Dissertations / Theses on the topic 'Health monitoring applications'
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Li, Wenda. "Passive radar for health monitoring applications." Thesis, University of Bristol, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738291.
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Hejll, Arvid. "Civil structural health monitoring : strategies, methods and applications /." Luleå : Division of Structural Engineering, Department of Civil and Mining Engineering, Luleå University of Technology, 2007. http://epubl.ltu.se/1402-1544/2007/10/.
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Ferreira, Gonzalez Javier. "Textile-enabled Bioimpedance Instrumentation for Personalised Health Monitoring Applications." Licentiate thesis, KTH, Medicinska sensorer, signaler och system (MSSS), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120373.
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A growing number of factors, including the costs, technological advancements, an ageing population, and medical errors are leading industrialised countries to invest in research on alternative solutions to improving their health care systems and increasing patients’ life quality. Personal Health System (PHS) solutions envision the use of information and communication technologies that enable a paradigm shift from the traditional hospital-centred healthcare delivery model toward a preventive and person-centred approach. PHS offers the means to follow patient health using wearable, portable or implantable systems that offer ubiquitous, unobtrusive bio-data acquisition, allowing remote access to patient status and treatment monitoring. Electrical Bioimpedance (EBI) technology is a non-invasive, quick and relatively affordable technique that can be used for assessing and monitoring different health conditions, e.g., body composition assessments for nutrition. EBI technology combined with state-of-the-art advances in sensor and textile technology are fostering the implementation of wearable bioimpedance monitors that use functional garments for the implementation of personalised healthcare applications. This research studies the development of a portable EBI spectrometer that can use dry textile electrodes for the assessment of body composition for the purposes of clinical uses. The portable bioimpedance monitor has been developed using the latest advances in system-on-chip technology for bioimpedance spectroscopy instrumentation. The obtained portable spectrometer has been validated against commercial spectrometer that performs total body composition assessment using functional textrode garments. The development of a portable Bioimpedance spectrometer using functional garments and dry textile electrodes for body composition assessment has been shown to be a feasible option. The availability of such measurement systems bring closer the real implementation of personalised healthcare systems.QC 20130405
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Hera, Adriana. "Instantaneous modal parameters and their applications to structural health monitoring." Link to electronic dissertation, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-121905-163738/.
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Dissertation (Ph.D..) -- Worcester Polytechnic Institute.Keywords: structural health monitoring; wavelet transform; time varying vibration modes; instantaneous modal parameters. Includes bibliographical references (p.181-186).
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Pearson, Matthew. "Development of lightweight structural health monitoring systems for aerospace applications." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/47104/.
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This thesis investigates the development of structural health monitoring systems (SHM) for aerospace applications. The work focuses on each aspect of a SHM system covering novel transducer technologies and damage detection techniques to detect and locate damage in metallic and composite structures. Secondly the potential of energy harvesting and power arranagement methodologies to provide a stable power source is assessed. Finally culminating in the realisation of smart SHM structures. 1. Transducer Technology A thorough experimental study of low profile, low weight novel transducers not normally used for acoustic emission (AE) and acousto-ultrasonics (AU) damage detection was conducted. This included assessment of their performance when exposed to aircraft environments and feasibility of embedding these transducers in composites specimens in order to realise smart structures. 2. Damage Detection An extensive experimental programme into damage detection utilising AE and AU were conducted in both composites and metallic structures. These techniques were used to assess different damage mechanism within these materials. The same transducers were used for novel AE location techniques coupled with AU similarity assessment to successfully detect and locate damage in a variety of structures. 3. Energy Harvesting and Power Management Experimental investigations and numerical simulations were undertaken to assess the power generation levels of piezoelectric and thermoelectric generators for typical vibration and temperature differentials which exist in the aerospace environment. Furthermore a power management system was assessed to demonstrate the ability of the system to take the varying nature of the input power and condition it to a stable power source for a system. 4. Smart Structures The research conducted is brought together into a smart carbon fibre wing showcasing the novel embedded transducers for AE and AU damage detection and location, as well as vibration energy harvesting. A study into impact damage detection using the techniques showed the successful detection and location of damage. Also the feasibility of the embedded transducers for power generation was assessed.
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Ji, Bing. "In-situ health monitoring of IGBT power modules in EV applications." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1474.
