The Internet Based real-time GPS synchronized wide-area Frequency Monitoring Network (FNET) is an extremely low cost and quickly deployable wide-area frequency measurement system with high dynamic accuracy which consists of Frequency Disturbance Recorder (FDR) distributed to more than 100 places around North America and an Information Management System situated at Virginia Tech. Since its first FDR deployment in 2003, the FNET system has been proved to be able to reliably receive phasor data accurately measured at and instantaneously sent via the Internet from different locations of interest, and efficiently run the analyzing program to detect and record significant system disturbances and subsequently estimate the location of disturbance center, namely the event location, in the electric grid based on the information gathered. The excellent performance of the FNET system so far has made power grid situation awareness and monitoring based on distribution level frequency measurements a reality, and thus advances our understanding of power system dynamics to a higher level and in a broader dimensionality. Chapter 1 and Chapter 2 of this dissertation briefly introduce the genesis and the architecture of the FNET system, followed by a summary of its concrete implementations. Chapter 3 and Chapter 4 outline FNET frequency estimation algorithm and phase angle estimation algorithm, including their attributes and the new methodologies to enhance them. In Chapter 5, the report discusses the algorithms developed at FNET to detect the frequency disturbance and estimate the disturbance location by the triangulation procedure using real-time frequency data and geographic topology of the FNET units in the power grid where the disturbance occurs. Then, the dissertation proceeds to introduce the FNET angle-based power system oscillation detection and present some research about Matrix Pencil Modal Analysis of FNET phase angle oscillation data in the following two chapters. Lastly, the content of this report is summarized and the future work envisioned in Chapter 8. / Ph. D.

Yes / Voltage instability problems in power system is an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know when and where the voltage stability problem can occur in order to apply suitable action to avoid unexpected results. In this paper, a study has been conducted to identify the weakest bus in the power system based on multi-variable control, modal analysis, and Singular Value Decomposition (SVD) techniques for both static and dynamic voltage stability analysis. A typical IEEE 3-machine, 9-bus test power system is used to validate these techniques, for which the test results are presented and discussed.

The precise and detailed characterization of the dynamic response of structures has become increasingly important in recent years. As a consequence, the accuracy of experimental data, which is often used to validate and update finite element models, has become extremely important. However, as researchers have attempted to identify and quantify sources of error in the experimental modal analysis (EMA) process, an important potential error source has been largely overlooked. Instabilities in the dynamic response of structures due to small variations in test environmental conditions may result in significant errors in experimental and analytical results, leading to erroneous and/or misleading conclusions. This thesis presents an experimental and analytical investigation of the effects of ambient temperature variations on the dynamic characteristics of a thin, square steel plate. The modal properties of the plate with two different boundary conditions and at temperatures above and below standard room temperature are examined. In addition, an analytical model is developed accounting for the effects of temperature-dependent material properties. Results indicate that natural frequencies and damping are significantly affected by changes in temperature. In the case of the natural frequency variations, the temperature-dependence of Young's modulus is the dominant factor, but boundary condition effects may also be important. Also, FRF magnitudes at spectral lines close to the resonances are very sensitive to temperature. Finally, only minor variations in the plate response shapes are observed, although significant changes in the imaginary component of the velocity field are evident. / Master of Science

This thesis discusses the experimental modal analysis work done on a reciprocating Freon compressor. The primary goal of this work was to aid in the development of a dynamic finite element model for the compressor. The crankcase, the compressor shell, and the entire compressor were each studied individually so that the characteristics of each component could be determined separately. For each of the tested elements, a modal survey was done followed by the determination, with the use of a laser, of the forced frequency response shapes associated with each resonance. These shapes, along with the associated frequencies, were compared with the results from the finite element analysis model to determine if the model needed to be updated. The crankcase was also tested to determine if a rigid-body assumption would be valid for the purpose of force analysis. This study resulted in the experimental data that could be used for comparison with the finite element model results. In general, the forced frequency response shapes could be matched to the finite element mode shapes up to 1400 Hz for the empty shell and assembled compressor, 2000 for the crankcase. Also, there were several conclusions that resulted from this study. These included acceptance of the rigid-body assumption for purposes of the force analysis, the need for a further look at the dynamic variations between individual compressors, and a suggestion to move the suspension mounts to the narrow side from the broad side. / Master of Science

