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Ayland, Nicholas D. "Automatic vehicle identification for road traffic monitoring." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254395.
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Fernandes, Winnie Cezario. "Thrips on roses: identification, monitoring and chemical control." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14048.
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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgicoThe growth in the production of ornamental plants is increasingly significant in Brazil and in the Northeast region, but the occurrence of pests is shown as a limiting factor. To minimize losses, adequate control measures should be employed. Accordingly, the correct identification of pests, population monitoring and studies on managements should be performed. The objective of this study was to identify thrips species in rose, characterize and quantify the damage loss caused by arthropod pests in the production of roses in Serra da Ibiapaba; to assess the fluctuation of thrips species in ten cultivars of rose, at different stages of flower development and monitoring systems, and; evaluate the efficiency of pesticides on Frankliniella spp. The experiments were conducted at the Company âReijers ProduÃÃo de Rosasâ, SÃo Benedito, Cearà State, âLagoa Jussaraâ in planting roses in greenhouses. Three species of thrips have been identified: Frankliniella schultzei (Trybom, 1910), F. occidentalis (Pergande, 1895) and Caliothrips phaseoli (Pergande, 1825) (Thysanoptera: Thripidae) with the largest recorded infestations for F. occidentalis and F. schultzei in phenological phases of roses, especially in flowering. The injury caused by thrips in floral cut roses button affected the quality invalidating them for marketing. There was no difference between the sampling periods (morning and afternoon) and sampling (tray beat and direct view of the floral button) to the ten cultivars of roses, so the choice of the time and method must be reconciled with practicality and cost. The insecticides demonstrated ability to cause mortality of thrips in extreme conditions, within completely enclosed structures (flower buds).
O crescimento na produÃÃo de plantas ornamentais à cada vez mais significativo no Brasil e na regiÃo Nordeste do paÃs, porÃm a ocorrÃncia de pragas mostra-se como fator limitante. Para minimizar as perdas, medidas adequadas de controle devem ser empregadas. Nesse sentido, a identificaÃÃo correta das pragas, seu monitoramento populacional e estudos sobre manejos devem ser realizados. O objetivo deste estudo foi identificar espÃcies de tripes em roseira, caracterizar danos e quantificar as perdas ocasionadas pelo artrÃpode-praga na produÃÃo de rosas na Serra da Ibiapaba; avaliar a flutuaÃÃo populacional das espÃcies de tripes em dez cultivares de roseira, em diferentes fases do desenvolvimento floral e sistemas de monitoramento, e; avaliar a eficiÃncia de produtos fitossanitÃrios sobre Frankliniella spp. Os experimentos foram conduzidos na Empresa Reijers ProduÃÃo de Rosas, Unidade SÃo Benedito/CE, Fazenda Lagoa Jussara, em plantio de roseiras sob cultivo protegido. Foram identificadas trÃs espÃcies de tripes: Frankliniella schultzei (Trybom, 1910), F. occidentalis (Pergande, 1895) e Caliothrips phaseoli (Pergande, 1825) (Thysanoptera: Thripidae) sendo as maiores infestaÃÃes registradas para F. occidentalis e F. schultzei nas diferentes fases fenolÃgicas das roseiras, especialmente na floraÃÃo. As injÃrias causadas pelos tripes no botÃo floral de rosas de corte afetaram aqualidade inviabilizando-as para a comercializaÃÃo. NÃo houve diferenÃa estatÃstica entre os perÃodos de amostragem (manhà e tarde) e os mÃtodos de amostragem (batida de bandeja e visualizaÃÃo direta do botÃo floral) para as dez cultivares de roseiras, assim a escolha do horÃrio e do mÃtodo devem ser conciliadascom praticidade e custo.Os inseticidas demonstraram capacidade de causar mortalidade de tripes em condiÃÃes extremas, ou seja, dentro de estruturas completamente fechadas (botÃes florais).
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Eriksson, Martin. "Monitoring, Modelling and Identification of Data Center Servers." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69342.
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Energy efficient control of server rooms in modern data centers can help reducing the energy usage of this fast growing industry. Efficient control, however, cannot be achieved without: i) continuously monitoring in real-time the behaviour of the basic thermal nodes within these infras- tructures, i.e., the servers; ii) analyzing the acquired data to model the thermal dynamics within the data center. Accurate data and accurate models are indeed instrumental for implementing efficient data centers cooling strategies. In this thesis we focus on Open Compute Servers, a class of servers designed in an open-source fashion and used by big players like Facebook. We thus propose a set of appropriate methods for collecting real-time data from these platforms and a dedicated thermal model describing the thermal dynamics of the CPUs and RAMs of these servers as a function of both controllable and non-controllable inputs (e.g., the CPU utilization levels and the air mass flow of the server’s fans). We also identify this model from real data and provide the results so to be reusable by other researchers.
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Zhang, Yi 1973. "Multi-channel blind system identification for central hemodynamic monitoring." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29622.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.Includes bibliographical references (leaves 89-91).
Multi-channel Blind System Identification (MBSI) is a technique for estimating both an unknown input and unknown channel dynamics from simultaneous output measurements at different channels through which the input signal propagates. It is a powerful tool particularly for the identification and estimation of dynamical systems in which a sensor, for measuring the input, is difficult to place. All of the existing MBSI algorithms, however, are not applicable to multi-channel systems sharing common dynamics among the channels, since these algorithms, by nature, exploit "differences" among the multiple channel dynamics. This requirement renders the MBSI algorithms useless in systems that have both a lumped-parameter nature and a distributed nature; all channels in a system of this type share poles dictated by the lumped-parameter dynamics. To overcome this difficulty, this thesis investigates a new approach, Intermediate Input Identification (IIID). This thesis proves that the distinct dynamics in each channel can be identified up to a scalar factor even when common dynamics are present. Based on this discovery, the MBSI problem is reformulated and an intermediate input is introduced, which integrates the original system input and the common dynamics shared by all the channels. The two-step IIID approach is developed to solve the problem: first, the distinct dynamics are identified from the outputs; second, the common dynamics are identified from the intermediate input by exploiting the zero-input response of the system. The identifiability conditions are thoroughly investigated. The sufficient and necessary conditions and the relationship between the linear-complexity condition of the original input and that of the intermediate input are derived in this thesis.
(cont.) This thesis also develops a central hemodynamic monitoring scheme based on IIID. The similarities between the structure of a digital wireless communication system and that of the cardiovascular system are explained. The input, the common dynamics and the distinct dynamics in the cardiovascular multi-channel system are derived based on the determinants of arterial blood pressure. Analysis of the data from a cardiovascular simulator and animal experiments verify the validity of this scheme. The positive results demonstrate that the IIID approach could open up the possibility for noninvasive central hemodynamic monitoring, which could significantly reduce the risks to which patients are exposed.
by Yi Zhang.
Ph.D.
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Bisht, Saurabh Singh. "Vibration Measurement Based Damage Identification for Structural Health Monitoring." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/77301.
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The focus of this research is on the development of vibration response-based damage detection in civil engineering structures. Modal parameter-based and model identification-based approaches have been considered. In the modal parameter-based approach, the flexibility and curvature flexibility matrices of the structure are used to identify the damage. It is shown that changes in these matrices can be related to changes in stiffness values of individual structural members. Using this relationship, a method is proposed to solve for the change in stiffness values. The application of this approach is demonstrated on the benchmark problem developed by the joint International Association of Structural Control and American Society of Civil Engineers Structural Health Monitoring task group. The proposed approach is found to be effective in identifying various damage scenarios of this benchmark problem. The effect of missing modes on the damage identification scheme is also studied.
The second method for damage identification aims at identifying sudden changes in stiffness for real time applications. It is shown that the high-frequency content of the response acceleration can be used to identify the instant at which a structure suffers a sudden reduction in its stiffness value. Using the Gibb's phenomenon, it is shown why a high-pass filter can be used for identifying such damages. The application of high-pass filters is then shown in identifying sudden stiffness changes in a linear multi-degree-of-freedom system and a bilinear single degree of freedom system. The impact of measurement noise on the identification approach is also studied. The noise characteristics under which damage identification can or cannot be made are clearly identified. The issue of quantification of the stiffness reduction by this approach is also examined. It is noted that even if the time at which the reduction in stiffness happens can be identified, the quantification of damage requires the knowledge of system displacement values. In principle, such displacements can be calculated by numerical integration of the acceleration response, but the numerical integrations are known to suffer from the low frequency drift error problems. To avoid the errors introduced due to numerical integration of the acceleration response, an approach utilizing the unscented Kalman filter is developed to track the sudden changes in stiffness values. This approach is referred to as the adaptive unscented Kalman filter (AUKF) approach. The successful application of the proposed AUKF approach is shown on two multi-degree of freedom systems that experience sudden loss of stiffness values while subjected to earthquake induced base excitation.Ph. D.