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Power electronics are an enabling technology and play a critical role in the establishment of an environmentally-friendly and sustainable low carbon economy. The electrification of passenger vehicles is one way of achieving this goal. It is well acknowledged that Electric vehicles (EVs) have inherent advantages over the conventional internal combustion engine (ICE) vehicles owing to the absence of emissions, high efficiency, and quiet and smooth operation. Over the last 20 years, EVs have improved significantly in their system integration, dynamic performance and cost. It has attracted much attention in research communities as well as in the market. In 2011 electric vehicle sales were estimated to reach about 20,000 units worldwide, increasing to more than 500,000 units by 2015 and 1.3 million by 2020 which accounts for 1.8 per cent of the total number of passenger vehicles expected to be sold that year. In general, electric vehicles use electric motors for traction drive, power converters for energy transfer and control, and batteries, fuel cells, ultracapacitors, or flywheels for energy storage. These are the core elements of the electric power drive train and thus are desired to provide high reliability over the lifetime of the vehicle. One of the vulnerable components in an electric power drive train is the IGBT switching devices in an inverter. During the operation, IGBT power modules will experience high mechanical and thermal stresses which lead to bond wire lift-off and solder joint fatigue faults. Theses stresses can lead to malfunctions of the IGBT power modules. A short-circuit or open-circuit in any of the power modules may result in an instantaneous loss of traction power, which is dangerous for the driver and other road users. These reliability issues are very complex in their nature and demand for the development of analytical models and experimental validation. This work is set out to develop an online measurement technique for health monitoring of IGBT and freewheeling diodes inside the power modules. The technique can provide an early warning prior to a power device failure. Bond wire lift-off and solder fatigue are the two most frequently occurred faults in power electronic modules. The former increases the forward voltage drop across the terminals of the power device while the latter increase the thermal resistance of the solder layers. As a result, bond wire lift-off can be detected by a highly sensitive and fast operating in-situ monitoring circuit. Solder joint fatigue is detected by measuring the thermal impedance of the power modules. This thesis focuses on the design and optimisation of the in-situ health monitoring circuit in an attempt to reducing noise, temperature variations and measurement uncertainties. Experimental work is carried out on a set of various IGBT power modules that have been modified to account for different testing requirements. Then the lifetime of the power module can be estimated on this basis. The proposed health monitoring system can be integrated into the existing IGBT driver circuits and can also be applied to other applications such as industrial drives, aerospace and renewable energy.
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Perelli, Alessandro <1985>. "Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6321/.
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Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization.
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Afshari, Mana. "Vibration- and Impedance-based Structural Health Monitoring Applications and Thermal Effects." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/27954.
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Structural Health Monitoring (SHM) is the implementation of damage detection and characterization algorithms using in vitro sensing and actuation for rapidly determining faults in structural systems before the damage leads to catastrophic failure. SHM systems provide near real time information on the state of the integrity of civil, mechanical and aerospace structures. A roadblock in implementing SHM systems in practice is the possibility of false positives introduced by environmental changes. In particular, temperature changes can cause many SHM algorithms to indicate damage when no damage exists. While several experimentally based efforts have been attempted to alleviate temperature effects on SHM algorithms, fundamental research on the effects of temperature on SHM has not been investigated.
The work presented in this dissertation composes of two main parts: the first part focuses on the experimental studies of different mechanical structures of aluminum beams, lug samples and railroad switch bolts. The experimental study of the aluminum lug samples and beams is done to propose and examine methods and models for in situ interrogation and detection of damage (in the form of a fatigue crack) in these specimen and to quantify the smallest detectable crack size in aluminum structures. This is done by applying the electrical impedance-based SHM method and using piezoceramic sensors and actuators. Moreover, in order to better extract the damage features from the measured electrical impedance, the ARX non-linear feature extraction is employed. This non-linear feature extraction, compared to the linear one, results in detection of damages in the micro-level size and improves the early detection of fatigue cracks in structures. Experimental results also show that the temperature variation is an important factor in the structural health monitoring applications and its effect on the impedance-based monitoring of the initiation and growth of fatigue cracks in the lug samples is experimentally investigated. The electrical impedance-based SHM technique is also applied in monitoring the loosening of bolted joints in a full-scale railroad switch and the sensitivity of this technique to different levels of loosening of the bolts is investigated.
The second part of the work presented here focuses on the analytical study and better understanding of the effect of temperature on the vibration-based SHM. This is done by analytical modeling of the vibratory response of an Euler-Bernoulli beam with two different support conditions of simply supported and clamped-clamped and with a single, non-breathing fatigue crack at different locations along the length of the beam. The effect of temperature variations on the vibratory response of the beam structure is modeled by considering the two effects of temperature-dependent material properties and thermal stress formations inside the structure. The inclusion of thermal effects from both of these points of view (i.e. material properties variations and generation of thermal stresses) as independent factors is investigated and justified by studying the formulations of Helmholtz free energy and stresses inside a body. The effect of temperature variations on the vibratory response of the cracked beam are then studied by integrating these two temperature-related effects into the analytical modeling. The effect of a growing fatigue crack as well as temperature variations and thermal loadings is then numerically studied on the deflection of the beam and the output voltage of a surface-bonded piezoceramic sensor.Ph. D.
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Boettcher, Dennis N. "A Resistance Based Structural Health Monitoring System for Composite Structure Applications." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/843.
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This research effort explored the possibility of using interwoven conductive and nonconductive fibers in a composite laminate for structural health monitoring (SHM). Traditional SHM systems utilize fiber optics, piezoelectrics, or detect defects by nondestructive test methods by use of sonar graphs or x-rays. However, these approaches are often expensive, time consuming and complicated.
The primary objective of this research was to apply a resistance based method of structural health monitoring to a composite structure to determine structural integrity and presence of defects.