Electric propulsion motors have emerged as a promising solution to address greenhouse gas emissions from Internal Combustion Engines (ICEs). While electric propulsion motors offer numerous advantages over Internal Combustion Engines (ICEs), they also pose certain challenges. Electric motors are prone to high-frequency tonal noise, which can be annoying to customers and become a quality concern in noise-sensitive automotive applications. The ongoing effort to increase the speed of electric propulsion motors for enhanced power density can have an adverse impact on rotors. This is due to the fact that the stress induced in the rotor is quadratically proportional to its speed. This concern becomes particularly significant for motors that rely on air barriers and thin bridges to enhance their electromagnetic performance. The thesis makes a contribution to address these challenges. First, the acoustic noise mitigation methods at the transmission stage are investigated. Then, acoustical materials are experimentally validated for their capacity to mitigate acoustic noise at the transmission stage. Then, experimental modal analysis is conducted to find out the modal characteristics of a stator-housing assembly. The mode shapes and modal frequency are compared with finite element results to evaluate the fidelity of the finite element model. Then, an equivalent damage approach is used to employ accelerated fatigue analysis for a rotor using constant amplitude load cycles. Finally, a thermomechanical fatigue analysis workflow is developed for a rotor to overcome the limitations of the constant amplitude load cycle approach, with an additional computational cost. / Thesis / Doctor of Philosophy (PhD)

This dissertation focuses on nonlinear modal interactions in plates. Our first investigation involved the activation of a two-to-one internal resonance in the response of a metallic cantilever plate. Although the plate was excited around the frequency of its second bending mode, its response contained a contribution from its first torsional mode. The frequency ratio between the bending and torsional modes was nearly two-to-one. Next, we investigated the energy transfer from high-frequency to low-frequency modes in a cantilever graphite-epoxy composite plate (90/30/ — 30/ — 30/30/90)_s. The plate was excited around the natural frequency of its seventh (third torsional) mode. For some excitation amplitudes and frequencies, we observed the activation of a low-frequency (first bending) mode accompanied by an amplitude and phase modulation of the seventh mode. We studied combination resonances in the responses of cantilever composite plates with the layups (90/30/ — 30/ — 30/30/90)_s and (—75/75/75/ — 75/75/ — 75)_s to harmonic base excitations. We activated the combination resonance f_e≈ ω₂ + ω₇ in the (90/30/ — 30/ — 30/30/90)_s plate, where the w; are the natural frequencies of the plate and f<sub>e<sub> is the excitation frequency. In the (—75/75/75/ — 75/75/ — 75)_s plate, we activated the external combination resonance f<sub>e<sub>≈ 1/2(ω₂+ω₅) and the combination internal resonance f_e≈1/2(ω₂+ω₁₃) ≈ ω₈. We carried out an experimental-modal analysis (EMA) of a nonclassically supported plate with and without a constrained-layer damping (CLD) patch attached on its upper left-hand side surface. The natural frequencies and mode shapes were used to ascertain the effect of the CLD patch. / Ph. D.