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Sakki, Kranthi Kumar. "A Radio Frequency Identification Multi-Sensor Health Monitoring System." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262351.
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Health Monitoring Systems (HMS) are used to monitor physiological signals such as the blood pressure, heart rate, and temperature of patients. The use of a HMS for continuous monitoring of the Vital Signs of patients requiring constant medical supervision, is particularly important. The current project presents the development and implementation of a multi-sensor HMS to track and record multiple parameters of a patient (Electrocardiogram, pulse, temperature, and body position). The project development uses biomedical sensor technology for monitoring the physiological signals, Radio Frequency Identification (RFID) technology for patient identification, and the Internet of Things (IoT) for information transmission. Sensors attached to a patient’s body collect data that alert users to abnormal values via smart devices, such as mobile phones or laptops. Experimental testing of the multi-sensor HMS developed and implemented for this project, demonstrates the system’s effectiveness in sensing, collecting, and transmitting accurate patient information for remote monitoring.
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Elbadawy, Mohamed Mohamed Zeinelabdin Mohamed. "Dynamic Strain Measurement Based Damage Identification for Structural Health Monitoring." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86167.
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Structural Health Monitoring (SHM) is a non-destructive evaluation tool that assesses the functionality of structural systems that are used in the civil, mechanical and aerospace engineering practices. A much desirable objective of a SHM system is to provide a continuous monitoring service at a minimal cost with ability to identify problems even in inaccessible structural components. In this dissertation, several such approaches that utilize the measured dynamic response of structural systems are presented to detect, locate, and quantify the damages that are likely to occur in structures. In this study, the structural damage is identified as a reduction in the stiffness characteristics of the structural elements. The primary focus of this study is on the utilization of measured dynamic strains for damage identification in the framed structures which are composed of interconnected beam elements. Although linear accelerations, being more convenient to measure, are commonly used in most SHM practices, herein the strains being more sensitive to elemental damage are considered. Two different approaches are investigated and proposed to identify the structural element stiffness properties. Both approaches are mode-based, requiring first the identification of system modes from the measured strain responses followed by the identification of the element stiffness coefficients. The first approach utilizes the Eigen equation of the finite element model of the structure, while the second approach utilizes the changes caused by the damage in the structural curvature flexibilities. To reduce size of the system which is primarily determined by the number of sensors deployed for the dynamic data collection, measurement sensitivity-based sensor selection criterion is observed to be effective and thus used. The mean square values of the measurements with respect to the stiffness coefficients of the structural elements are used as the effective measures of the measurement sensitivities at different sensor locations. Numerical simulations are used to evaluate the proposed identification approaches as well as to validate the sensitivity-based optimal sensor deployment approach.Ph. D.
All modern societies depend heavily on civil infrastructure systems such as transportation systems, power generation and transmission systems, and data communication systems for their day-to-day activities and survival. It has become extremely important that these systems are constantly watched and maintained to ensure their functionality. All these infrastructure systems utilize structural systems of different forms such as buildings, bridges, airplanes, data communication towers, etc. that carry the service and environmental loads that are imposed on them. These structural systems deteriorate over time because of natural material degradation. They can also get damaged due to excessive load demands and unknown construction deficiencies. It is necessary that condition of these structural systems is known at all times to maintain their functionality and to avoid sudden breakdowns and associated ensuing problems. This condition assessment of structural systems, now commonly known as structural health monitoring, is commonly done by visual onsite inspections manually performed at pre-decided time intervals such as on monthly and yearly basis. The length of this inspection time interval usually depends on the relative importance of the structure towards the functionality of the larger infrastructure system. This manual inspection can be highly time and resource consuming, and often ineffective in catching structural defects that are inaccessible and those that occur in between the scheduled inspection times and dates. However, the development of new sensors, new instrumentation techniques, and large data transfer and processing methods now make it possible to do this structural health monitoring on a continuous basis. The primary objective of this study is to utilize the measured dynamic or time varying strains on structural components such as beams, columns and other structural members to detect the location and level of a damage in one or more structural elements before they become serious. This detection can be done on a continuous basis by analyzing the available strain response data. This approach is expected to be especially helpful in alerting the owner of a structure by identifying the iv occurrence of a damage, if any, immediately after an unanticipated occurrence of a natural event such as a strong earthquake or a damaging wind storm.
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Appler, Jason A. Finney Sean M. McMellon Michael A. "Aerial remote radio frequency identification system for small vessel monitoring." Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/MBAPR/2009/Dec/09Dec%5FAppler%5FMBA.pdf.
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"Submitted in partial fulfillment of the requirements for the degree of Master of Business Administration from the Naval Postgraduate School, December 2009."Advisor(s): Dew, Nicholas ; Hudgens, Bryan. "December 2009." "MBA Professional report"--Cover. Description based on title screen as viewed on January 26, 2010. Author(s) subject terms: RFID, Radio Frequency Identification, airborne, vessel monitoring. Includes bibliographical references (p. 103-110). Also available in print.
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Jiang, Bing. "Ubiquitous monitoring of distributed infrastructures /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/6118.
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Bakhary, Norhisham. "Structural condition monitoring and damage identification with artificial neural network." University of Western Australia. School of Civil and Resource Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0102.
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Many methods have been developed and studied to detect damage through the change of dynamic response of a structure. Due to its capability to recognize pattern and to correlate non-linear and non-unique problem, Artificial Neural Networks (ANN) have received increasing attention for use in detecting damage in structures based on vibration modal parameters. Most successful works reported in the application of ANN for damage detection are limited to numerical examples and small controlled experimental examples only. This is because of the two main constraints for its practical application in detecting damage in real structures. They are: 1) the inevitable existence of uncertainties in vibration measurement data and finite element modeling of the structure, which may lead to erroneous prediction of structural conditions; and 2) enormous computational effort required to reliably train an ANN model when it involves structures with many degrees of freedom. Therefore, most applications of ANN in damage detection are limited to structure systems with a small number of degrees of freedom and quite significant damage levels. In this thesis, a probabilistic ANN model is proposed to include into consideration the uncertainties in finite element model and measured data. Rossenblueth's point estimate method is used to reduce the calculations in training and testing the probabilistic ANN model. The accuracy of the probabilistic model is verified by Monte Carlo simulations. Using the probabilistic ANN model, the statistics of the stiffness parameters can be predicted which are used to calculate the probability of damage existence (PDE) in each structural member. The reliability and efficiency of this method is demonstrated using both numerical and experimental examples. In addition, a parametric study is carried out to investigate the sensitivity of the proposed method to different damage levels and to different uncertainty levels. As an ANN model requires enormous computational effort in training the ANN model when the number of degrees of freedom is relatively large, a substructuring approach employing multi-stage ANN is proposed to tackle the problem. Through this method, a structure is divided to several substructures and each substructure is assessed separately with independently trained ANN model for the substructure. Once the damaged substructures are identified, second-stage ANN models are trained for these substructures to identify the damage locations and severities of the structural ii element in the substructures. Both the numerical and experimental examples are used to demonstrate the probabilistic multi-stage ANN methods. It is found that this substructuring ANN approach greatly reduces the computational effort while increasing the damage detectability because fine element mesh can be used. It is also found that the probabilistic model gives better damage identification than the deterministic approach. A sensitivity analysis is also conducted to investigate the effect of substructure size, support condition and different uncertainty levels on the damage detectability of the proposed method. The results demonstrated that the detectibility level of the proposed method is independent of the structure type, but dependent on the boundary condition, substructure size and uncertainty level.
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Webster, Janelle T. "Individual identification, disease monitoring and home range of Leiopelma hamiltoni." Thesis, University of Canterbury. Biological Sciences, 2004. http://hdl.handle.net/10092/1454.