The conductive properties of fiber such as carbon, copper, or constantan - a copper-nickel alloy - can be utilized as sensors within the structure. This allows the structure to provide feedback via electrical signals to a user which are essential for evaluating the health of the structure. In this research, the conductive fiber was made from constantan wire which was embedded within a composite laminate; whereas prepreg fiberglass, a nonconductive material, serves as the main structural element of the laminate. A composite laminate was constructed from four layers of TenCate 7781 “E” fiberglass and BT250E-1 resin prepreg. Integrating the constantan within the composite laminate provides a sensory element which supplies measurements of structural behavior. Thus, with fiberglass, epoxy, and a constantan conductive element, a three-part composite laminate is developed.
Test specimens used in this research were fabricated using a composite air press with the recommended manufacturer cure cycle. A TenCate BT250E-1 Resin System and 7781 "E" impregnated glass-fiber/epoxy weave was used. A constantan wire of 0.01” gauge diameter was integrated into the composite structure. The composite laminate specimen with the integrated SHM system was tested under tensile and flexural loads employing test standards specified by ASTM D3039 and D7264, respectively. These test methods were modified to determine the behavior of the laminate in the elastic range only. A tension and flexural delamination test case was also developed to investigate the sensitivity of the SHM system to inherent defects. Moreover, material characteristic tests were completed to validate manufacturer provided material characteristics. The specimens were tested while varying the constantan configurations, such as the sensor length and orientation. A variety of techniques to integrate the sensor were also investigated. Two different measurement methods were used to determine strain. Strain measurements were made with Instron Bluehill 2 software and correlated to strain obtained by the structural health monitoring system with the use of a data acquisition code written to interact with a micro-ohm-meter.
The experimental results showed good agreement between measurements made by the two different methods of measurement. Observations discovered that varying the length of the sensor element improved sensitivity, but resulted in different prediction models when compared to cases with less sensor length. The predictions are based on the gauge factor, which was determined for the each test case. This value provides the essential relationship between resistance and strain. Experiments proved that the measured gauge factor depended greatly on the sensor length and orientation. The correlation was of sufficient accuracy to predict strain values in a composite laminate without the use of any added tools or equipment besides the ohm-meter.
Analytical solutions to the loading cases were developed to validate results obtained during experiments. The solutions were in good agreement with the experimental results.
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Overbey, Lucas A. "Time series analysis and feature extraction techniques for structural health monitoring applications." 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?p3291254.
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Thesis (Ph. D.)--University of California, San Diego, 2008.Title from first page of PDF file (viewed February 14, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 257-268).
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Yang, Cheng [Verfasser]. "Distributed piezoelectric transducers and their applications in structural health monitoring / Cheng Yang." Siegen : Universitätsbibliothek der Universität Siegen, 2017. http://d-nb.info/1129453316/34.
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Li, Kit-ling Carol, and 李潔寧. "m-Health smartphone applications on chronic disease monitoring : development and regulatory considerations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206932.
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Introduction: The market for chronic disease management apps for patients is growing from year to year. However, policy and regulation of app use for medical purposes in Asia Pacific are not developed. Methods: 1) A systematic review of randomized-controlled trials of diabetes management apps for patients are assessed as to determine whether using the app (intervention group) leads to significant reductions in HbA1c levels ; 2) A comparison of paid and free apps based on number of group functions between Apple iTunes App Store and Google Play for Android. Results: 1) A reduction in HbA1c in both the intervention (m-Health) and control (usual care) group, although two studies identified the changes as statistically insignificant; 2) Apple iTunes App store included 95 free diabetes management apps for patients and 86 paid apps at an average cost of $19.91. Google Play offered 80 free apps and 31 paid apps at an average cost of $4.31. The largest HbA1c reductions could be found when clinical, social, behavioural, and affective factors are taken into account in the app’s supporting system (e.g. WellDoc™ System (WDS). Discussion: There is some evidence to suggest that mobile apps for diabetes management for patients show reductions in HbA1c similar to usual care. In Hong Kong, some progress has been made regarding the promotion of the use of m-Health for the elderly and disabled, but policies on app development, approval, and regulation are absent. Future expansion of ICT may consider m-Health for chronic disease management based on international lessons on medical device and medical apps guidelines.published_or_final_version
Public Health
Master
Master of Public Health
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Kural, Aleksander. "Ultrasonic lamb wave energy transmission system for aircraft structural health monitoring applications." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/58395/.
