An electronic architecture for intelligent portable pulse-echo ultrasonic instrument

This design aims to reduce the required measurement time and size of the instrument by modifying its electronic architecture in terms of components used, configuration of programmable logic and firmware. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3177

Waveform acquisition with resolutions exceeding those of the ADCs employed

This chapter discusses various software/firmware and hardware methods and architectures to improve the fidelity of the acquired waveforms beyond the vertical and horizontal resolutions that are possible with the ADC employed. The applicability of these approaches, and the limits on the enhancements that are achievable, depend upon the nature of the acquired waveform, and they are presented separately for one-shot, repeatable and repetitive waveforms. The possibilities of combining applicable methods in order to simultaneously increase both resolutions are also discussed. The consideration is illustrated by the simulation results and the acquired experimental waveforms relevant to the ultrasonic non-destructive evaluation

Embedded supervisory control and output reporting for the oscillating ultrasonic temperature sensors

Ultrasonic temperature sensors can potentially outperform conven-tional sensors because they are capable of very fast sensing across the complete ultrasound pathway, whilst conventional sensors only sense temperature at a single point and have substantial thermal inertia. We report recent develop-ments in electronic instrumentation for oscillating ultrasonic temperature sen-sors with the aim of achieving high accuracy and low scatter at a low cost

Laboratory Directed Research and Development FY-10 Annual Report

The FY 2010 Laboratory Directed Research and Development (LDRD) Annual Report is a compendium of the diverse research performed to develop and ensure the INL's technical capabilities can support the future DOE missions and national research priorities. LDRD is essential to the INL -- it provides a means for the laboratory to pursue novel scientific and engineering research in areas that are deemed too basic or risky for programmatic investments. This research enhances technical capabilities at the laboratory, providing scientific and engineering staff with opportunities for skill building and partnership development

Automatic simultaneous measurement of phase velocity and thickness in composite plates using iterative deconvolution

A new method for the automatic and simultaneous measurement of phase velocity and thickness for thin composite plates was developed based on Ping He's method, without any need of a priori knowledge of the material parameters. Two composites were analyzed: a block of clean epoxy and a thin specimen of glass-fiber reinforced plastic produced by resin transfer molding. The proposed method combines cross-correlation functions and iterative deconvolution for accurate measurement of times of flight and gating. The new method has demonstrated to be more accurate than conventional Ping He's method, and can be implemented automatically thus saving processing time and increasing accuracy.This research was funded by a Project IN-SMART, Grant no. VP1-3.1SMM-10-V-02-012 and by the Spanish Ministerio de Ciencia e Innovacion (TEC2011-23403).Rodriguez Martinez, A.; Svilainis, L.; Dumbrava, V.; Chaziachmetovas, A.; Salazar Afanador, A. (2014). Automatic simultaneous measurement of phase velocity and thickness in composite plates using iterative deconvolution. NDT and E International. 66:117-127. https://doi.org/10.1016/j.ndteint.2014.06.001S1171276

A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved generalisation, reduced development time, ability for continuous retraining, and the correlation of sensor data to important process information. However, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, select the type of machine learning algorithm to use, and find a suitable set of model hyperparameters. The aim of this article is to facilitate implementation of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. The article first reviews the use of ultrasonic sensors for monitoring processes, before reviewing the combination of ultrasonic measurements and machine learning. We include literature from other sectors such as structural health monitoring. This review covers feature extraction, feature selection, algorithm choice, hyperparameter selection, data augmentation, domain adaptation, semi-supervised learning and machine learning interpretability. Finally, recommendations for applying machine learning to the reviewed processes are made

Ultrasonic monitoring of temperature distributions and degradation in engineering components

Material degradations such as corrosion and erosion are prevalent in facilities of the energy sector. Excessive and unexpected material degradations can lead to catastrophic structure failures, severe service disruption and even loss of lives. Ultrasound-based non-destructive evaluation (NDE) and structural health monitoring (SHM) technology has been developed to provide quantitative data to monitor degradation processes so that they can be mitigated in the best possible way. This thesis aims to improve these ultrasound-based NDE and SHM methods by reducing their uncertainties and exploring their potential applications on emerging technologies. The first part of the thesis focuses on advancing temperature compensation strategies of the NDE and SHM technology. This is because environmental and operational conditions (EOCs), especially temperature changes are one of the biggest sources of uncertainties in the current ultrasonic measurements. At the same time, temperature controls the rate of various electrochemical processes and therefore influences the rate of material degradation. A novel dual-wave approach is presented to enhance the performance of the existing monitoring technology. By exciting both shear and longitudinal waves at the same location of a component, variations of component thickness and internal temperature distributions can be simultaneously monitored. It was shown that even under drastic thermal swings (40 celsius temperature change in under 3 minutes), thickness variations of 2 micrometers can be accurately tracked. Compared with the existing single-wave approach, thickness measurement errors can be reduced by a factor of 5. At the same time, ultrasonic temperature predictions agreed with the independent measurements using a resistance temperature detector (RTD) to within 2 celsius and corrosion-induced temperature prediction drift can be reduced by a factor of 9. The second part of the thesis explores the possibility of applying ultrasonic NDE and SHM technology on monitoring degradation phenomena in energy storage systems (ESS). ESS such as batteries have seen rapid developments recently due to the emerging demand to store energy produced from renewable resources. However, these devices also suffer from degradation issues and require appropriate monitoring solutions. This work targets a specific battery degradation phenomenon - dendrite growth at the electrode/electrolyte interface. A novel measurement method is proposed which can excite the SH0* mode guided wave in a waveguide that also serves as the battery electrode. Experimental investigations demonstrated the feasibility of the approach. The SH0* mode guided wave was shown to be sensitive to zinc dendrite in the order of tens of micrometres. Moreover, the results revealed correlations between ultrasonic signal variations and the underlying zinc plating/stripping processes, thus providing more physical insights into battery degradation mechanisms.Open Acces

Design and Validation of a Wearable, Continuous, and Non-Invasive Hydration Monitor that uses Ultrasonic Pulses to Detect Changes in Tissue Hydration Status

Chronic dehydration is an endemic problem for many population groups. Current methods of monitoring hydration status are invasive, time consuming, cannot be performed while exercising, and require lab resources. A proposed solution is a wearable, continuous, and non-invasive device that uses harm-free ultrasonic pulses to detect changes in tissue hydration status over time. Customer and engineering requirements were defined and used to guide the design process. Literature reviews were performed to identify essential information on dehydration, assess current methods, discover state of the art devices, and describe ultrasonic theory. Market research was performed to identify athletes as the target population group. An adjustable elastic nylon bicep band prototype was manufactured and the integration of more advanced components was proposed. The theoretical signal processing method used to detect hydration status was validated through initial tests with a prototype electrical system composed of a Teensy 3.1 board, two 18 kHz piezoceramic disc elements, and an Arduino/LabVIEW interface. Tests with aluminum, rubber, and sponge materials were performed to compare the signal response to propagation through materials with different acoustic properties and water contents. Finally, tests performed with dehydrated bovine muscle tissue revealed a statistically significant difference between hydrated and dehydrated tissue, a promising indication for future device refinement