17,022 research outputs found

    Characterization of Power Transformer Frequency Response Signature using Finite Element Analysis

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    Power transformers are a vital link in electrical transmission and distribution networks. Monitoring and diagnostic techniques are essential to decrease maintenance and improve the reliability of the equipment.This research has developed a novel, versatile, reliable and robust technique for modelling high frequency power transformers. The purpose of this modelling is to enable engineers to conduct sensitivity analyses of FRA in the course of evaluating mechanical defects of power transformer windings. The importance of this new development is that it can be applied successfully to industry transformers of real geometries

    Using Landsat Images to Determine Water Storing Capacity in Mediterranean Environments

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    Reservoirs play an important role in water management and are key elements for water supply. Monitoring is needed in order to guarantee the quantity and quality of stored water. However, this task is sometimes not easy. The objective of this study was to develop a procedure for predicting volume of stored water with remote sensing in water bodies under Mediterranean climate conditions. To achieve this objective,multispectral Landsat 7 and 8 images (NASA) were analyzed for the following five reservoirs: La Serena,La Pedrera, Beniarrés, Cubillas and Negratín (Spain). Reservoirs water surface was computed with the spectral angle mapper (SAM) algorithm.After that, cross-validation regression models were computed in order to assess the capability of water surface estimations to predict stored water in each of the reservoirs. The statistical models were trained with Landsat 7 images and were validated by using Landsat 8 images. Our results suggest a good capability of water volume prediction from free satellite imagery derived from surface water estimations. Combining free remote sensing images and open source GIS algorithms can be a very useful tool for water management and an integrated and efficient way to control water storage,especially in low accessible sites

    Kernel Based Model Parametrization and Adaptation with Applications to Battery Management Systems.

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    With the wide spread use of energy storage systems, battery state of health (SOH) monitoring has become one of the most crucial challenges in power and energy research, as SOH significantly affects the performance and life cycle of batteries as well as the systems they are interacting with. Identifying the SOH and adapting of the battery energy/power management system accordingly are thus two important challenges for applications such as electric vehicles, smart buildings and hybrid power systems. This dissertation focuses on the identification of lithium ion battery capacity fading, and proposes an on-board implementable model parametrization and adaptation framework for SOH monitoring. Both parametric and non-parametric approaches that are based on kernel functions are explored for the modeling of battery charging data and aging signature extraction. A unified parametric open circuit voltage model is first developed to improve the accuracy of battery state estimation. Several analytical and numerical methods are then investigated for the non-parametric modeling of battery data, among which the support vector regression (SVR) algorithm is shown to be the most robust and consistent approach with respect to data sizes and ranges. For data collected on LiFePO4 cells, it is shown that the model developed with the SVR approach is able to predict the battery capacity fading with less than 2% error. Moreover, motivated by the initial success of applying kernel based modeling methods for battery SOH monitoring, this dissertation further exploits the parametric SVR representation for real-time battery characterization supported by test data. Through the study of the invariant properties of the support vectors, a kernel based model parametrization and adaptation framework is developed. The high dimensional optimization problem in the learning algorithm could be reformulated as a parameter estimation problem, that can be solved by standard estimation algorithms such as the least-squares method, using a SVR special parametrization. The resulting framework uses the advantages of both parametric and non-parametric methods to model nonlinear dynamics, and greatly reduces the required effort in model development and on-board computation. The robustness and effectiveness of the developed methods are validated using both single cell and multi-cell module data.PhDNaval Architecture and Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116688/1/chsweng_1.pd

    Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

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    BACKGROUND: Turning is a challenging mobility task requiring coordination and postural stability. Optimal turning involves a cranio-caudal sequence (i.e., the head initiates the motion, followed by the trunk and the pelvis), which has been shown to be altered in patients with neurodegenerative diseases, such as Parkinson's disease as well as in fallers and frails. Previous studies have suggested that the cranio-caudal sequence exhibits a specific signature corresponding to the adopted turn strategy. Currently, the assessment of cranio-caudal sequence is limited to biomechanical labs which use camera-based systems; however, there is a growing trend to assess human kinematics with wearable sensors, such as attitude and heading reference systems (AHRS), which enable recording of raw inertial signals (acceleration and angular velocity) from which the orientation of the platform is estimated. In order to enhance the comprehension of complex processes, such as turning, signal modeling can be performed. AIM: The current study investigates the use of a kinematic-based model, the sigma-lognormal model, to characterize the turn cranio-caudal signature as assessed with AHRS. METHODS: Sixteen asymptomatic adults (mean age = 69.1 +/- 7.5 years old) performed repeated 10-m Timed-Up-and-Go (TUG) with 180 degrees turns, at varying speed. Head and trunk kinematics were assessed with AHRS positioned on each segments. Relative orientation of the head to the trunk was then computed for each trial and relative angular velocity profile was derived for the turn phase. Peak relative angle (variable) and relative velocity profiles modeled using a sigma-lognormal approach (variables: Neuromuscular command amplitudes and timing parameters) were used to extract and characterize the cranio-caudal signature of each individual during the turn phase. RESULTS: The methodology has shown good ability to reconstruct the cranio-caudal signature (signal-to-noise median of 17.7). All variables were robust to speed variations (p > 0.124). Peak relative angle and commanded amplitudes demonstrated moderate to strong reliability (ICC between 0.640 and 0.808). CONCLUSION: The cranio-caudal signature assessed with the sigma-lognormal model appears to be a promising avenue to assess the efficiency of turns

    Pharmacokinetics and biotransformation of biopharmaceuticals:by liquid chromatography with unit-mass and high-resolution mass spectrometric detection