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In this project an investigation of a wireless power transmission method utilising ultrasonic Lamb waves travelling along plates was performed. To the author’s knowledge, this is the first time such a system was investigated. The primary application for this method is the supply of power to wireless structural health monitoring (SHM) sensor nodes located in remote areas of the aircraft structure. A vibration generator is placed in a location where electricity supply is readily available. Ultrasonic waves generated by this device travel through the aircraft structure to a receiver in a remote wireless sensor node. The receiver converts the mechanical vibration of the ultrasonic waves back to electricity, which is used to power the sensor node. An experimental setup comprising a 1000 × 821 × 1.5 mm aluminium plate was designed to model an aircraft skin panel. Pairs of piezoelectric transducers were positioned along the longer edges of the plate. The electric impedance characteristics of three transducer types were measured. A circuit simulation MATLAB code was written. An input and output power measurement system was developed. The MFC M8528-P1 transducer type was identified as providing the best performance. The use of inductors to compensate for the capacitive characteristics of transducers was shown to provide up to 170-fold power throughput increase. The propagation of Lamb waves in the experimental plate was mapped using a scanning laser vibrometer and simulated using LISA finite difference method software. An optimised laboratory system transmitted 17 mW of power across a distance of 54 cm while being driven by a 20 V, 224 kHz signal. This figure can be easily increased by using a higher drive voltage. This shows that the system is capable of supplying sufficient power to wireless SHM sensor nodes, which currently have a maximum power requirement of approximately 200 mW.
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Statham, Shannon M. "Autonomous structural health monitoring technique for interplanetary drilling applications using laser doppler velocimeters." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39488.
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With the goal to continue interplanetary exploration and search for past or existent life on Mars, software and hardware for unmanned subsurface drills are being developed. Unlike drilling on Earth, interplanetary exploration drills operate with very low available power and require on-board integrated health monitoring systems, with quick-response recovery procedures, under complete autonomous operations. As many drilling faults are not known a priori, Earth-based direction and control of an unmanned interplanetary drilling operation is not practical. Such missions also require advanced robotic systems that are more susceptible to structural and mechanical failures, which motivates a need for structural health monitoring techniques relevant to interplanetary exploration systems.
Structural health monitoring (SHM) is a process of detecting damage or other types of defects in structural and mechanical systems that have the potential to adversely affect the current or future performance of these systems. Strict requirements for interplanetary drilling missions create unique research problems and challenges compared with SHM procedures and techniques developed to date. These challenges include implementing sensors and devices that do not interfere with the drilling operation, producing "real-time" diagnostics of the drilling condition, and developing an automation procedure for complete autonomous operations.
Thus, the completed thesis work presents basic research leading to the dynamic analysis of rotating structures with specific application to interplanetary subsurface drill systems, and the formulation of an autonomous, real-time, dynamics-based SHM technique for drilling applications. This includes modeling and validating the structural dynamic system, with and without damage or faults, for a prototype interplanetary subsurface drill, exploring the use of Laser Doppler Velocimeter sensors for use in real-time SHM, developing signal filters to remove inherent harmonic components from the dynamic signal of rotating structures, developing an automation procedure with the associated software, and validating the SHM system through laboratory experiments and field tests.
The automated dynamics-based structural health monitoring technique developed in this thesis presents advanced research accomplishments leading to real-time, autonomous SHM, and it has been validated on an operating dynamic system in laboratory and field tests. The formulated SHM and drilling operation also met or exceeded all specified requirements. Other major contributions of this thesis work include the formulation and demonstration of real-time, autonomous SHM in rotating structures using Laser Doppler Velocimeter sensors.
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Martin, Luke Andrew. "Developing a Self-Powered, Wireless Damage Detection System for Structural Health Monitoring Applications." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/9962.
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The research presented in this manuscript introduces an independent structural health monitoring (SHM) system capable of performing impedance-based testing and detecting shifts in resonant frequencies. This independent structural health monitoring system incorporates a low power wireless transmitter that sends a warning signal when damage is detected in a structure. Two damage detection techniques were implemented on the SHM system and successfully used for evaluating structural damage. The first impedance-based technique is used to detect a gouge introduced to a composite plate. The second technique is a modal parameter technique that analyzes shifts in natural frequency; this technique was used to detect structural changes in an aluminum cantilever beam. In additional to the above test structures, an aircraft rib provided by the United States Air Force was also tested. This test was performed using the HP 4192A impedance analyzer so that the advantage of high frequency impedance-based tested could be demonstrated.
Insight is given into the power characteristics of SHM systems and the need to incorporate power harvesting into these SHM devices is addressed. Also, a comparison between digital signal processors and microprocessors is included in this document.Master of Science
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Mikulski, Heather Ann. "Utilizing Connected Health Applications in Diabetes Care: Implications for Public Health and Policy in the U.S." Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1619799550674987.
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Wadekar, Chinmay. "A Tiny Machine Learning implementation with low-power devices in Structural Health Monitoring Applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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The thesis work focuses on the practical implementation of Machine Learning models on Embedded Systems and the selected target for the tests is the Arduino Nano 33 BLE Sense board. The workflow starts with study of TinyML concepts, encompassing model conversion to TFLite and finally to a hex model ready for deployment on the microcontroller board. Examples from the literature will be discussed and experimentally implemented, such as, “Hello World”, “Magic Wand” and “Micro Speech-Recognition” tasks as per the book “TinyML - Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers” by Pete Warden, Daniel Situnayake. The final aim of this manuscript, which constitutes the core part of the work, is to implement novel TinyML models in SHM applications: specifically, two types of Neural Networks (NNs) namely the Associative Neural Network (ANN) and the One Class Classifier Neural Network (OCCNN) on Arduino Nano 33 BLE Sense board. These NNs are meant for damage detection and binary classification problems, whose output consists of a structural bulletin specifying whether the monitored is healthy or damaged.