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Amphibian populations are declining on a global scale and although disease outbreaks are a commonly accepted hypothesis they are not the only one. My aims for my thesis were to study the home range of Leiopelma hamiltoni, to determine whether a photographic database could be used to individual identified them and monitor the health status of the population. Habitat loss is a possible cause. For this reason monitoring an animals' home range is a possible method to detect early impacts the population is facing. By tracking 12 L. hamiltoni within a 12 m x 6 m grid on Maud Island, it was shown that the home range size can vary from 0.5 m2 to 25 m2 based on the minimum convex polygon method. However, to track multiple individuals it is important to be able to distinguish among frogs. The commonly used methods of identification, such as toe clipping, pose potentially detrimental effects. Therefore, non-invasive methods based on natural markings need to be established. Through the use of the dark pigmented patterns found on the skin of L. hamiltoni individuals can be identified on recapture with a mean accuracy of 93%. By developing a database to maintain the photographs used for individual identification, the database can also be used to monitor the status of the population. During 2003 numerous L. hamiltoni were observed with denuded patches predominantly on the facial region. By monitoring five individuals within the captive facility at the University of Canterbury it was discovered that frogs appear to be able to cure themselves. Through researching the home range requirements and developing a photographic database to monitor the population status of L. hamiltoni, it will aid in the management of ensuring the long-term survival of this archaic species of frog.
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He, Xianfei. "Vibration-based damage identification and health monitoring of civil structures." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3289036.
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Thesis (Ph. D.)--University of California, San Diego, 2008.Title from first page of PDF file (viewed February 5, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Appler, Jason A., Michael A. McMellon, and Sean M. Finney. "Aerial remote radio frequency identification system for small vessel monitoring." Monterey, California. Naval Postgraduate School, 2009. http://hdl.handle.net/10945/10384.
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MBA Professional ReportApproved for public release, distribution unlimited
MBA Professional Report
This MBA Professional Report proves the feasibility of using aircraft mounted RFID antennas to detect commercially available Radio Frequency Identification (RFID) tags affixed to small vessels. The project was conducted because monitoring small vessels in U.S. coastal and inland waters is considered a gap in homeland security, as well as problematic for marine resource managers tasked with enforcing sanctuary and fishing regulations. The premises of the project are that 1) RFID tags are less invasive and more cost effective than other current methods of proposed monitoring, 2) airborne platforms can monitor areas of interest faster and more efficiently than surface based monitoring systems, and 3) small vessel registration numbers can be electronically associated with the serial number of the affixed RFID tag. The cost of tagging each vessel is low (around $50 per vessel), and the tag number of any vessel could be read remotely from 0.3 to 0.5 nautical miles away. The agency reading the tag would be able to retrieve the associated vessel registration information from a national database through a back-end data-link system. This system could improve coastal and port security by providing remote monitoring of real-time vessel location information, and could enable improvements in resource management methods by enabling correlation of location and identification data for recreational vessels engaged in natural resource use.
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Gökçe, Hasan Burak. "Structural identification through monitoring, modeling and predictive analysis under uncertainty." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5222.
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Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure's response and performance in a probabilistic manner. The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric St-Id approach is employed without the use of an analytical model.; It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels.; The new methodology, which is successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions.ID: 031001436; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Adviser: F. Necati ?çatba?ƒ.; Title from PDF title page (viewed June 24, 2013).; Thesis (Ph.D.)--University of Central Florida, 2012.; Includes bibliographical references (p. 173-187).
Ph.D.
Doctorate
Civil, Environmental, and Construction Engineering
Engineering and Computer Science
Civil Engineering
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Hahn, Jin-Oh Ph D. Massachusetts Institute of Technology. "A system identification approach to non-invasive central cardiovascular monitoring." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45337.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (leaves 180-187).
This thesis presents a new system identification approach to non-invasive central cardiovascular monitoring problem. For this objective, this thesis will develop and analyze blind system identification and input signal reconstruction algorithms for a class of 2-channel IIR and Wiener systems. In particular, this thesis will present blind identifiability conditions for a class of 2-channel IIR and Wiener wave propagation systems and develop the associated blind identification algorithms. It will be shown that the blind identifiability conditions can be achieved in many real-world applications by appropriate selection of channel lengths, sensor locations, and sampling frequency which are the specifications that the system design can exploit for blind identifiability In addition, this thesis will develop a novel input signal reconstruction algorithm that is applicable to general class of multi-channel IIR and Wiener systems. Furthermore, this thesis will rigorously analyze and evaluate three analytic measures for determining the system order and other key parameters of the black-box dynamics as well as for quantifying the quality of the identified gray-box dynamics, without any direct use of unknown input signal: persistent excitation, model identifiability and asymptotic variance. The blind identification and input signal reconstruction algorithms will first be applied to 2-sensor central cardiovascular monitoring problem using two distinct peripheral blood pressure measurements, where the cardiovascular wave propagation dynamics is blindly identified and the aortic blood pressure and flow signals are reconstructed by exploiting black-box and physics-based gray-box model structures of the cardiovascular system.
(cont.) The validity of the 2-sensor central cardiovascular monitoring methodology will be illustrated by experimental data from swine subjects and simulation data from a full-scale human cardiovascular simulator across diverse physiologic conditions. The 2-sensor central cardiovascular monitoring methodology will then be extended to address noninvasive, 1-sensor cardiovascular monitoring problem, where the specific challenges involved are 1) identifying the cardiovascular wave propagation dynamics and reconstructing the aortic blood pressure signal by exploiting the measurement from a single peripheral sensor, and 2) identifying the scale for calibrating the blood pressure signal. In order to address these challenges, this thesis will propose a heuristics-based system order estimation algorithm and a model-based blood pressure calibration algorithm, which will be combined with the blind identification of the cardiovascular wave propagation dynamics to realize the non-invasive 1-sensor central cardiovascular monitoring. The non-invasive 1-sensor central cardiovascular monitoring methodology will be illustrated by experimental data from swine subjects, simulation data from a full-scale human cardiovascular simulator, and experimental data from human subjects across diverse physiologic conditions.
by Jin-Oh Hahn.
Ph.D.
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SANTOS, Adam Dreyton Ferreira dos. "Output-only methods for damage identification in structural health monitoring." Universidade Federal do Pará, 2017. http://repositorio.ufpa.br/jspui/handle/2011/9076.
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CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
No campo da monitorização de integridade estrutural (SHM), a identificação de dano baseada em vibração tem se tornado uma área de pesquisa crucial devido a sua potencial aplicação em estruturas de engenharia do mundo real. Assumindo que os sinais de vibração podem ser medidos pelo emprego de diferentes tipos de sistemas de monitorização, quando aplica-se o tratamento de dados adequado, as características sensíveis a dano podem ser extraídas e usadas para avaliar dano estrutural incipiente ou progressivo. Entretanto, as estruturas do mundo real estão sujeitas às mudanças regulares nas condições operacionais e ambientais (e.g., temperatura, umidade relativa, massa de tráfego e outros), as quais impõem dificuldades na identificação do dano estrutural uma vez que essas mudanças influenciam diferentes características de forma distinta. Nesta tese por agregação de artigos, a fim de superar essa limitação, novos métodos output-only são propostos para detectar e quantificar dano em estruturas sob influências operacionais e ambientais não medidas. Os métodos são baseados nos campos de aprendizagem de máquina e inteligência artificial e podem ser classificados como técnicas baseadas em kernel e clusterização. Quando os novos métodos são comparados àqueles do estado da arte, os resultados demonstraram que os primeiros possuem melhor performance de detecção de dano em termos de indicações de dano falso-positivas (variando entre 3,6Ű5,4%) e falso-negativas (variando entre 0Ű2,6%), sugerindo potencial aplicabilidade em soluções práticas de SHM. Se os métodos propostos são comparados entre si, aqueles baseados em clusterização, nomeadamente as abordagens de expectância-maximização global via algoritmos meméticos, provaram ser as melhores técnicas para aprender a condição estrutural normal, sem perda de informação ou sensibilidade aos parâmetros iniciais, e para detectar dano (erros totais iguais a 4,4%).
