5,493 research outputs found

    NOVEL MODELING, TESTING AND CONTROL APPROACHES TOWARDS ENERGY EFFICIENCY IMPROVEMENT IN PERMANENT MAGNET SYNCHRONOUS MOTOR AND DRIVE SYSTEMS

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    This thesis investigates energy efficiency improvement in permanent magnet synchronous motor (PMSM) and drive system to achieve high–performance drive for practical industrial and primarily, traction applications. In achieving improved energy efficiency from a system level, this thesis proposes: (1) Accurate modeling and testing of loss components in PMSM considering inverter harmonics; (2) Easy–to–implement, accurate parameter determination techniques to understand variations in motor parameters due to saturation, cross–saturation and temperature; and (3) Control methodologies to improve system level efficiency considering improved loss models and parameter variations. An improved loss model to incorporate the influence of motor–drive interaction on the motor losses is developed by taking time and space harmonics into account. An improved winding function theory incorporating armature reaction fields due to fundamental and harmonic stator magnetic fields is proposed to calculate the additional harmonic losses in the PMSM. Once all contributing losses in the motor are modelled accurately, an investigation into control variables that affect the losses in the motor and inverter is performed. Three major control variables such as DC link voltage, switching frequency and current angle are chosen and the individual losses in the motor and inverter as well as the system losses are studied under varying control variables and wide operating conditions. Since the proposed loss as well as efficiency modeling involves machine operation dependent parameters, the effects of parameter variation on PMSM due to saturation and temperature variation are investigated. A recursive least square (RLS) based multi–parameter estimation is proposed to identify all the varying parameters of the PMSM to improve the accuracy and validity of the proposed model. The impact of losses on these parameters as well as the correct output torque considering the losses are studied. Based on the proposed loss models, parameter variations and the investigation into control variables, an off–line loss minimization procedure is developed to take into account the effects of parameter variations. The search–based procedure generates optimal current angles at varying operating conditions by considering maximization of system efficiency as the objective. In order to further simplify the consideration of parameter variations in real–time conditions, an on–line loss minimization procedure using DC power measurement and loss models solved on–line using terminal measurements in a PMSM drive is proposed. A gradient descent search–based algorithm is used to calculate the optimal current angle corresponding to maximum system efficiency from the input DC power measurement and output power based on the loss models. During the thesis investigations, the proposed models and control techniques are extensively evaluated on a laboratory PMSM drive system under different speeds, load conditions, and temperatures

    MODERNIZATION OF THE MOCK CIRCULATORY LOOP: ADVANCED PHYSICAL MODELING, HIGH PERFORMANCE HARDWARE, AND INCORPORATION OF ANATOMICAL MODELS

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    A systemic mock circulatory loop plays a pivotal role as the in vitro assessment tool for left heart medical devices. The standard design employed by many research groups dates to the early 1970\u27s, and lacks the acuity needed for the advanced device designs currently being explored. The necessity to update the architecture of this in vitro tool has become apparent as the historical design fails to deliver the performance needed to simulate conditions and events that have been clinically identified as challenges for future device designs. In order to appropriately deliver the testing solution needed, a comprehensive evaluation of the functionality demanded must be understood. The resulting system is a fully automated systemic mock circulatory loop, inclusive of anatomical geometries at critical flow sections, and accompanying software tools to execute precise investigations of cardiac device performance. Delivering this complete testing solution will be achieved through three research aims: (1) Utilization of advanced physical modeling tools to develop a high fidelity computational model of the in vitro system. This model will enable control design of the logic that will govern the in vitro actuators, allow experimental settings to be evaluated prior to execution in the mock circulatory loop, and determination of system settings that replicate clinical patient data. (2) Deployment of a fully automated mock circulatory loop that allows for runtime control of all the settings needed to appropriately construct the conditions of interest. It is essential that the system is able to change set point on the fly; simulation of cardiovascular dynamics and event sequences require this functionality. The robustness of an automated system with incorporated closed loop control logic yields a mock circulatory loop with excellent reproducibility, which is essential for effective device evaluation. (3) Incorporating anatomical geometry at the critical device interfaces; ascending aorta and left atrium. These anatomies represent complex shapes; the flows present in these sections are complex and greatly affect device performance. Increasing the fidelity of the local flow fields at these interfaces delivers a more accurate representation of the device performance in vivo

    Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS) for the computational analyses of high speed reacting flows

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    The principal objective is to extend the boundaries within which large eddy simulations (LES) and direct numerical simulations (DNS) can be applied in computational analyses of high speed reacting flows. A summary of work accomplished during the last six months is presented

    Locating and extracting acoustic and neural signals

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    This dissertation presents innovate methodologies for locating, extracting, and separating multiple incoherent sound sources in three-dimensional (3D) space; and applications of the time reversal (TR) algorithm to pinpoint the hyper active neural activities inside the brain auditory structure that are correlated to the tinnitus pathology. Specifically, an acoustic modeling based method is developed for locating arbitrary and incoherent sound sources in 3D space in real time by using a minimal number of microphones, and the Point Source Separation (PSS) method is developed for extracting target signals from directly measured mixed signals. Combining these two approaches leads to a novel technology known as Blind Sources Localization and Separation (BSLS) that enables one to locate multiple incoherent sound signals in 3D space and separate original individual sources simultaneously, based on the directly measured mixed signals. These technologies have been validated through numerical simulations and experiments conducted in various non-ideal environments where there are non-negligible, unspecified sound reflections and reverberation as well as interferences from random background noise. Another innovation presented in this dissertation is concerned with applications of the TR algorithm to pinpoint the exact locations of hyper-active neurons in the brain auditory structure that are directly correlated to the tinnitus perception. Benchmark tests conducted on normal rats have confirmed the localization results provided by the TR algorithm. Results demonstrate that the spatial resolution of this source localization can be as high as the micrometer level. This high precision localization may lead to a paradigm shift in tinnitus diagnosis, which may in turn produce a more cost-effective treatment for tinnitus than any of the existing ones

    잔차 합성곱 신경망을 통한 산업용 로봇 기어박스의 동작 적응형 퓨샷 고장 감지 방법

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    학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 윤병동.Nowadays, industrial robots are indispensable equipment for automated manufacturing processes because they can perform repetitive tasks with consistent precision and accuracy. However, when faults occur in the industrial robot, it can lead to the unexpected shutdown of the production line, which brings significant economic losses, so the fault detection is important. The gearbox, one of the main drivetrain components of an industrial robot, is often subjected to high torque loads, and faults occur frequently. When faults occur in the gearbox, the amplitude and frequency of the torque signal are modulated, which leads to changes in the characteristics of the torque signal. Although several previous studies have proposed fault detection methods for industrial robots using torque signals, it is still a challenge to extract fault-related features under various environmental and operating conditions and to detect faults in the complex motions used in industrial sites To overcome such difficulties, in this paper, we propose a novel motion-adaptive few-shot (MAFS) fault detection method of industrial robot gearboxes using torque ripples via a one-dimensional (1D) residual-convolutional neural network (Res-CNN) and binary-supervised domain adaptation (BSDA). The overall procedure of the proposed method is as follows. First, applying the moving average filtering to the torque signal to extract the data trend, and the torque ripples of the high-frequency band are obtained as a residual value between the original signal and the filtered signal. Second, classifying the state of pre-processed torque ripples under various operating and environmental conditions. It is shown that Res-CNN network 1) distinguishes small differences between normal and fault torque ripples effectively, and 2) focuses on important regions of the input data by the attention effect. Third, after constructing the Siamese network with a pre-trained network in the source domain, which consisted of simple motions, detecting the faults on the target domain, which consisted of complex motions through BSDA. As a result, 1) the similarities of the jointly shared physical mechanisms of torque ripples between simple and complex motions are learned, and 2) faults of the gearbox are adaptively detected while the industrial robot executes complex motions. The proposed method showed the most superior accuracy over other deep learning-based methods in few-shot conditions where only one cycle of each normal and fault data of complex motions is available. In addition, the transferable regions on the torque ripples after domain adaptation was highlighted using 1D guided grad-CAM. The effectiveness of the proposed method was validated with experimental data of multi-axial welding motions in constant and transient speed, which are commonly executed in real-industrial fields such as the automobile manufacturing line. Furthermore, it is expected that the proposed method is applicable to other types of motions, such as inspection, painting, assembly, and so on. The source code is available on my GitHub page of https://github.com/oyt9306/MAFS.Chapter 1. Introduction 1 1.1 Research Motivation 1 1.2 Scope of Research 4 1.3 Thesis Layout 5 Chapter 2. Research Backgrounds 6 2.1 Interpretations of Torque Ripples 6 2.1.1. Causes of torque ripples 6 2.1.1. Modulations on torque ripples due to gearbox faults 8 2.2 Architectures of Res-CNN 11 2.2.1 Convolutional Operation 11 2.2.2 Pooling Operation 12 2.2.3 Activation 13 2.2.4 Batch Normalization 13 2.2.5 Residual Learning 15 2.3 Domain Adaptation (DA) 17 2.3.1 Few-shot domain adaptation 18 Chapter 3. Motion-Adaptive Few-Shot (MAFS) Fault Detection Method 20 3.1 Pre-processing 23 3.2 Network Pre-training 28 3.3 Binary-Supervised Domain Adaptation (BSDA) 31 Chapter 4. Experimental Validations 37 4.1 Experimental Settings 37 4.2 Pre-trained Network Generation 40 4.3 Motion-Adaptation with Few-Shot Learning 43 Chapter 5. Conclusion and Future Work 52 5.1 Conclusion 52 5.2 Contribution 52 5.3 Future Work 54 Bibliography 55 Appendix A. 1D Guided Grad-CAM 60 국문 초록 62Maste