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Álvarez-Carulla, Albert. "Energy Harvesting Solutions for Self-Powered Devices: From Structural Health Monitoring to Biomedical Applications." Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/670900.
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The thesis reflects the research carried out on the development of truly self-powered devices. The development of devices for the scopes of Structural Health Monitoring (SHM) and Point-of-Care devices (PoC) is shown. New solutions are implemented in the field of energy harvesting to use a single transducer as sensor element and power supply for the system. In this research, the transducers used are piezoelectric generators and galvanic cells, being extrapolated the developments made to other types of transducers or generators.La tesis recoge la investigación realizada sobre el desarrollo de dispositivos verdaderamente auto- alimentados. Se muestra el desarrollo de dispositivos para el ámbito de la monitorización de la salud de estructuras (SHM) y el ámbito de los dispositivos Point-of-Care (PoC). Para ello, se implementan nuevas soluciones del ámbito de la recolección de energía para utilizar un único transductor como elemento sensor y de fuente de alimentación para el sistema. En esta investigación, los transductores utilizados son generadores piezoeléctricos y celdas galvánicas, siendo extrapolables los desarrollos realizados a otro tipos de transductores o generadores.
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Dong, Haobo (Haobo Jack). "Passive EPC Class 1 Gen 2 UHF RFID sensor tag For health monitoring applications." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66026.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 101-102).
Parkinson's disease (PD) is a chronic and degenerative condition that affects millions of Americans. Current approach of PD evaluation, diagnosis, and treatment is mainly qualitative using the Unified Parkinson's Disease Rating Scale (UPDRS) or the Hoehn and Yahr scale. Assessment of the efficacy of the drugs used is difficult and subjective. A long-term monitoring device that can collect movement data in assisting quantitative analysis proves to be useful and needed. This thesis discusses a discrete prototype of a passive EPC Class 1 Gen 2 UHF RFID sensor tag which is a preliminary step in realizing such a monitoring device. The prototype is capable of collecting 8-bit sensor (temperature, inertial, etc) data and transmits it in real-time through a RFID backscatter link to an UHF reader. It is shown that the device can achieve a read distance up to 3 meters at 5 reads/s and a max data rate of about 640 Kbps.
by Haobo (Jack) Dong.
S.M.
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Kaphle, Manindra R. "Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53201/1/Manindra_Kaphle_Thesis.pdf.
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Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique.
In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment.
In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization.
A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources.
Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures.
By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.
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Alhadrami, Hani Abdullah. "Development and applications of mutagenicity and carcinogenicity bioassays for human health risk assessment." Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=166645.
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Young children are particularly sensitive to environmental pollutants. They can directly ingest soil by putting dirty hands and objects in their mouths. The reliance on animal derived models for human health risk and exposure assessment has several limitations. In this investigation, a tool-kit was developed and optimised to facilitate more accurate, reliable and representative predictions of soil contaminants that might pose a significant hazard to young children. The tool-kit was developed and optimised using an in vitro human digestion bioassay. This procedure was followed by the optimisation of several mutagenicity bioassays to link to the bioaccessible fraction which quantified by the in vitro bioassay. The application of novel and sensitive environmental-based biosensors requires them to work in parallel with effective and proven extraction techniques. In this study, chemical analysis was used to quantify the bioaccessible (human assimilated portion) of pollutants in soils. Acute toxicity was measured using constitutively marked bioluminescent bacterial biosensors and these were indicative of the total contaminant burden. A range of mutagenic assays were applied and optimised. In the Ames assay, any compound exhibiting a greater than two-fold increase in the number of revertants colonies over the number of spontaneous revertants was considered as a mutagen. Mutagenic-responsive SOS-lux based microbial biosensors were compared to the Ames assay. Mutagenicity assessment of a broad range of environmental pollutants (i.e. B[a]P, DiB(a,h)A, B[a]A, Ni and Cu), was performed using four SOS-lux microbial biosensors; E. coli DPD1718, E. coli K12C600, S. aureus pAmiUmuC and S. aureus pAmiRecA. The results substantiated that the four biosensors were unable to be induced by these pollutants. Nevertheless, E. coli DPD1718 and E. coli K12C600 were successfully induced by Mitomycin C (MMC) in a dose response manner. The Ames assay was performed for the above pollutants in the absence and the presence of the metabolic activation S9 mix. The standard plate incorporation assay and a modification protocol for the Ames assay were applied. Results reported from the Ames assay confirmed mutagenicity responses of the tested pollutants except Cu and Ni. MMC was selected and introduced into soil samples as a case study to assess the performance of the developed tool-kit. Soils amended with MMC were extracted by the in vitro human digestion bioassay, and the mutagenicity of the bioaccessible fraction was measured using the Ames assay and the biosensors. A comparison was made between the permissible concentrations of MMC obtained from the developed tool-kit and RISC4 derived concentrations. The four microbial biosensors applied in this study were incapable to detect the mutagenicity of the tested pollutants. On the other hand, the Ames assay was more robust and sensitive to a broad range of environmental pollutants. The in vitro human digestion bioassay enabled the quantification of the human bioaccessible fraction of the tested pollutants. This fraction posed a concern due to its estimation of the doses that would reach the blood circulation and cause harm to human. While the permissible concentration of MMC measured by the developed tool-kit was less than 10 μg MMC/g, the RISC4 model calculated that it should be 40 μg MMC/g. This revealed that, in this situation, risk assessment model was less conservative than empirical study for human health risk assessment. This study enabled the assessment of the permissible concentrations of environmental pollutants that could remain in a soil and pose permissible harm to humans. This approach also enabled a comparison of modelled and empirical data to allow a measure of sensitivity to be judged. There is a need to develop bioassay techniques more able to assess the potency of hydrophobic compounds both in isolation and combination.