In the structural health monitoring (SHM) field, vibration-based damage identification has become a crucial research area due to its potential to be applied in real-world engineering structures. Assuming that the vibration signals can be measured by employing different types of monitoring systems, when one applies appropriate data treatment, damage-sensitive features can be extracted and used to assess early and progressive structural damage. However, real-world structures are subjected to regular changes in operational and environmental conditions (e.g., temperature, relative humidity, traffic loading and so on) which impose difficulties to identify structural damage as these changes influence different features in a distinguish manner. In this thesis by papers, to overcome this drawback, novel output-only methods are proposed for detecting and quantifying damage on structures under unmeasured operational and environmental influences. The methods are based on the machine learning and artificial intelligence fields and can be classified as kernel- and cluster-based techniques. When the novel methods are compared to the state-of-the-art ones, the results demonstrated that the former ones have better damage detection performance in terms of false-positive (ranging between 3.65.4%) and false-negative (ranging between 0-2.6%) indications of damage, suggesting their applicability for real-world SHM solutions. If the proposed methods are compared to each other, the cluster-based ones, namely the global expectation-maximization approaches based on memetic algorithms, proved to be the best techniques to learn the normal structural condition, without loss of information or sensitivity to the initial parameters, and to detect damage (total errors equal to 4.4%).
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Agustin, Rebecca A. "A load identification and diagnostic framework for aggregate power monitoring." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130678.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 85-88).
Power monitoring solutions have the potential to collect large amounts of data from the operation of electromechanical loads, such as measurements of power, torque, vibration, and acoustic signals. These measurements can act as unique identifiers for the early identification of degrading system performance, providing a rich feature space for fault detection and diagnostics (FDD). However, mainstream machine learning methods may overlook potential features with key physical context in the development of soft faults due to a lack of faulty load data in publicly available datasets. Therefore, a physically informed feature space must be selected and evaluated specifically for FDD applications in which load behaviors evolve over time. This thesis presents both a method for evaluating a potential load disaggregation feature space and a framework for load classification based on adaptive load benchmarks and health tracking.
by Rebecca A. Agustin.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Oliver, William A. "Monitoring Software and Charged Particle Identification for the CLAS12 Detector." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6031.
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The CEBAF Large Acceptance Spectrometer for the 12 GeV era, known as CLAS12, uses the time of flight (TOF) system to identify charged particles from scattering events between the beam and target. The TOF system is divided into two parts: The Forward time of flight system, and the Central time of flight system. These two sub-systems subtend different polar angles of the detector geometry for wide acceptance of scattered particles. Reconstruction is the service used to identify particles from the interactions between the beam and target, called as a vertex or the point where the interaction occurs. The vertex position is traced back using the tracking system and the TOF system. The resolution of the detector affects the accuracy of the reconstructed vertex location. This paper’s goal will be to develop software for validation suite for CLAS12, which will include central and forward tracking plots. Plots will be developed to check the precision of the reconstructed vertices in both the central and forward detectors. This will be done assuming a target with zero dimension at 𝑣𝑧 = 0, and an extended target of 5 cm at 𝑣𝑧 = 0. This paper will also look at the TOF resolution, and identify particles using the TOF detectors and the effect of the vertex correction on the velocity vs. momentum plots.
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Jhinaoui, Ahmed. "Subspace-based identification and vibration monitoring algorithms for rotating systems." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S161.
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Les méthodes d'identification dites sous-espace sont largement utilisées pour la caractérisation des modes propres et la surveillance des structures mécaniques. Elles ont fait leurs preuves pour les systèmes dont la dynamique est invariante dans le temps. Elles ne sont, toutefois, pas adaptées à des systèmes à rotors comme les hélicoptères et les éoliennes qui, de part leurs parties tournantes, sont périodiques dans le temps. Le but de cette thèse est d'étendre le champ d'application de ces méthodes à cette classe particulière de systèmes. Tout d'abord, un algorithme qui permet d'identifier certaine structure modale, dite de Floquet, est proposée. Ensuite, une étude de sensibilité est réalisée dans le but de quantifier les incertitudes, liées aux bruits ou à d'autres facteurs, sur les paramètres modaux identifiés. Enfin et partant de l'algorithme d'identification, une méthode de détection d'instabilité est développée. Cette méthode est basée sur la définition d'un résidu, fonction des paramètres modaux, et la surveillance d'un changement éventuel de ce résidu qui correspond à une déviation vers un régime instable. Ces méthodes ont été appliquées à des modèles numériques et à des données expérimentalesSubspace identification methods are widely used for caracterizing modal param-eters and for vibration monitoring of mechanical structures. They were shown powerful for the so-called linear time-invariant systems. However, they are not adapted to rotating sys-tems such as helicopters and wind turbines, which are inherently time-periodic systems. The goal of this thesis is to extend the applicability of these methods to this particular class of systems. First, a new identification algorithm is suggested. This algorithm permits to iden-tify the so-called Floquet modal structure. Then, a sensitivity study is conducted in order to quantify uncertainties, related to noises and other sources, about the identified modal param-eters. Finally and based on the suggested identification algorithm, a method for instability detection is developed. The main feature of this method is to define some residual, which is function of modal parameters, then to detect an eventual change over it which means a possible deviation toward an unstable regime. The suggested methods were applied to both numerical and experimental data
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Holliday, Derrick Michael John. "On-line tests for parameter identification in cage induction machines." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/1344.
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Quintana, i. Badosa Guillem. "Stability lobes diagram identification and surface roughness monitoring in milling processes." Doctoral thesis, Universitat de Girona, 2010. http://hdl.handle.net/10803/7769.
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La millora de la productivitat i la qualitat són indubtablement dues de les principals exigències del sector productiu modern i factors clau per la competitivitat i la supervivència. Dins aquest sector,la fabricació per arrancada de material juga encara avui en dia un paper protagonista tot i l'aparició de noves tècniques de conformat per addició.Indústries com l'aeronàutica, l'automobilística,la del motlle o l'energètica, depenen en bona part de les prestacions de les màquines-eina. Aquesta Tesi aborda dos aspectes rellevants quan es tracta de millorar de la productivitat i la qualitat del sector productiu: el problema del fimbrament, més conegut per la denominació anglosaxona chatter,i la monitorització de la rugositat superficial en el mecanitzat a alta velocitat.Productivity and quality improvement are undoubtedly two of the main demands of the
modern manufacturing sector and key factors for competitiveness and survival. Within this sector, material removal processes play, still nowadays, a principal role despite the emergence of additive manufacturing techniques. Industries such as aerospace, automotive, molds and dies or energy largely depend on machine tools performance for improved productivity and quality. This Thesis is focused on two important aspects when it comes to improving productivity and quality of the manufacturing sector: chatter problem, and surface roughness monitoring in high speed milling.
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Gbenga, Ogungbuyi Michael. "Identification and monitoring of oil pipeline spill fire using space applications." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29877.
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Oil pipeline spills in the Niger Delta cause a great deal of environmental damage to sensitive ecosystems and losses of many millions of dollars to the Nigerian economy every year. These spills occur along the routes of pipeline infrastructure and other oil facilities like flowlines, trunk lines, flow stations, barges, well heads etc. The causes of these spill events include: operational or maintenance error, ageing oil facilities, as well as acts of deliberate sabotage of the pipeline equipment which often result in explosions and fire outbreaks. In this project, we have investigated whether satellite observations could be used to detect these oil pipeline fires. The Nigerian National Oil Spill Detection and Response Agency (NOSDRA) database contains a total of 10 072 oil spill reports from 2007 to 2015. The space-based approach we considered in this dissertation included the use of data gathered by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites, which recorded 85 129 active fire hotspots in the Niger Delta from 2007 to 2015. Since the oil spill reports serve as validation data for these oil spill fires, we explored the capability of the MODIS instrument to study the spatio-temporal correlation between spills and fire events by attempting to investigate whether the largest spills by volume that resulted in fires could be detected from space in near-real time. Although the NOSDRA oil spill reports are plagued with several irregularities from the Joint Investigation Visits by the joint task force who visit spill sites, our approach in this dissertation automated the filtering process of the raw database to meet our research goal and objective. This study confirms that, indeed, fires resulting from oil spills are detectable using the MODIS fire products. For 43 of the largest spill events, we were able to establish a spatio-temporal correlation of spill incident reports with MODIS fires clearly associated with the oil pipeline infrastructure. Our study also shed light on the spatial and temporal characteristics of non-pipeline fires in the study area.
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Xing, Shutao. "Structural Identification and Damage Identification using Output-Only Vibration Measurements." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/1067.
