Novel Approaches for Nondestructive Testing and Evaluation

Nondestructive testing and evaluation (NDT&E) is one of the most important techniques for determining the quality and safety of materials, components, devices, and structures. NDT&E technologies include ultrasonic testing (UT), magnetic particle testing (MT), magnetic flux leakage testing (MFLT), eddy current testing (ECT), radiation testing (RT), penetrant testing (PT), and visual testing (VT), and these are widely used throughout the modern industry. However, some NDT processes, such as those for cleaning specimens and removing paint, cause environmental pollution and must only be considered in limited environments (time, space, and sensor selection). Thus, NDT&E is classified as a typical 3D (dirty, dangerous, and difficult) job. In addition, NDT operators judge the presence of damage based on experience and subjective judgment, so in some cases, a flaw may not be detected during the test. Therefore, to obtain clearer test results, a means for the operator to determine flaws more easily should be provided. In addition, the test results should be organized systemically in order to identify the cause of the abnormality in the test specimen and to identify the progress of the damage quantitatively

Novel Materials and Devices for Terahertz Detection and Emission for Sensing, Imaging and Communication

Technical advancement is required to attain a high data transmission rate, which entails expanding beyond the currently available bandwidth and establishing a new standard for the highest data rates, which mandates a higher frequency range and larger bandwidth. The THz spectrum (0.1-10 THz) has been considered as an emerging next frontier for the future 5G and beyond technology. THz frequencies also offer unique characteristics, such as penetrating most dielectric materials like fabric, plastic, and leather, making them appealing for imaging and sensing applications. Therefore, employing a high-power room temperature, tunable THz emitters, and a high responsivity THz detector is essential. Dyakonov-theory Shur\u27s was applied in this dissertation to achieve tunable THz detection and emission by plasma waves in high carrier density channels of field-effect devices. The first major contribution of this dissertation is developing graphene-based THz plasmonics detector with high responsivity. An upside-down free-standing graphene in a field effect transistor based resonant room temperature THz detector device with significantly improved mobility and gate control has been presented. The highest achieved responsivity is ~3.1kV/W, which is more than 10 times higher than any THz detector reported till now. The active region is predominantly single-layer graphene with multi-grains, even though the fabricated graphene THz detector has the highest responsivity. The challenges encountered during the fabrication and measurement of the graphene-based detector have been described, along with a strategy to overcome them while preserving high graphene mobility. In our new design, a monolayer of hBN underneath the graphene layer has been deposited to increase the mobility and electron concentration rate further. We also investigated the diamond-based FETs for their potential characteristics as a THz emitters and detectors. Diamond\u27s wide bandgap, high breakdown field, and high thermal conductivity attributes make it a potential semiconductor material for high voltage, high power, and high-temperature operation. Diamond is a good choice for THz and sub-THz applications because of its high optical phonon scattering and high momentum relaxation time. Numerical and analytical studies of diamond materials, including p-diamond and n-diamond materials, are presented, indicating their effectiveness as a prospective contender for high temperature and high power-based terahertz applications These detectors are expected to be a strong competitor for future THz on-chip applications due to their high sensitivity, low noise, tunability, compact size, mobility, faster response time, room temperature operation, and lower cost. Furthermore, when plasma wave instabilities are induced with the proper biasing, the same devices can be employed as THz emitters, which are expected to have a higher emission power. Another key contribution is developing a method for detecting counterfeit, damaged, forged, or defective ICs has been devised utilizing a new non-destructive and unobtrusive terahertz testing approach to address the crucial point of hardware cybersecurity and system reliability. The response of MMICs, VLSI, and ULSIC to incident terahertz and sub-terahertz radiation at the circuit pins are measured and analyzed using deep learning. More sophisticated terahertz response profiles and signatures of specific ICs can be created by measuring a more significant number of pins under different frequencies, polarizations, and depth of focus. The proposed method has no effect on ICs operation and could provide precise ICs signatures. The classification process between the secure and unsecure ICs images has been explained using data augmentation and transfer learning-based convolution neural network with ~98% accuracy. A planar nanomatryoshka type core-shell resonator with hybrid toroidal moments is shown both experimentally and analytically, allowing unique characteristics to be explored. This resonator may be utilized for accurate sensing, immunobiosensing, quick switching, narrow-band filters, and other applications

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

CNN-LSTM-based prognostics of bidirectional converters for electric vehicles’ machine

This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.Peer ReviewedPostprint (published version

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

[EN] Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.K Barrera-Llanga appreciates the financial support of the Secretary of Higher Education, Science, Technology and Innovation of Ecuador as a personal sponsor entity.Barrera-Llanga, K.; Burriel-Valencia, J.; Sapena-Bano, A.; Martinez-Roman, J. (2023). A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors. Sensors. 23(19):1-20. https://doi.org/10.3390/s23198196120231

Vision-based Monitoring System for High Quality TIG Welding

The current study evaluates an automatic system for real-time arc welding quality assessment and defect detection. The system research focuses on the identification of defects that may arise during the welding process by analysing the occurrence of any changes in the visible spectrum of the weld pool and the surrounding area. Currently, the state-of-the-art is very simplistic, involving an operator observing the process continuously. The operator assessment is subjective, and the criteria of acceptance based solely on operator observations can change over time due to the fatigue leading to incorrect classification. Variations in the weld pool are the initial result of the chosen welding parameters and torch position and at the same time the very first indication of the resulting weld quality. The system investigated in this research study consists of a camera used to record the welding process and a processing unit which analyse the frames giving an indication of the quality expected. The categorisation is achieved by employing artificial neural networks and correlating the weld pool appearance with the resulting quality. Six categories denote the resulting quality of a weld for stainless steel and aluminium. The models use images to learn the correlation between the aspect of the weld pool and the surrounding area and the state of the weld as denoted by the six categories, similar to a welder categorisation. Therefore the models learn the probability distribution of images’ aspect over the categories considered

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles

In recent years deep-learning techniques have been developed and applied to inspect cracks in RC structures. The accuracy of these techniques leads to believe that they may also be applied to the identification of other pathologies. This article proposes a technique for automated detection of exposed steel rebars. The tools developed rely on convolutional neural networks (CNNs) based on transfer-learning using AlexNet. Experiments were conducted in large-scale structures to assess the efficiency of the method. To circumvent limitations on the proximity access to structures as large as the ones used in the experiments, as well as increase cost efficiency, the image capture was performed using an unmanned aerial system (UAS). The final goal of the proposed methodology is to generate orthomosaic maps of the pathologies or structure 3D models with superimposed pathologies. The results obtained are promising, confirming the high adaptability of CNN based methodologies for structural inspection.This work was financially supported by: Base Funding - UIDB/04708/2020 and Programmatic Funding - UIDP/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções funded by national funds through the FCT/MCTES (PIDDAC). Additionally, the author Rafael Cabral acknowledges the support provided by the doctoral grant UI/BD/150970/2021 - Portuguese Science Foundation, FCT/MCTES.info:eu-repo/semantics/publishedVersio

Bayesian convolutional neural networks for RUL prognostics of solenoid valves with uncertainty estimations

Solenoid valves (SV) are essential components of industrial systems and therefore widely used. As they suffer from high failure rates in the field, fault prognosis of these assets plays a major role for improving their maintenance and reliability. In this work, Bayesian convolutional neural networks are used to predict the remaining useful life (RUL) of SVs, by training them on the valve's current signatures. Predictive performance is further improved upon by using salient physical features obtained from an electromechanical model as the network's training input. Results show that our designed network architecture produces well-calibrated uncertainty estimations of the RUL predictive distributions, which is an important concern in prognostic decision-making