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Gao, Peng. "Towards Designing Information System of Health-Monitoring Applications for Caregivers: A Study in Elderly Care." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209572.
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With the increasing elderly population and longer life expectancies, smart wearable technologies are playing an important role in facilitating caregivers to monitor elderly people remotely. Aifloo’s wristband is one smart wristband which can collect various data, predict activities and detect abnormalities to enable elderly people to live independently at home. However, too much information and poor visualizations will cause huge difficulties for caregivers to interpret the data. Six caregivers were interviewed in this study to investigate what data is relevant to monitor elderly people and how they interpret the different designed displays. The main results show that alarms, fall incidents and medication compliance are the most important. Besides, caregivers place a greater emphasis on holistic views of data and they want to highlight abnormal behaviors and alerts. In the end, design guidelines for the information system to present data meaningfully and intuitively are generated.Med ett ökande antal äldre och en ökande medellivslängd kommer smart, bärbar teknologi att spela en större roll i äldrevården för att övervaka de äldre. Aifloos armband är en smart teknologi som kan samla in olika former av data, förutsäga aktiviteter och upptäcka avvikande och onormala beteenden, vilket kan användas av äldre som bor självständiga i sena egna hem. Stora mängder data, och dåliga visualiseringar av dem, orsakar svårigheter för vårdgivare att tolka datan. I den här studien har sex vårdgivare intervjuats för att utforska vilken data som är relevant för dem, och hur de kan tolka information ifrån en grupp olika gränssnitt. Studiens resultat visar att alarm, fallolyckor och översikt över hur de äldre efterföljer sina medicinska recept är viktigast. Vårdgivarna lägger en större vikt vid att förstå datan holistiskt, och de vill synliggöra avvikande beteendemönster och varningar. Slutgiltligen presenteras riktlinjer för hur IT-system kan designas för att presentera data på ett meningsfullt och intuitivt vis.
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Dürager, Christian [Verfasser], and Christian [Akademischer Betreuer] Boller. "Model-based damage feature extraction for structural-health monitoring applications / Christian Dürager ; Betreuer: Christian Boller." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2018. http://d-nb.info/1183673507/34.
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Lourenco, Celia Maria Farinha. "Applications of proton transfer reaction and selected ion flow tube mass spectrometry in health monitoring." Thesis, Open University, 2017. http://oro.open.ac.uk/49148/.
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This thesis investigates the use of Volatile Organic Compounds (VOCs) in disease diagnosis and monitoring. VOCs may be found in the human body, in exhaled breath, faecal matter, urine, and skin. Analysis of the volatile profile produced in the human body can provide an indicator of metabolic status, allowing the screening and monitoring of different diseases and conditions, non-invasively and painlessly. In this thesis a range of highly sensitive analytical techniques have been adopted to measure such VOCs and demonstrate that such monitoring may be used as a disease diagnostic. For example breath samples may be analysed and calibrated against gas-phase standards prepared under physiologically representative concentrations as a tool for non-invasive disease monitoring, e.g. type 2 diabetes. Detailed faecal headspace analyses of two different mouse models of type 2 diabetes (Cushing´s mice and Afmid) were made. The mouse model of Cushing’s syndrome develop excessive circulating glucocorticoid concentrations, which are associated with obesity, hyperglycaemia and insulin resistance. The Afmid knockout mice suffer inactivation of Afmid genes, which in part regulates many functions including pancreatic secretion. These mice show impaired glucose tolerance. The gut microbiota of diabetic mice appear to have a different composition when compared to wild-type littermates, i.e. significantly increased levels of short-chain fatty acids (SCFAs), ketones, alcohols and aldehydes were found in the faecal headspace of diabetic mice, and a possible link between gut microbiota and type 2 diabetes is demonstrated. The use of VOCs as a screening tool of colorectal cancer was also explored. The current screening tools show lack of sensitivity and specificity for the screening of the disease. The volatile faecal profile of patients with colorectal cancer was investigated, and sulphide compounds, including hydrogen sulphide (H2S) are shown to have potential as biomarkers for screening of colorectal cancer.
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Albakri, Mohammad Ismail. "Modeling and Experimental Analysis of Piezoelectric Augmented Systems for Structural Health and Stress Monitoring Applications." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/84513.
