the output space in an uncertain framework to overcome these limitations and yield better solutions. Uncertainty is modeled within the quantitative possibility theory framework. Experimentations on real ultrasonic dataset show good performances of the proposed multiclass classifier. An accuracy rate of 93% has been achieved.

In recent years, due to the destructive and unproductive character of pavement specimen extraction, pavement maintenance technology intensified the use of non-destructive techniques for pavement evaluation which resulted in the development of several devices and evaluation methods. This paper describes the use of technology based on low frequency ultrasonic tomography for evaluation of concrete pavement parameters. The equipment was applied in three experimental sections with different concrete pavements built at the University of Sao Paulo campus. The ultrasonic signal processing is given. The results analysis enables the efficient and reliable identification of thickness and reinforcement position within the concrete slab. Construction problems were evidenced in one of experimental sections with thickness deficiencies and reinforcement in a position below projected. Furthermore, the use of a novel concrete quality indicator was correlated with the presence of transverse cracks and alkali-silica reaction within the sections.

The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.

Monitoring pipeline wall is an important issue in oil and gas industries. Over time, the defect can occur in the pipeline and can impact surrounding population, environment and may result in injuries or fatalities. While flaws in the pipeline could be detected by ultrasonic testing and monitoring the severity of the flaw. The limitation of ultrasonic testing is the signal contaminate with backscattering noise, which masks flaw echoes in the measured signal. Signal processing take place in the recent year to de-noising for improving signal-to-noise ratio and extract the feature for flaws classification. This paper presents a comprehensive overview of signal processing techniques used to improve ultrasonic detection method with and without intelligent classifier. Finally, the advantages and disadvantages feature extraction provided for classifications process.

The ultrasonic echoes reflected from debris in lubricant contain a lot of useful information, which can represent the size, material and geometric characteristics of the debris. Our preliminary simulation investigations and physical model analysis results show that the waveshape features are feasible and essential to discriminate debris in lubricant. An accurate waveshape features extraction method of debris echoes is presented based on the matching pursuit (MP). The dictionary of Gabor functions, which is suitable for ultrasonic signal processing, is adopted for MP. To seek faster and more accurate calculation of MP, quantum-behaved particle swarm optimization (QPSO) is introduced to optimize the MP algorithm. The simulation and experimental results reveal that the proposed method can effectively extract the waveshape features of debris echoes and air bubble echoes. Utilizing the extracted waveshape features, the debris with different shapes and air bubble can be distinguished.

Ultrasonic testing (UT) technique has been widely used in defect detection of composite materials. To better identify the defective regions, a number of one- or two-dimensional signal processing methods have been adopted for defect signal enhancement. However, the application of these methods is limited by their complex operation. Most of the existing methods cannot deal with the entire three-dimensional tensor of UT data in an efficient manner. In order to solve this problem, a third-order tensor decomposition method, Tucker3, is adopted in this paper for UT-based defect detection in fiber reinforced polymer (FRP) structures. After Tucher-3 decomposition, the defect information is extracted by a small number of factors, which is further summarized by the leverages. The candidate defective regions are then identified from the leverages, based on which the locations and the shapes of the defects can be calculated by clustering.

In this thesis, the set of novel ultrasonic signal analysis and interpretation methods, which are mandatory in order to properly analyse the complex behaviour of guided waves, to extract the features of detected damage and to validate the monitoring system, were proposed. These methods aim to solve the key problems in the field of guided wave structural health monitoring. The methods proposed in this research creates a framework for the guided wave based structural health monitoring system, that can predict the complex mechanism of guided wave generation; analyse multi-modal propagation; decompose guided wave signals captured on plate-like anisotropic structures; detect and describe the delamination type defects and validate the correctness of the inspection system upon installation to an in-service structure., Darbe pasiūlyti ir ištirti ultragarsiniai stebėsenos metodai, kurie palengvina nukreiptųjų bangų signalų analizę bei interpretavimą ir leidžia aptikti, apibūdinti lakštinėse kompozitinėse struktūrose atsiradusius defektus ir patvirtinti jų atsiradimą. Pasiūlyti metodai padeda spręsti esmines su signalų analize ir defektų aptikimu susijusias problemas ir sudaro kompleksinį paprastesnio tokių sistemų realizavimo pagrindą. Disertacijoje pasiūlyta grupė signalų analizės metodų, kurie gali būti atskirai arba kartu taikomi nukreiptųjų bangų žadinimo, sklidimo analizei, modų dekompozicijai, atsisluoksniavimo defektams aptikti, o taip pat stebėsenos sistemoms testuoti, jų jautrumui įvertinti.