    Nonlinear factor analysis and its application to acoustical source separation and identification

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    Acoustical signals of mechanical systems can provide original information of operating conditions, and thus benefit for machinery condition monitoring and fault diagnosis. However, acoustical signals measured by sensors are mixed signals of all the sources, and normally it is impossible to be directly used for acoustical source identification or feature extraction. Therefore, this paper presents nonlinear factor analysis (NLFA) and applies it to acoustical source separation and identification of mechanical systems. The effects by numbers of hidden neurons and mixed signals on separation performances of NLFA are comparatively studied. Furthermore, acoustical signals from a test bed with shell structures are separated and identified by NLFA and correlation analysis, and the effectiveness of NLFA on acoustical signals is validated by both numerical case studies and an experimental case study. This work can benefit for machinery noise monitoring, reduction and control, and also provide pure source information for machinery condition monitoring or fault diagnosis

    Stratégies de gestion d’énergie pour véhicules électriques et hybride avec systèmes hybride de stockage d’énergie

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    Les véhicules électriques et hybrides font partie des éléments clés pour résoudre les problèmes de réchauffement de la planète et d'épuisement des ressources en combustibles fossiles dans le domaine du transporte. En raison des limites des différents systèmes de stockage et de conversion d’énergie en termes de puissance et d'énergie, les hybridations sont intéressantes pour les véhicules électriques (VE). Dans cette thèse, deux hybridations typiques sont étudiées • un sous-système de stockage d'énergie hybride combinant des batteries et des supercondensateurs (SC) ; • et un sous-système de traction hybride parallèle combinant moteur à combustion interne et entraînement électrique. Ces sources d'énergie et ces conversions combinées doivent être gérées dans le cadre de stratégies de gestion de l'énergie (SGE). Parmi celles-ci, les méthodes basées sur l'optimisation présentent un intérêt en raison de leur approche systématique et de leurs performances élevées. Néanmoins, ces méthodes sont souvent compliquées et demandent beaucoup de temps de calcul, ce qui peut être difficile à réaliser dans des applications réelles. L'objectif de cette thèse est de développer des SGE simples mais efficaces basées sur l'optimisation en temps réel pour un VE et un camion à traction hybride parallèle alimentés par des batteries et des SC (système de stockage hybride). Les complexités du système étudié sont réduites en utilisant la représentation macroscopique énergétique (REM). La REM permet de réaliser des modèles réduits pour la gestion de l'énergie au niveau de la supervision. La théorie du contrôle optimal est ensuite appliquée à ces modèles réduits pour réaliser des SGE en temps réel. Ces stratégies sont basées sur des réductions de modèle appropriées, mais elles sont systématiques et performantes. Les performances des SGE proposées sont vérifiées en simulation par comparaison avec l’optimum théorique (programmation dynamique). De plus, les capacités en temps réel des SGE développées sont validées via des expériences en « hardware-in-the-loop » à puissances réduites. Les résultats confirment les avantages des stratégies proposées développées par l'approche unifiée de la thèse.Abstract: Electric and hybrid vehicles are among the keys to solve the problems of global warming and exhausted fossil fuel resources in transportation sector. Due to the limits of energy sources and energy converters in terms of power and energy, hybridizations are of interest for future electrified vehicles. Two typical hybridizations are studied in this thesis: • hybrid energy storage subsystem combining batteries and supercapacitors (SCs); and • hybrid traction subsystem combining internal combustion engine and electric drive. Such combined energy sources and converters must be handled by energy management strategies (EMSs). In