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This dissertation studied the structural identification and damage detection of civil engineering structures. Several issues regarding structural health monitoring were addressed.
The data-driven subspace identification algorithm was investigated for modal identification of bridges using output-only data. This algorithm was tested through a numerical truss bridge with abrupt damage as well as a real concrete highway bridge with actual measurements. Stabilization diagrams were used to analyze the identified results and determine the modal characteristics. The identification results showed that this identification method is quite effective and accurate.
The influence of temperature fluctuation on the frequencies of a highway concrete bridge was investigated using ambient vibration data over a one-year period of a highway bridge under health monitoring. The data were fitted by nonlinear and linear regression models, which were then analyzed.
The substructure identification by using an adaptive Kalman filter was investigated by applying numerical studies of a shear building, a frame structure, and a truss structure. The stiffness and damping were identified successfully from limited acceleration responses, while the abrupt damages were identified as well. Wavelet analysis was also proposed for damage detection of substructures, and was shown to be able to approximately locate such damages.
Delamination detection of concrete slabs by modal identification from the output-only data was proposed and carried out through numerical studies and experimental modal testing. It was concluded that the changes in modal characteristics can indicate the presence and severity of delamination. Finite element models of concrete decks with different delamination sizes and locations were established and proven to be reasonable.
Pounding identification can provide useful early warning information regarding the potential damage of structures. This thesis proposed to use wavelet scalograms of dynamic response to identify the occurrence of pounding. Its applications in a numerical example as well as shaking table tests of a bridge showed that the scalograms can detect the occurrence of pounding very well.
These studies are very useful for vibration-based structural health monitoring.
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Chochol, Catherine. "Continuous time and space identification : An identification process based on Chebyshev polynomials expansion for monitoring on continuous structure." Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0095/document.
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La méthode d'identification développée dans cette thèse est inspirée des travaux de D. Rémond. On considérera les données d'entrée suivante : la réponse de la structure, qui sera mesurée de manière discrète, et qui dépendra des dimensions de la structure (temps, espace) le modèle de comportement, qui sera exprimé sous forme d'une équation différentielle ou d'une équation aux dérivées partielles, les conditions aux limites ainsi que la source d'excitation seront considérées comme non mesurées, ou inconnues. La procédure d'identification est composée de trois étapes : la projection sur une base polynomiale orthogonale (polynômes de Chebyshev) du signal mesuré, la différentiation du signal mesuré, l'estimation de paramètres, en transformant l'équation de comportement en une équation algébrique. La poutre de Bernoulli a permis d'établir un lien entre l'ordre de troncature de la base polynomiale et le nombre d'ondes contenu dans le signal projeté. Sur un signal bruité, nous avons pu établir une valeur de nombre d'onde et d'ordre de troncature minimum pour assurer une estimation précise du paramètre à identifier. Grâce à l'exemple de la poutre de Timoshenko, nous avons pu réadapter la procédure d'identification à l'estimation de plusieurs paramètres. Trois paramètres dont les valeurs ont des ordres radicalement différents ont été estimés. Cet exemple illustre également la stratégie de régularisation à adopter avec ce type de problèmes. L'estimation de l'amortissement sur une poutre a été réalisée avec succès, que ce soit à l'aide de sa réponse transitoire ou à l'aide du régime établi. Le cas bidimensionnel de la plaque a également été traité. Il a permis d'établir un lien similaire au cas de la poutre de Bernoulli entre le nombre d'onde et l'ordre de troncature. Deux cas d'applications expérimentales ont été traités au cours de cette thèse. Le premier se base sur le modèle de la poutre de Bernoulli, appliqué à la détection de défaut. En effet on applique un procédé d'identification ayant pour hypothèse initiale la continuité de la structure. Dans le cas où celle-ci ne le serait pas on s'attend à observer une valeur aberrante du paramètre reconstruit. Le procédé permet de localiser avec succès le lieu de la discontinuité. Le second cas applicatif vise à reconstruire l'amortissement d'une structure 2D : une plaque libre-libre. On compare les résultats obtenus à l'aide de notre procédé d'identification à ceux obtenus par Ablitzer à l'aide de la méthode RIFF. Les deux méthodes permettent d'obtenir des résultats sensiblement prochesThe purpose of this work is to adapt and improve the continuous time identification method proposed by D. Rémond for continuous structures. D. Rémond clearly separated this identification method into three steps: signal expansion, signal differentiation and parameter estimation. In this study, both expansion and differentiation steps are drastically improved. An original differentiation method is developed and adapted to partial differentiation. The existing identification process is firstly adapted to continuous structure. Then the expansion and differentiation principle are presented. For this identification purpose a novel differentiation model was proposed. The aim of this novel operator was to limit the sensitivity of the method to the tuning parameter (truncation number). The precision enhancement using this novel operator was highlighted through different examples. An interesting property of Chebyshev polynomials was also brought to the fore : the use of an exact discrete expansion with the polynomials Gauss points. The Gauss points permit an accurate identification using a restricted number of sensors, limiting de facto the signal acquisition duration. In order to reduce the noise sensitivity of the method, a regularization step was added. This regularization step, named the instrumental variable, was inspired from the automation domain. The instrumental variable works as a filter. The identified parameter is recursively filtered through the structure model. The final result is the optimal parameter estimation for a given model. Different numerical applications are depicted. A focus is made on different practical particularities, such as the use of the steady-state response, the identification of multiple parameters, etc. The first experimental application is a crack detection on a beam. The second experimental application is the identification of damping on a plate
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Bredemeyer, Stefan [Verfasser]. "Monitoring gas emissions of active volcanoes - identification of natural degassing variations and combination of volcano monitoring techniques / Stefan Bredemeyer." Kiel : Universitätsbibliothek Kiel, 2017. http://d-nb.info/112814932X/34.
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Ruryk, Andriy. "Development of microsystem technology suitable for bacterial identification and gene expression monitoring." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974502286.
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Santos, Flores Jorge Santiago. "Monitoring of dual-purpose cattle farms to constraints identification in Yucatan, Mexico." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263250.
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Swetye, Michael Harrison. "Monitoring, identification, and intervention for metabolic disorders in veterans with psychotic disorders." [New Haven, Conn. : s.n.], 2008. http://ymtdl.med.yale.edu/theses/available/etd-12092008-164555/.
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Thomas, Mark David. "Linear and non-linear blind system identification with applications to condition monitoring." Thesis, Royal Holloway, University of London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286886.
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Yamamoto, Kyosuke. "Bridge Damage Identification Using Vehicle Response." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/159406.
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Calmels, Bastien. "Immunothérapie non-spécifique et immuno-monitoring pour le traitement du cancer." Paris 7, 2004. http://www.theses.fr/2004PA077185.
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Tri, Rachmanto. "Monitoring of biodiesel transesterification process using impedance measurement." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4337/.
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Alternative diesel fuels have been the subject of extensive investigation. Fatty acid methyl ester (FAME) based Biodiesel manufactured from vegetable oils or animal fats is an excellent candidate to replace common diesel fuel being renewable, non-toxic and often giving rise to reduced exhaust gas emissions. The transesterification process has been commonly and widely used to produce biodiesel from vegetable oil or animal fat. Vegetable oils or animal fats generally have viscosities higher than standard diesel oil. This means that it is necessary to reduce the viscosity by means of reacting vegetable oil with alcohol in the presence of a suitable catalyst. The target product for this reaction is methyl ester, with glycerol and potentially soap produced as by products with the process of transesterification. Methylester (Biodiesel) is produced by converting triglycerides to alkylesters. A batch transesterification process has two significant mechanisms, and exhibits a mass transfer controlled region that precedes a second order kinetically controlled region. In order to control the conversion process it is useful to employ process monitoring. In particular monitoring of the mass transfer processes that limits the initial reaction rates could prove to be beneficial in allowing for process optimization and control. This thesis proposes the use of a new method of biodiesel process monitoring using low frequency (15kHz) impedance sensing which is able to provide information regarding the progress of mass transfer and chemical reaction during biodiesel production. An interdigitated (ID) sensor has been used to monitoring the biodiesel process The ID sensor is of simple construction and consists of two sets of interleaved electrodes (fingers). The two sets of electrodes are separated by a gap and when an AC excitation voltage is applied across the interleaved electrodes an oscillating electric field is developed. The response of the fluid surrounding the sensor to the applied excitation was then used to determine progress of the chemical reaction by evaluating the real and complex impedance. A significant and unambiguous change in the components of impedance has been shown to occur during mixing (mass transfer) and transesterification. The impedance measurements gained during transesterification were then used for the development of a system model. A systematic approach was used to select mathematical models and system identification techniques were evaluated. The system identification investigation used real process measurement data in conjunction with the Matlab system identification toolbox.