Darbe pasiūlyti ir ištirti ultragarsiniai stebėsenos metodai, kurie palengvina nukreiptųjų bangų signalų analizę bei interpretavimą ir leidžia aptikti, apibūdinti lakštinėse kompozitinėse struktūrose atsiradusius defektus ir patvirtinti jų atsiradimą. Pasiūlyti metodai padeda spręsti esmines su signalų analize ir defektų aptikimu susijusias problemas ir sudaro kompleksinį paprastesnio tokių sistemų realizavimo pagrindą. Disertacijoje pasiūlyta grupė signalų analizės metodų, kurie gali būti atskirai arba kartu taikomi nukreiptųjų bangų žadinimo, sklidimo analizei, modų dekompozicijai, atsisluoksniavimo defektams aptikti, o taip pat stebėsenos sistemoms testuoti, jų jautrumui įvertinti., In this thesis, the set of novel ultrasonic signal analysis and interpretation methods, which are mandatory in order to properly analyse the complex behaviour of guided waves, to extract the features of detected damage and to validate the monitoring system, were proposed. These methods aim to solve the key problems in the field of guided wave structural health monitoring. The methods proposed in this research creates a framework for the guided wave based structural health monitoring system, that can predict the complex mechanism of guided wave generation; analyse multi-modal propagation; decompose guided wave signals captured on plate-like anisotropic structures; detect and describe the delamination type defects and validate the correctness of the inspection system upon installation to an in-service structure.

In this paper, signal processing techniques are tested for their ability to resolve echoes associated with delaminations in carbon fiber-reinforced polymer multi-layered composite materials (CFRP) detected by ultrasonic methods. These methods include split spectrum processing (SSP) and the expectation-maximization (EM) algorithm. A simulation study on defect detection was performed, and results were validated experimentally on CFRP with and without delamination defects taken from aircraft. Comparison of the methods for their ability to resolve echoes are made.

Ultrasonic echo signal from running rail defect detection equipment generally contains noise, which makes it hard to identify the defects. In this paper, wavelet transform based approach is proposed to process the ultrasonic echo signal. Through the experimental verification, the proposed method is effective in detecting the defects of rail under running operation.

The wheel-set maintenance mainly depends on ultrasonic nondestructive testing techniques to detect defects. Wheels are pressed or shrunk on the axle, and the press-fit has significant influence on ultrasonic detection at press-fit part of the in service axle comparing with bare axle. Limited by the complex testing conditions of the press-fit interface, higher amplitude noise is present in the echo signal. In this paper, simulation and axle detection experiments and filtering algorithms optimization are used to improve the signal-noise ratio. The phased array ultrasonic probe parameters and arrangements are optimized by software simulation and reference axle testing. A series of defect detection experiments between bare axle and axle with press-fit have been complemented by using phased array ultrasonic. Ultrasonic signal processing and denoising algorithm has been conducted to effectively improve the signal-noise ratio.

In this paper, we present ultrasonic signal processing methods for the quantification of volume loss in a thick-wall aluminum tube, including temperature compensation, along with a novel experimental sequence for control purposes. The experimental sequence contains damage steps and null (no-damage) steps, randomly distributed and of random time duration. For the quantification, circumferentially propagating ultrasonic waves are used because they can repeatedly sample the volume loss area in the tube. Our experimental results show that the ultrasonic signal processing methods used in this paper, combined with the optimal signal stretching temperature compensation technique, can successfully quantify the volume loss. doi: 10.12783/SHM2015/89

High-speed implementations of computationally-intensive algorithms are essential for real-time ultrasonic signal processing application development. Hardware-software co-design allows flexible system development to carry out such experiments. In this paper, we developed a reconfigurable ultrasonic system-on-chip hardware (RUSH) platform which offers a Linux based development environment by integrating FPGA firmware with specially designed software for high performance ultrasonic signal processing applications. Furthermore, the performance and resource utilization of the RUSH platform is analyzed for various ultrasonic signal processing algorithm implementations.