Advancements in high-throughput technologies have led to an exponential increase in the generation of multi-modal data in computational biology. These datasets, comprising diverse biological measurements such as genomics, transcriptomics, proteomics, metabolomics, and imaging data, offer a comprehensive view of biological systems at various levels of complexity. However, integrating and analyzing such heterogeneous data present significant challenges due to differences in data modalities, scales, and noise levels. Another challenge for multi-modal analysis is the complex interaction network that the modalities share. Understanding the intricate interplay between different biological modalities is essential for unraveling the underlying mechanisms of complex biological processes, including disease pathogenesis, drug response, and cellular function. Machine learning algorithms have emerged as indispensable tools for studying multi-modal data in computational biology, enabling researchers to extract meaningful insights, identify biomarkers, and predict biological outcomes. In this dissertation, we first propose a multi-modal integration framework that takes two interconnected data modalities and their interaction network to iteratively update the modalities into new representations with better disease outcome predictive abilities. The deep learning-based model underscores the importance and performance gains achieved through the incorporation of network information into integration process. Additionally, a multi-modal framework is developed to estimate protein expression from mRNA and microRNA (miRNA) expressions, along with the mRNA-miRNA interaction network. The proposed network propagation model simulates in-vivo miRNA regulation on mRNA translation, offering a cost-effective alternative to experimental protein quantification. Analysis reveals that predicted protein expression exhibits a stronger correlation with ground truth protein expression compared to mRNA expression. Moreover, the effectiveness of integrative models is contingent upon the quality of input data modalities and the completeness of interaction networks, with missing values and network noise adversely affecting downstream tasks. To address these challenges, two multi-modal imputation models are proposed, facilitating the imputation of missing values in time series data. The first model allows the imputation of missing values in time series gene expression utilizing single nucleotide polymorphism (SNP) data for children at high risk of type 1 diabetes. The imputed gene expression allows us to predict the progression towards type 1 diabetes at birth with six years prediction horizon. Subsequently, a follow-up study introduces a generalized multi-modal imputation framework capable of imputing missing values in time series data using either another time series or cross-sectional data collected from the same set of samples. These models excel at imputation tasks, whether values are missing randomly or an entire time step in the series is absent. Additionally, leveraging the additional modality, they are able to estimate a completely missing time series without prior values. Finally, to mitigate noise in the interaction network, a link prediction framework for drug-target interaction prediction is developed. This study demonstrates exceptional performance in cold start predictions and investigates the efficacy of large language models for such predictions. Through a comprehensive review and evaluation of state-of-the-art algorithms, this dissertation aims to provide researchers with valuable insights, methodologies, and tools for harnessing the rich information embedded within multi-modal biological datasets.

L’identification des paramètres modaux des machines-outils et des robots d’usinage, en service, constitue un levier d’optimisation des performances de coupe. En effet, la connaissance en continue du comportement dynamique d’une machine permet une prédiction fine des conditions de stabilité, bases d’un pilotage intelligent des paramètres du procédé. Cependant, la présence de fortes excitations harmoniques, dues à la rotation de la broche et de l’outil coupant, rend les techniques classiques d’Analyse Modale Opérationnelle (AMO) inapplicables. Le premier objectif de cette thèse consiste à déterminer une méthode d’AMO adéquate pour une application en présence des harmoniques. Une étude comparative des méthodes existantes est conduite, à savoir : la méthode de décomposition dans le domaine fréquentiel (EFDD), la méthode d’identification dans le sous-espace stochastique (SSI), la méthode PolyMAX et la méthode basée sur la fonction de transmissibilité (TFB). La méthode TFB est choisie afin de réaliser une identification modale opérationnelle des robots d’usinage. Cette technique est tout d’abord investiguée sur une machine-outil cartésienne. Cette étape est justifiée par le fait qu’une machine-outil est une structure plus rigide qui présente moins de variations des propriétés dynamiques par rapport à un robot d’usinage. Les résultats montrent la pertinence de la méthode TFB pour identifier les paramètres modaux de la machine-outil en usinage, même en présence des composantes harmoniques fortement dominantes. Ensuite, l’identification modale opérationnelle du robot d’usinage ABB IRB 6660, qui présente une structure moins rigide par rapport à une machine-outil, est menée sur une trajectoire d’usinage. Les résultats obtenus permettent d’établir une base modale du robot montrant l’évolution de son comportement modal en service. L’originalité des travaux présentés réside dans le développement d’une procédure robuste d’identification modale opérationnelle qui permet de suivre l’évolution du comportement modal du robot en cours d’usinage dans son espace de travail. / The identification of the modal parameters of machining robots in service has a significant adverse influence on machining stability, which will, therefore, decrease the quality of the workpiece and reduce the tool life. However, in presence of strong harmonic excitation, the application of Operational Modal Analysis (OMA) is not straightforward. Firstly, the issue of choosing the most appropiate OMA method for an application in presence of harmonic components, is handled. For a comparison purpose, the modified Enhanced Frequency Domain Decomposition (EFDD) method, the Stochastic Subspace Identification (SSI) method, the PolyMAX method and the Transmissibility Function Based (TFB) method are investigated. The obtained results lead to the adoption of the Transmissibility Function Based (TFB) method for an OMA of machining robots. For an accurate modal identification procedure, the OMA of a machine tool is, initially, conducted. It is a preparation step in order to verify the performance of the chosen method under machining conditions as well as a machine tool is a rigid structure, thus, it has less variation in its dynamic behavior compared to a machining robot. Results demonstrate the efficiency of the TFB method to identify the machine tool modal parameters even in the presence of preponderant harmonic components. Finally, the OMA of the machining robot ABB IRB 6660, which has a flexible structure compared to a machine tool, is carried out for a machining trajectory. The obtained results allow the identification of a modal basis of the machining robot illustrating the evolution of its modal behavior, in service. The main novelty of this thesis lies in the development of a robust procedure for an operational modal identification of machining robots, in service, which makes it possible to continuously follow the variations in the modal parameters of machining robots.