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Haghighi, Mona. "Rule-based Risk Monitoring Systems for Complex Datasets." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6248.
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In this dissertation we present rule-based machine learning methods for solving problems with high-dimensional or complex datasets. We are applying decision tree methods on blood-based biomarkers and neuropsychological tests to predict Alzheimer’s disease in its early stages. We are also using tree-based methods to identify disparity in dementia related biomarkers among three female ethnic groups. In another part of this research, we tried to use rule-based methods to identify homogeneous subgroups of subjects who share the same risk patterns out of a heterogeneous population. Finally, we applied a network-based method to reduce the dimensionality of a clinical dataset, while capturing the interaction among variables. The results show that the proposed methods are efficient and easy to use in comparison to the current machine learning methods.
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Baldwin, Mark W. "Modal Analysis Techniques in Wide-Area Frequency Monitoring Systems." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/26522.
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The advent of synchronized wide-area frequency measurements obtained from frequency disturbance recorders and phasor measurement units has presented the power industry with special opportunities to study power system dynamics. I propose the use of wide-area frequency measurements in identifying system disturbances based on power system post-event modal properties.
In this work, power system dynamics are examined from an internal system energy viewpoint. Since an electric power system is composed of coupled rotating machines (large generators) which have air gap magnetic fields that are essentially static, or quasi-static, the power system may be modeled as a system with energy stored in quasi-static magnetic fields. The magnetic fields in the machines do change with time but may be modeled as static as far as wave propagation is concerned. The dynamic model that I develop treats this magnetic energy specifically as potential energy. Each rotating machine also contains an inertia due to the mass and motion of its rotor train and so each machine contains a rotational kinetic energy. Thus the internal system energy for a power system dynamic model may be considered to be contained in potential (magnetic) and kinetic (rotating mass) energies. This notion of internal energy lends itself to the use of a state-space model where each system state is associated with either a kinetic energy or a potential energy. An n-machine system would have a total of 2n states and would thus be a 2n-th order system. For many power system disturbances, I postulate that a linearized version of this model may be used to examine system natural response in terms of frequency and phasor measurements. The disturbances that I will investigate include generator and line outages. For any particular outage, the power system exhibits a very specific natural response in terms of its kinetic and potential energies. Kinetic energy in the system is directly related to each specific machine's rotational speed. I propose that the kinetic energy corresponds directly with bus frequencies through a linear transformation. Likewise magnetic field energy in each machine corresponds directly with a torque angle. The potential energy in the system thus corresponds directly with bus angles through a linear transformation.
The primary focus of this work is on frequency deviation modal characteristics – specifically damped oscillation frequencies, mode shapes, and damping ratios. This work presents how specific disturbances on a power system will lead to specific oscillation frequencies in the deviation quantities and that these oscillation frequencies may be used to identify the disturbance. The idea of disturbance identification stems out of previous work done in locating disturbances by using a distributed parameter (DP) model of an electric power system. This DP model, which assumes a wave-like motion of frequency and phase quantities, was used to locate disturbances via a triangulation method. This present work, instead of using a DP model of the power system, assumes lumped parameters and focuses on disturbance identification strictly via modal characteristics – particularly oscillation frequency in the frequency deviations. This model is not concerned with geographic location but focuses on system topology, loading, and machine mass as lumped parameters. Advantages of disturbance identification include mainly reliability enhancements but can also be used in marketing applications.
The state-space model used to realize this theory is verified via simulation using small, "academic" systems which should prove useful in classroom settings. Additionally the model is verified on a larger test system in order prove its validity and potential usefulness on large power systems.Ph. D.
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Wood, S. E. "The monitoring and identification of Saprolegnia parasitica and its infection of salmonid fish." Thesis, University of Newcastle Upon Tyne, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380752.
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Carrillo, Ramírez Nicolas. "Condition monitoring of complex rotating machines using system identification and speech processing techniques." Berlin Logos, 2008. http://d-nb.info/992155452/04.
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Singh-Levett, Ishan. "Real-time integral based structural health monitoring." Thesis, University of Canterbury. Mechanical Engineering, 2006. http://hdl.handle.net/10092/1171.
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Structural Health Monitoring (SHM) is a means of identifying damage from the structural response to environmental loads. Real-time SHM offers rapid assessment of structural safety by owners and civil defense authorities enabling more optimal response to major events. This research presents an real-time, convex, integral-based SHM methods for seismic events that use only acceleration measurements and infrequently measured displacements, and a non-linear baseline model including hysteretic dynamics and permanent deformation. The method thus identifies time-varying pre-yield and post-yield stiffness, elastic and plastic components of displacement and final residual displacement. For a linear baseline model it identifies only timevarying stiffness. Thus, the algorithm identifies all key measures of structural damage affecting the immediate safety or use of the structure, and the long-term cost of repair and retrofit. The algorithm is tested with simulated and measured El Centro earthquake response data from a four storey non-linear steel frame structure and simulated data from a two storey non-linear hybrid rocking structure. The steel frame and rocking structures exhibit contrasting dynamic response and are thus used to highlight the impact of baseline model selection in SHM. In simulation, the algorithm identifies stiffness to within 3.5% with 90% confidence, and permanent displacement to within 7.5% with 90% confidence. Using measured data for the frame structure, the algorithm identifies final residual deformation to within 1.5% and identifies realistic stiffness values in comparison to values predicted from pushover analysis. For the rocking structure, the algorithm accurately identifies the different regimes of motion and linear stiffness comparable to estimates from previous research. Overall, the method is seen to be accurate, effective and realtime capable, with the non-linear baseline model more accurately identifying damage in both of the disparate structures examined.
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Lindner, Brian Siegfried. "Exploiting process topology for optimal process monitoring." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95987.
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Thesis (MEng) -- Stellenbosch University, 2014.ENGLISH ABSTRACT: Modern mineral processing plants are characterised by a large number of measured variables, interacting through numerous processing units, control loops and often recycle streams. Consequentially, faults in these plants propagate throughout the system, causing significant degradation in performance. Fault diagnosis therefore forms an essential part of performance monitoring in such processes. The use of feature extraction methods for fault diagnosis has been proven in literature to be useful in application to chemical or minerals processes. However, the ability of these methods to identify the causes of the faults is limited to identifying variables that display symptoms of the fault. Since faults propagate throughout the system, these results can be misleading and further fault identification has to be applied. Faults propagate through the system along material, energy or information flow paths, therefore process topology information can be used to aid fault identification. Topology information can be used to separate the process into multiple blocks to be analysed separately for fault diagnosis; the change in topology caused by fault conditions can be exploited to identify symptom variables; a topology map of the process can be used to trace faults back from their symptoms to possible root causes. The aim of this project, therefore, was to develop a process monitoring strategy that exploits process topology for fault detection and identification. Three methods for extracting topology from historical process data were compared: linear cross-correlation (LC), partial cross-correlation (PC) and transfer entropy (TE). The connectivity graphs obtained from these methods were used to divide process into multiple blocks. Two feature extraction methods were then applied for fault detection: principal components analysis (PCA), a linear method, was compared with kernel PCA (KPCA), a nonlinear method. In addition, three types of monitoring chart methods were compared: Shewhart charts; exponentially weighted moving average (EWMA) charts; and cumulative sum (CUSUM) monitoring charts. Two methods for identifying symptom variables for fault identification were then compared: using contributions of individual variables to the PCA SPE; and considering the change in connectivity. The topology graphs were then used to trace faults to their root causes. It was found that topology information was useful for fault identification in most of the fault scenarios considered. However, the performance was inconsistent, being dependent on the accuracy of the topology extraction. It was also concluded that blocking using topology information substantially improved fault detection and fault identification performance. A recommended fault diagnosis strategy was presented based on the results obtained from application of all the fault diagnosis methods considered.