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Detection, characterization and prognosis of damage in civil, aerospace and mechanical structures, known as structural health monitoring (SHM), have been a growing area of research over the last few decades. As several in-service civil, mechanical and aerospace structures are approaching or even exceeding their design life, the implementation of SHM systems is becoming a necessity. SHM is the key for transforming schedule-driven inspection and maintenance into condition-based maintenance, which promises enhanced safety and overall life-cycle cost reduction. While damage detection and characterization can be achieved, among other techniques, by analyzing the dynamic response of the structure under test, damage prognosis requires the additional knowledge of loading patterns acting on the structure. Accurate, nondestructive, and reference-free measurement of the state-of-stress in structural components has been a long standing challenge without a fully-satisfactory outcome.
In light of this, the main goal of this research effort is to advance the current state of the art of structural health and loading monitoring, with focus being cast on impedance-based SHM and acoustoelastic-based stress measurement techniques. While impedance-based SHM has been successfully implemented as a damage detection technique, the utilization of electromechanical impedance measurements for damage characterization imposes several challenges. These challenges are mainly stemming from the high-frequency nature of impedance measurements. Current acoustoelastic-based practices, on the other hand, are hindered by their poor sensitivity and the need for calibration at a known state of stress. Addressing these challenges by developing and integrating theoretical models, numerical algorithms and experimental techniques defines the main objectives of this work.
A key enabler for both health and loading monitoring techniques is the utilization of piezoelectric transducers to excite the structure and measure its response. For this purpose, a new three-layer spectral element for piezoelectric-structure interaction has been developed in this work, where the adhesive bonding layer has been explicitly modeled. Using this model, the dynamic response of piezoelectric-augmented structures has been investigated. A thorough parametric study has been conducted to provide a better understanding of bonding layer impact on the response of the coupled structure. A procedure for piezoelectric material characterization utilizing its free electromechanical impedance signature has been also developed. Furthermore, impedance-based damage characterization has been investigated, where a novel optimization-based damage identification approach has been developed. This approach exploits the capabilities of spectral element method, along with the periodic nature of impedance peaks shifts with respect to damage location, to solve the ill-posed damage identification problem in a computationally efficient manner.
The second part of this work investigates acoustoelastic-based stress measurements, where model-based technique that is capable of analyzing dispersive waves to calculate the state of stress has been developed. A criterion for optimal selection of excitation waveforms has been proposed in this work, taking into consideration the sensitivity to the state of stress, the robustness against material and geometric uncertainties, and the ability to obtain a reflections-free response at desired measurement locations. The impact of material- and geometry-related uncertainties on the performance of the stress measurement algorithm has also been investigated through a comprehensive sensitivity analysis. The developed technique has been experimentally validated, where true reference-free, uncalibrated, acoustoelastic-based stress measurements have been successfully conducted.
Finally, the applicability of the aforementioned health and loading monitoring techniques to railroad track components has been investigated. Extensive in-lab experiments have been carried out to evaluate the performance of these techniques on lab-scale and full-scale rail joints. Furthermore, in-field experiments have been conducted, in collaboration with Norfolk Southern and the Transportation Technology Center Inc., to further investigate the performance of these techniques under real life operating and environmental conditions.Ph. D.
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Mascarenas, David D. L. ""Mobile host" wireless sensor networks a new sensor network paradigm for structural health monitoring applications /." 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?p3330318.
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Thesis (Ph. D.)--University of California, San Diego, 2008.Title from first page of PDF file (viewed November 19, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 143-148).
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Manovi, Livia. "Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring.
Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features.
In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed.
In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality.
The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data.
Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.
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Rocker, Samantha Nicole. "Piezoresistivity Characterization of Polymer Bonded Energetic Nanocomposites under Cyclic Load Cases for Structural Health Monitoring Applications." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91427.