The maximum entropy method (MEM) is a signal processing technique for reconstructing high quality, all positive images from noisy data. The paper describes an extension to the MEM that allows consideration of images having both positive and negative parts. The necessary alterations to the algorithm of Skilling and Bryan (1984) are described. Simulations using positive and bipolar images have been carried out with the aim of observing the effect of the enhancement on the performance of the MEM. Three areas are discussed in detail: the frequency extension property; the comparison of spike and rectangular bipolar images; and the reliability of the restoration of peak heights when using bipolar images. Where appropriate, the MEM results are compared with those from the Wiener-Hopf filter. The modification now extends the application area of the MEM in ultrasonic signal processing, allowing its use in situations where images contain both positive and negative images.

The major task of imaging systems used in non-destructive testing is detection of defects (flaws, holes) echoes and evaluation or their dimensions and position. Images generated by direct imaging are poor quality due to diffraction, coherent echoes, limited capabilities of system, white noise. So acoustic images and optic images are very different. Solving these problems, numerical methods are used for signal processing and analysis. An application for ultrasonic signal processing of wavelet transformations was investigated in the work. The investigation was signals distortion type and level when wavelets transformation may give effective results. The investigation was performed using modeling and signal processing of the experimental signals.

Permanently mounted ultrasonic sensors are a key component of systems under development for structural health monitoring. Signal processing plays a critical role in the viability of such systems due to the difficulty in interpreting signals received from structures of complex geometry. This paper describes a differential feature‐based approach to classifying signal changes as either “environmental” or “structural”. Data are presented from piezoelectric discs bonded to an aluminum specimen subjected to both environmental changes and introduction of artificial defects. The classifier developed as part of this study was able to correctly identify artificial defects that were not part of the initial training and evaluation data sets. Central to the success of the classifier was the use of the Short Time Cross Correlation to measure coherency between the signal and reference as a function of time.

PRELIMINARY RESULTS OF THE RADIOFREQUENCY ULTRASONIC SIGNAL PROCESSING IN PROSTATE CANCER DETECTION

This paper describes new intelligent or neural‐like processing procedures by which the ultrasonic signals scattered by an obstacle can be processed to recover the characteristics of the unknown scatterer. In particular, it focuses on the recently developed automatic modeler by which the characteristics of the scattering phenomenon from a number of scatterers is learned using a set of systematic learning data. It is demonstrated that ultrasonic signals measured in subsequent tests from an unknown scatterer can be processed to optimally recover the characteristics of the unknown obstacle. The solution of the inverse scattering problem that is obtained is thus completely empirical and not based on any elastodynamic theory. [Work supported by the National Science Foundation under Grant MSM‐8904384.]

The interpretation of ultrasonic signals in the inspection for flaws in bonded regions of multilayered specimens is difficult because of signal energy loss due to material attenuation, undesired reverberations within certain layers, and overlapping responses from different interfaces because of finite transducer bandwidth. The flaws are usually air gaps, lack of adhesion, and porosity within the bonding agent.

The demand for powder metallurgy (P/M) parts in its traditional automotive market is predicted to grow, but future sector expansion depends directly upon its capability to manufacture zero-defect parts for industries such as aerospace and medicine. The lack of adequate inspection systems has important implications from the point of view of quality assurance, since it increases the costs, time and wasted material. In recent years the applicability of several techniques for the inspection of P/M parts has been investigated, such as Eddy current testing, computer tomography or X-ray imaging, but studies have revealed that all of them have deficiencies that make them unavailable for a complete and reliable flaw detection and density defect recognition. A new inspection tool has been developed based on pulse echo ultrasonic technology combined with robotics, which makes it possible to provide a global density map of sintered.

The application of multi-mode techniques, in order to determine the times of flight of the ultrasounds, gives solution to the main problems of this kind of sensorial system (little precision and low speed of acquisition), but also presents certain constraints to perform its processing in real time. In this way, the application of the principles of the logical reconfiguration implies more optimal and efficient systems in the use of resources assigned initially to each task of the process, obtaining significant improvements in the developed platforms compared with other possible processing architectures more frequently used.