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    Unlike the current situation for small-molecule drugs, the biotransformation of biopharmaceuticals is a largely unexplored field. Although much attention is paid to (the prevention of) degradation and structural alteration of protein-based drugs in pharmaceutical formulations, almost nothing is known about what happens to such a drug once it is dosed to a patient. An important reason for this is the fact that it is virtually impossible to investigate biotransformation using LBAs, because they typically cannot distinguish unchanged and biotransformed versions of the drug. With the increasing use of LC-MS/MS for protein quantification, it is now becoming more and more evident that in vivo chemical and enzymatic reactions of biopharmaceuticals are very common. Pharmacologically, biotransformation may affect the activity of a protein drug and, from an analytical perspective, it can also have a large influence on the concentration result that is reported.If we look at biopharmaceutical analysis from a more technical and instrumental point of view. So far, most protein LC-MS methods are being performed using triple-quadrupole mass spectrometry after sample digestion and further sample processing. This type of mass spectrometry has unit-mass resolution and its use for protein quantification essentially is an extension of the typical approach for small-molecule analysis. Very little is known about the quantitative possibilities of other high-resolution mass spectrometry (HRMS) approaches for biopharmaceuticals. HRMS is extensively used for qualitative purposes, such as the structural elucidation or confirmation of both small and large molecules, because of its high mass accuracy, but it also offers the option for quantitative analysis and extensive data re-processing. It can thus be used as an alternative detection technique for digested protein analysis with improved selectivity compared to unit-mass MS and it also is capable of quantifying intact proteins, which is virtually impossible on triple-quadrupole instrumentation

    Vibroacoustic transformer condition monitoring

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    Throughout the life of a transformer the effects of mechanical shocks, insulation aging, thermal processes and short circuit forces will cause deformations in the winding. This deformation can lead to vibration in the transformer and mechanical fatigue of the solid insulation. Defects which form in a transformers structure can cause faults such as partial discharge, hot spots and arcing. These faults generate combustible gases which can be analysed for condition assessment of the transformer. The development of a suitable and cost effective vibration measurement system forms a key part of this research project. A monitoring system is developed for real-time vibration analysis. An embedded capacitive accelerometer is used in conjunction with an Arduino microcontroller to record vibrations. The sensor platform is designed to communicate wirelessly via XBee radios to a terminal computer. A software program and user interface is designed as a tool for analysis. The outcomes and benefits of these works are primarily based on determining the condition of transformer insulation through measurements of vibration. Following a working measurement system, suitable transformer sites are monitored. Spectral analysis is performed in the frequency domain to determine a correlation with gas analysis results. The validity of vibroacoustic measurement as a predictive maintenance tool is subsequently evaluated. Six transformers are chosen for vibration monitoring with analysis of the vibration signatures correlated to the dissolved gas analysis reports at each site. The vibration signatures at each location are analysed using the Short Time Fourier Transform and frequency peaks compared for the different sites. It was noted that sensor location does not have a large impact on vibration magnitudes and identifying the frequency components present in the signal. However, from the signatures obtained there is not enough variation in magnitude or frequency components to suggest that this method can identify the type of fault present

    JUNO Conceptual Design Report

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    The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4σ\sigma, and determine neutrino oscillation parameters sin2θ12\sin^2\theta_{12}, Δm212\Delta m^2_{21}, and Δmee2|\Delta m^2_{ee}| to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. \sim17,000 508-mm diameter PMTs with high quantum efficiency provide \sim75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.Comment: 328 pages, 211 figure

    Health Monitoring of LAV Planet Gear Bushings using Signature Analysis Techniques

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    The Center for Integrated Manufacturing Studies (CIMS) is studying the improvement of military Light Armored Vehicles (LAVs) for the United States Department of Defense. A focus of this study is the Marine Corps LAVs that are experiencing failures in the planetary assembly which serves as the vehicle\u27s final drive system. The primary failure source is the bushings that provide the interface between the planet gears and their respective pins. Currently, to detect a bushing failure, vehicle occupants must exit the LAV and place their hand on the wheel hub cover to check for excessive heat. If the hub feels too hot, travel must stop so the planetary assembly can cool down. These overheating wheel hubs can lead to catastrophic failure of the planetary assembly. Therefore, CIMS is working to analyze these bushing failures and develop a method that will allow occupants to detect potential bushing failures from inside the moving vehicle. In the past, the relationship of pin-bushing interface temperature and wear showed that temperature does not indicate bushing failure soon enough for practical implementation. It was the intention of this current wear study to evaluate bushing failures using vibration signatures as part of an effort to develop failure prognostic tools for (future) in-service use. This thesis was conducted as a feasibility assessment study to evaluate bushing failure from a vibration and signal processing standpoint. Accelero meters were used to collect vibration data from the bushings. Collected vibration signatures were analyzed and examined as bushing wear progressed to determine whether or not remaining bushing life could be predicted using vibration signatures. Vibration data was analyzed from an energy standpoint; that is, the band power was calculated for several frequency bands of interest. Band power was plotted versus bushing wear to reveal any potential relationship between the two. Test results showed that a direct, linear relationship exists between bushing wear and band power in the 2000 to 2100 Hz frequency range. The results of this thesis suggest that vibration data can be used to identify the severity bushing wear. Since this investigation was conducted as a feasibility assessment, additional work is required before this wear detection method can be implemented on an actual LAV. It is recommended that similar bushing wear-vibration studies be conducted where bushings are tested on the Mustang dynamometer (at CIMS) and then on an actual LAV
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