AFRIKAANSE OPSOMMING: Moderne mineraalprosesseringsaanlegte word gekarakteriseer deur ʼn groot aantal gemete veranderlikes, wat in wisselwerking tree met mekaar deur verskeie proseseenhede, beheerlusse en hersirkulasiestrome. As gevolg hiervan kan foute in aanlegte deur die hele sisteem propageer, wat prosesprestasie kan laat afneem. Foutdiagnose vorm dus ʼn noodsaaklike deel van prestasiemonitering. Volgens literatuur is die gebruik van kenmerkekstraksie metodes vir foutdiagnose nuttig in chemiese en mineraalprosesseringsaanlegte. Die vermoë van hierdie metodes om die fout te kan identifiseer is egter beperk tot die identifikasie van veranderlikes wat simptome van die fout vertoon. Aangesien foute deur die sisteem propageer kan resultate misleidend wees, en moet verdere foutidentifikasie metodes dus toegepas word. Foute propageer deur die proses deur materiaal-, energie- of inligtingvloeipaaie, daarom kan prosestopologie inligting gebruik word om foutidentifikasie te steun. Topologie inligting kan gebruik word om die proses in veelvoudige blokke te skei om die blokke apart te ontleed. Die verandering in topologie veroorsaak deur fouttoestande kan dan analiseer word om simptoomveranderlikes te identifiseer. ʼn Topologiekaart van die proses kan ontleed word om moontlike hoofoorsake van foute op te spoor. Die doel van hierdie projek was dus om ʼn prosesmoniteringstrategie te ontwikkel wat prosestopologie benut vir fout-opspooring en foutidentifikasie. Drie metodes vir topologie-ekstraksie van historiese prosesdata is met mekaar vergelyk: liniêre kruiskorrelasie, parsiële kruiskorrelasie en oordrag-entropie. Konnektiwiteitsgrafieke verkry deur hierdie ekstraksie-metodes is gebruik om die proses in veelvoudige blokke te skei. Twee kenmerkekstraksiemetodes is hierna toegepas om foutdeteksie te bewerkstellig: hoofkomponentanalise (HKA), ʼn liniêre metode; en kernhoofkomponentanalise (KHKA), ʼn nie-lineêre metode. Boonop was drie tipes moniteringskaart metodes vergelyk: Shewhart kaarte, eksponensieel-geweegde bewegende gemiddelde kaarte en kumulatiewe som kaarte. Twee metodes om simptoom veranderlikes te identifiseer vir foutidentifikasie was daarna vergelyk: gebruik van individuele veranderlikes; en inagneming van die verandering in konnektiwiteit. Die konnektiwiteitgrafieke was daarna gebruik om hoofoorsake van foute op te spoor. Dit is gevind dat topologie informasie nuttig was vir foutidentifikasie vir meeste van die fouttoestande ondersoek. Nogtans was die prestasie onsamehangend, aangesien dit afhanklik is van die akkuraatheid waarmee topologie ekstraksie uitgevoer is. Daar was ook afgelei dat die gebruik van topologie blokke beduidend die fout-opspooring en foutidentifikasie prestasie verbeter het. ʼn Aanbevole foutdiagnose strategie is voorgestel.
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KONDURY, SHIRISHA. "CONTINUOUS AND AUTOMATED TRAFFIC MONITOR FOR IMMEDIATE IDENTIFICATION AND STATISTICAL HISTORY OF INFLUENCE LINE AND RATING FACTORS." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin996765671.
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Bala, Saimir, Jan Mendling, Martin Schimak, and Peter Queteschiner. "Case and Activity Identification for Mining Process Models from Middleware." Springer, Cham, 2018. http://epub.wu.ac.at/6620/1/PoEM2018%2Dsubmitted.pdf.
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Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider.
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Preston, Robin Huckaby. "LMS-based method for damage detection applied to Phase II of Structural Health Monitoring benchmark problem." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3728.
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Structural Health Monitoring (SHM) is the process of monitoring the state of a
structure to determine the existence, location, and degree of damage that may exist
within the entire structure. A structureÂs health or level of damage can be monitored by
identifying changes in structural or modal parameters. In this research, the structureÂs
health is monitored by identifying changes in structural stiffness. The Adaptive Least
Mean Square (LMS) filtering approach is used to directly identify changes in structural
stiffness for the IASC-ASCE Structural Health Monitoring Task Group Benchmark
problem for both Phase I and II. The research focuses primarily on Phase II of the
benchmark problem. In Phase II, modeling error and noise is introduced to the problem
making the problem more realistic. The research found that the LMS filter approach can
be used to detect damage and distinguish relative severity of the damage in Phase II of
the benchmark problem in real time. Even though the LMS filter approach identified
damage, a threshold below which damage is hard to identify exists. If the overall
stiffness changes less than 10%, then identifying the presence and location of damage is
difficult. But if the time of damage is known, then the presence and location can be
determined. The research is of great interest to those in the structural health monitoring
community, structural engineers, and inspection practitioners who deal with structural
damage identification problems.
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Kelly, Brendan T. "A Newly Proposed Method for Detection, Location, and Identification of Damage in Prestressed Adjacent Box Beam Bridges." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1339520527.
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Chen, Wei. "Signal processing for optical performance monitoring and impairment mitigation." Connect to thesis, 2006. http://repository.unimelb.edu.au/10187/1713.
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Optical performance monitoring is essential for managing optical networks. One important quantity to monitor is the optical signal-to-noise ratio (OSNR). And in high bit rate fiber optical systems operating at 10 Gb/s or beyond, compensating optical impairments becomes important. In this thesis, we investigate OSNR monitoring using beat noise and present two new OSNR monitoring techniques. We propose an OSNR monitoring technique using uncorrelated beat noise and show by experiment for a 10 Gb/s system that in the OSNR range from 10 dB to 30 dB, the proposed OSNR monitoring scheme has a measurement error of less than 0.5 dB. Then, we propose and experimentally demonstrate for the first time an OSNR monitoring technique using beat noise for optical packet switched networks which performs monitoring on a packet basis. The response time of the OSNR monitor can be around 10 ns and the OSNR measurement error is found to be less than 0.6 dB for OSNR from 10 dB to 30 dB. We also explore chromatic dispersion and polarization-mode dispersion (PMD) mitigation using Viterbi equalization in 10 Gb/s nonreturn-to-zero differential phase-shift keying (NRZ-DPSK) and differential quadrature phase-shift keying (NRZ-DQPSK) systems. We show through simulations that using Viterbi equalizers improves the performance of NRZ-OOK, NRZ-DPSK and NRZ-DQPSK receivers. For NRZ-DQPSK receiver with a Viterbi equalizer, the chromatic dispersion tolerance is about 5048 ps/nm and the PMD tolerance is about 160 ps at 3 dB OSNR penalty.
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Dong, Jessica. "Identification of novel NK cell-mediated immunosurveillance function: immunogenicity regulation by monitoring antigen frequency." PLoS One, 2012. http://hdl.handle.net/1993/22039.
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Computational analysis of total amino acid sequences indicate that select combinations that occur less frequently are correlated to increased immunogenicity in humans. Much evidence has been gathered in silico, but little is known about in vivo experimental validation. This concept can be applied to adjuvant research where increased immunogenicity is desirable and can aid in the potency and efficacy of vaccines. A rare peptide called 5mer4 was found to adjuvant influenza vaccines by increasing survival, humoral and cellular immune responses with a speculated NK cell mediated mechanism. Therefore we hypothesize that rare peptides are able to stimulate an increased immune response in comparison to common peptides through a NK-mediated fashion. The first aim of this study is to determine whether rare sequences are able to stimulate an increased immune response collectively in comparison to commonly occurring peptides. Mice vaccinated with rare, semi-common and common peptides indicate a trend of heightened cellular immune response from rare peptides. However, select rare peptide sequences based on high IFNγ responses do not always correlate directly to increased vaccine efficacy against H5N1-H05 influenza virus, indicating that additional immune parameters need to be taken into consideration. When compared against other adjuvants, 5mer4 performed better in both humoral and survival studies. Previous findings suggest NK cell involvement warranted the second aim of this thesis which is to further delineate the role of NK cells as rare peptide immune modulators. Macrophages were evaluated to determine the effect of peptide, but no increase in stimulation could be observed. NK cells incubated with rare peptides show increased levels of early activation marker CD69 in comparison to common peptides. Microscopy data indicates that rare, but not common peptides are able to bind to NK cells. Depletion of NK abrogated adjuvant activity of 5mer4 peptide, suggesting the necessary role of NK cells for adjuvant effect. Taken together, rare peptides have shown the ability to modulate the immune response through NK cell activation verifying our hypothesis. These findings can be extrapolated towards multiple fields such as anti-tumor therapies and can lead to the development of immunomodulators with high efficacy at a lower cost.