La robotisation des procédés d’usinage suscite l’intérêt des industriels en raison du grand espace de travail et le faible coût des robots par rapport aux machines-outils conventionnelles et la possibilité d’usiner des pièces de formes complexes. Cependant, la faible rigidité de la structure robotique favorise le déclenchement de phénomènes dynamiques liés à l’usinage sollicitant le robot en bout de l’outil qui dégradent la qualité de surface de la pièce usinée. L’objectif de ces travaux de thèse est de caractériser le comportement dynamique des robots en usinage. Ces travaux ont suivi une démarche en trois étapes : La modélisation d’un premier modèle considéré de référence où le robot est au repos. Ensuite l’identification du comportement dynamique du robot en service. Enfin, l’exploitation des modèles dynamiques du robot en vue de prédire la stabilité de coupe. L’originalité de ces travaux porte sur le développement des méthodes d’identification modale opérationnelles. Elles permettent d’intégrer les conditions réelles d’usinage et d’élaborer des modèles plus précis que le premier modèle de connaissance sans être biaisés par l’effet des harmoniques de rotation de l’outil. Enfin, des préconisations sur le choix de configurations du robot et sur la direction des forces d’excitation sont proposées pour assurer la stabilité de la coupe lors de l’usinage robotisé. / Machining robots have major advantages over cartesian machine tools because of their flexibility, their ability to reach inaccessible areas on a complex part, and their important workspace. However, their lack of rigidity and precision is still a limit for precision tasks. The stresses generated by the cutting forces and inertia are important and cause static and dynamic deformations of the structure which result in problems of workpiece surface. The aim of the thesis work is to characterize the dynamic behavior of robots during machining operation. This work followed a three-step approach : Modeling a first model considered as a reference where the robot is at rest. Then the identification of the dynamic behavior in service. Finally, the prediction of the cutting stability using the robot dynamic model. The originality of this work is the development of new operational modal identification methods. They integrate the machining conditions and result into a more accurate model than the first model of reference without being biased by harmonics. Finally, guidlines of robot’s configurations and excitation forces’ direction are proposed to ensure the robotic machining stability.

Thesis deals with determination of layerwise mechanical properties of composite ceramics by indirect method, namely Youngs modulus. Based on literature review, it was found that a method to determine elastic properties of one or more components of multi layered composites based on experimental modal analysis and finite element modal analysis, or analytical approach exists. Method based on FE modal analysis was applied to ceramic laminate, where it was attempt to determine youngs modulus of one component. Beyond that, it was attempt to determine Youngs moduli of both components using first two bending resonant frequencies. Results were unsatisfying. Sensitivity analysis showed that layers with unknown Youngs modulus were overly sensitive to small changes in input parameters due to their small relative thickness with respect to thickness of laminate and due to location in laminate. Based on this conclusion, recommendations were made with respect to suitable geometry of test specimens.