which, optimization-based methods are of interest due to their high performance. Nonetheless, these methods are often complicated and computation consuming which can be difficult to be realized in real-world applications. The objective of this thesis is to develop simple but effective real-time optimization-based EMSs for an electric car and a parallel hybrid truck supplied by batteries and SCs. The complexities of the studied system are tackled by using Energetic Macroscopic Representation (EMR) which helps to conduct reduced models for energy management at the supervisory level. Optimal control theory is then applied to these reduced models to accomplish real-time EMSs. These strategies are simple due to the suitable model reductions but systematic and high-performance due to the optimization-based methods. The performances of the proposed strategies are verified via simulations by comparing with off-line optimal benchmark deduced by dynamic programming. Moreover, real-time capabilities of these novel EMSs are validated via experiments by using reduced-scale power hardware-in-the-loop simulation. The results confirm the advantages of the proposed strategies developed by the unified approach in the thesis

    Number of sources estimation using a hybrid algorithm for smart antenna

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    The number of sources estimation is one of the vital key technologies in smart antenna. The current paper adopts a new system that employs a hybrid algorithm of artificial bee colony (ABC) and complex generalized Hebbian (CGHA) neural network to Bayesian information criterion (BIC) technique, aiming to enhance the accuracy of number of sources estimation. The advantage of the new system is that no need to compute the covariance matrix, since its principal eigenvalues are computed using the CGHA neural network for the received signals. Moreover, the proposed system can optimize the training condition of the CGHA neural network, therefore it can overcome the random selection of initial weights and learning rate, which evades network oscillation and trapping into local solution. Simulation results of the offered system show good responses through reducing the required time to train the CGHA neural network, fast converge speed, effectiveness, in addition to achieving the correct number of sources

    Smart FRP Composite Sandwich Bridge Decks in Cold Regions

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    INE/AUTC 12.0

    Thermal breakage of window glass in room fires conditions - Analysis of some important parameters

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    In a compartment fire, the breakage and possible fallout of a window glass has a significant impact on the fire dynamics. The thermal breakage of glass depends on various parameters such as glass type, edge shading, edges conditions and constraints on the glass. The purpose of the present study is to investigate some of the key parameters affecting the thermal breakage of window glass in fire conditions using a recently developed and validated computer tool. Fallout is not within the scope of this study. Different boundary conditions of the glass pane (unconstrained and constrained) subjected to fire radiant heat are investigated. The analysis shows that to prevent glass thermal breakage, it is important to provide enough spacing between the frame and glass pane to accommodate the thermal expansion, and constraints on the glass structure should be avoided. The zones where the glass is likely to crack first are shown. The study also quantifies the effects of glass edge conditions on its thermal breakage in fire conditions; such analysis has not been reported in the literature due to its complexity and the statistical nature of edge flaws. The results show that an ordinary float glass mostly used in windows, with the “as-cut” edge condition would break later and is stronger than a ground edge or polished edge glass for the scenarios investigated. The study demonstrates how a predictive tool could be employed for a better understanding of thermal breakage of window glass in fires and for design guidance
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