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Treetrong, Juggrapong. "The use of parameter identification methods for the condition monitoring of electric motor drives." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498681.
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Induction motors are the most widely used motors among electric motors in industries. These are due to their reasonable cost, reasonably small sizes, ruggedness, low maintenance, and operation with an easily available power supply including their high reliability. However, these motors are often exposed to hostile environments during operation. These abnormal situations may lead to early deterioration of motors i.e., development of faults. Without any actions, these faults may increase to severe problems such as secondary damages to downstream equipment, unexpected breakdowns. Condition monitoring is modern technologies generally used to observe the health of electric machines regularly. Several condition monitoring methods for the induction motors have been developed using the MCSA (Motor Current Signature Analysis) and vibration analysis which explore the possibility of the early detection of developing faults in the electric machines so that the rectification can be planned in advance before any catastrophic failure. Vibration based condition monitoring requires the installation of number of vibration sensors and can detect the early fault but the quantification of the electrical faults (either in the stator or rotor or both) is generally impossible. However the other option - the MCSA doesn't require additional sensors. Hence the present research study utilized the use of the MCSA for the early detection and the quantification of faults which would be useful for quick rectification of the identified faults in practice.
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Liu, Gang. "Spatiotemporal Sensing and Informatics for Complex Systems Monitoring, Fault Identification and Root Cause Diagnostics." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5727.
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In order to cope with system complexity and dynamic environments, modern industries are investing in a variety of sensor networks and data acquisition systems to increase information visibility. Multi-sensor systems bring the proliferation of high-dimensional functional Big Data that capture rich information on the evolving dynamics of natural and engineered processes. With spatially and temporally dense data readily available, there is an urgent need to develop advanced methodologies and associated tools that will enable and assist (i) the handling of the big data communicated by the contemporary complex systems, (ii) the extraction and identification of pertinent knowledge about the environmental and operational dynamics driving these systems, and (iii) the exploitation of the acquired knowledge for more enhanced design, analysis, monitoring, diagnostics and control.
My methodological and theoretical research as well as a considerable portion of my applied and collaborative work in this dissertation aims at addressing high-dimensional functional big data communicated by the systems. An innovative contribution of my work is the establishment of a series of systematic methodologies to investigate the complex system informatics including multi-dimensional modeling, feature extraction and selection, model-based monitoring and root cause diagnostics.
This study presents systematic methodologies to investigate spatiotemporal informatics of complex systems from multi-dimensional modeling and feature extraction to model-driven monitoring, fault identification and root cause diagnostics. In particular, we developed a multiscale adaptive basis function model to represent and characterize the high-dimensional nonlinear functional profiles, thereby reducing the large amount of data to a parsimonious set of variables (i.e., model parameters) while preserving the information. Furthermore, the complex interdependence structure among variables is identified by a novel self-organizing network algorithm, in which the homogeneous variables are clustered into sub-network communities. Then we minimize the redundancy of variables in each cluster and integrate the new set of clustered variables with predictive models to identify a sparse set of sensitive variables for process monitoring and fault diagnostics. We evaluated and validated our methodologies using real-world case studies that extract parameters from representation models of vectorcardiogram (VCG) signals for the diagnosis of myocardial infarctions. The proposed systematic methodologies are generally applicable for modeling, monitoring and diagnosis in many disciplines that involve a large number of highly-redundant variables extracted from the big data.
The self-organizing approach was also innovatively developed to derive the steady geometric structure of a network from the recurrence-based adjacency matrix. As such, novel network-theoretic measures can be achieved based on actual node-to-node distances in the self-organized network topology.
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Asaeikheybari, Golnoush. "WORKPLACE ENVIRONMENTAL AND BEHAVIORAL RISK FACTOR IDENTIFICATION AND MONITORING SYSTEM USING WEARABLE SENSOR TECHNOLOGY." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1596820612674035.
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Aladesanmi, Ereola Johnson. "Non intrusive load monitoring & identification for energy management system using computational intelligence approach." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/13561.
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Includes bibliography.Electrical energy is the life line to every nation’s or continent development and economic progress. Referable to the recent growth in the demand for electricity and shortage in production, it is indispensable to develop strategies for effective energy management and system delivery. Load monitoring such as intrusive load monitoring, non-intrusive load monitoring, and identification of domestic electrical appliances is proposed especially at the residential level since it is the major energy consumer. The intrusive load monitoring provides accurate results and would allow each individual appliance's energy consumption to be transmitted to a central hub. Nevertheless, there are many practical disadvantages to this method that have motivated the introduction of non-intrusive load monitoring system. The fiscal cost of manufacturing and installing enough monitoring devices to match the number of domestic appliances is considered to be a disadvantage. In addition, the installation of one meter per household appliances would lead to congestion in the house and thus cause inconvenience to the occupants of the house, therefore, non-intrusive load monitoring technique was developed to alleviate the aforementioned challenges of intrusive load monitoring. Non-intrusive load monitoring (NILM) is the process of disaggregating a household’s total energy consumption into its contributing appliances. The total household load is monitored via a single monitoring device such as smart meter (SM). NILM provides cost effective and convenient means of load monitoring and identification. Several nonintrusive load monitoring and identification techniques are reviewed. However, the literature lacks a comprehensive system that can identify appliances with small energy consumption, appliances with overlapping energy consumption and a group of appliance ranges at once. This has been the major setback to most of the adopted techniques. In this dissertation, we propose techniques that overcome these setbacks by combining artificial neural networks (ANN) with a developed algorithm to identify appliances ranges that contribute to the energy consumption within a given period of time usually an hour interval.
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Murtaza, Muhammed. "Identification and monitoring of somatic mutations in solid cancers by sequencing circulating tumour DNA." Thesis, University of Cambridge, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708647.
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Bier, Thomas. "Disaggregation of Electrical Appliances using Non-Intrusive Load Monitoring." Thesis, Mulhouse, 2014. http://www.theses.fr/2014MULH8860/document.
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Cette thèse présente une méthode pour désagréger les appareils électriques dans le profil des bâtiments résidentiels de charge. Au cours des dernières années, la surveillance de l’énergie a obtenu beaucoup de popularité dans un environnement privé et industriel. Avec des algorithmes de la désagrégation, les données mesurées à partir de soi-disant compteurs intelligents peuvent être utilisés pour fournir de plus amples informations de la consommation d’énergie. Une méthode pour recevoir ces données est appelé non-intrusifs charge identification. La majeure partie de la thèse peut être divisée en trois parties. Dans un premier temps, un système de mesure propre a été développé et vérifié. Avec ce système, les ensembles de données réelles peuvent être générés pour le développement et la vérification des algorithmes de désagrégation. La deuxième partie décrit le développement d’un détecteur de flanc. Différentes méthodes sont présentées et évaluées, avec lequel les temps de commutation des appareils peuvent être détectés dans le profil de la charge. La dernière partie décrit un procédé de classification. Différents critères sont utilisés pour la classification. Le classificateur reconnaît et étiquette les appareils individuels de la courbe de charge. Pour les classifications différentes structures de réseaux de neurones artificiels sont comparésThis thesis presents a method to disaggregate electrical appliances in the load profile of residential buildings. In recent years, energy monitoring has obtained significantly popularity in private and industrial environment. With algorithms of the disaggregation, the measured data from so-called smart meters can be used to provide more information of the energy usage. One method to receive these data is called non-intrusive appliance load monitoring.The main part of the thesis can be divided into three parts. At first, an own measurement system was developed and verified. With that system, real data sets can be generated for the development and verification of the disaggregation algorithms. The second part describes the development of an event detector. Different methods are presented and evaluated, with which the switching times of the appliances can be detected in the load profile. The last part describes a classification method. Different features are used for the classification. The classifier recognizes and labels the individual appliances in the load profile. For the classification different structures of artificial neural network (ANN) are compared