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The strain and damage sensing abilities of randomly oriented multi-walled carbon nanotubes (MWCNTs) dispersed in the polymer binder of energetic composites were experimentally investigated. Ammonium perchlorate (AP) crystals served as the inert energetic and atomized aluminum as the metallic fuel, both of which were combined to create a representative fuel-oxidizer filler often used for aerospace propulsive applications. MWCNTs were dispersed within an elastomer binder of polydimethylsiloxane (PDMS), and hybrid energetics were fabricated from it, with matrix material comprised of the identified fillers. The nanocomposites were characterized based on their stress-strain response under monotonic uniaxial compression to failure, allowing for the assessment of effects of MWCNTs and aluminum powder on average compressive elastic modulus, peak stress, and strain to failure. The piezoresistive response was measured as the change in impedance with applied monotonic strain in both the mesoscopic and microscopic strain regimes of mechanical loading for each material system, as well as under ten cycles of applied compressive loading within those same strain regimes. Gauge factors were calculated to quantify the magnitude of strain and damage sensing in MWCNT-enhanced material systems. Electrical response of single-cycle thermal loading was explored with epoxy in place of the elastomer binder of the previously discussed studies. Piezoresistive response due to microscale damage from thermal expansion was observed exclusively in material systems enhanced by MWCNTs. The results discussed herein validate structural health monitoring (SHM) applications for embedded carbon nanotube sensing networks in polymer-based energetics under unprecedented cyclic loads.Master of Science
The ability to characterize both deformation and damage in real time within materials of high energetic content, such as solid rocket propellant, is of great interest in experimental mechanics. Common energetic ammonium perchlorate, in the fonn of crystal particles, was embedded in polymer binders (ie PDMS and epoxy) and investigated under a variety of mechanical and thermal loads. Carbon nanotubes, conductive tube-shaped molecular structures of carbon atoms, have been demonstrated in prior proofs of concept to induce substantial electrical response change when dispersed in composites which are experiencing strain. With the introduction of carbon nanotubes in the energetic composites investigated herein, the electrical response of the material systems was measured as a change in impedance with applied strain. Elastomer-bonded energel.ks were t.esl.ed under monotonic compression and cyclic compression, and expanded exploration was done on these material systems with the additional particulate of aluminum powder, allowing for varied particulate sizes and conductivity enhancement of the overall composite. The magnitude of the resulting piezoresistive change due to strain and microscale damage was observed to increase dramatically in material systems enhanced by MWCNT networks. Local heating was used to explore thermal loading on epoxy-bonded energetic material systems, and sensing of permanent damage to the material through its CNT network was proven through a permanent change in the electrical response which was exclusive to the CNT-enhanced material systems. These results demonstrate valid structural health monitoring (SHM) applications for embedded carbon nanotube sensing networks in particulate energetic composites, under a variety of load cases.
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Frauenthal, Jay Matthew. "Design and Exploration of a Computer Vision Based Unmanned Aerial Vehicle for Railroad Health Applications." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/56559.
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Railroad tracks require consistent and periodic monitoring to ensure safety and reliability. Unmanned Aerial Vehicles (UAVs) have great potential because they are not constrained to the track, allowing trains to continue running while the UAV is inspecting. Also, they can be quickly deployed without human intervention. For these reasons, the first steps towards creating a track-monitoring UAV system have been completed.
This thesis focuses on the design of algorithms to be deployed on a UAV for the purpose of monitoring the health of railroad tracks. Before designing the algorithms, the first steps were to design a rough physical structure of the final product. A small multirotor or fixed-wing UAV will be used with a gimbaled camera mounted on the belly. The camera will take images of the tracks while the onboard computer processes the images. The computer will locate the tracks in the image as well as perform defect detection on those tracks.
Algorithms were implemented once a rough physical structure of the product was completed. These algorithms detect and follow rails through a video feed and detect defects in the rails. The rail following algorithm is based on a custom-designed masking technique that locates rails in images. A defect detection algorithm was also created. This algorithm detect defects by analyzing gradient data on the rail surface.Master of Science
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Stevens, Timothy. "Rehab Tracker: Framework for Monitoring and Enhancing NMES Patient Compliance." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1001.
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We describe the development of a cyber-physical system (Rehab Tracker) for improving patient compliance with at-home physical rehabilitation using neuromuscular electrical stimulation (NMES) therapy. Rehab Tracker consists of three components: 1) hardware modifications to sense and store use data from an FDA-approved NMES therapy device and provide Bluetooth communication capability, 2) an iOS-based smartphone/tablet application to receive and transmit NMES use data and serve as a conduit for patient-provider interactions and 3) a back-end server platform to receive device use data, display compliance data for provider review and provide automated positive and remedial push notifications to patients to improve compliance. This system allows for near real-time compliance monitoring via a secure web portal and offers a novel conduit for patient-provider communication during at-home rehabilitation to improve compliance. The system was tested in patients (n=5) who suffered anterior cruciate ligament rupture and surgical repair to provide proof-of-principal evidence for system functionality and an initial assessment of system usability. The system functioned as designed, recording 89% of rehabilitation sessions. Thus, Rehab Tracker is a functionally correct system with the potential to be used as a tool for studying NMES and mobile communication methodologies at scale and improving compliance with at-home rehabilitation programs.
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Li, Dongsheng [Verfasser]. "Load dependent sensor placement method based on representative least squares : applications in structural health monitoring / Dongsheng Li." Aachen : Shaker, 2012. http://d-nb.info/1069048461/34.
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Hu, Chennan. "Two Innovative Applications Combining Fiber Optics and High Power Pulsed Laser: Active Ultrasonic Based Structural Health Monitoring and Guided Laser Micromachining." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/94130.
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This dissertation presents the exploration of two fiber optics techniques involving high power pulse laser delivery. The first research topic is "Embedded Active Fiber Optic Sensing Network for Structural Health Monitoring in Harsh Environments", which uses the fiber delivered pulse laser for acoustic generation. The second research topic is "Fiber Optics Guided Laser Micromachining", which uses the fiber delivered pulse laser for material ablation.
The objective of the first research topic is to develop a first-of-a-kind technology for remote fiber optic generation and detection of acoustic waves for structural health monitoring in harsh environments. Three different acoustic generation mechanisms were studied in detail, including laser induced plasma breakdown (LIB), Erbium-doped fiber laser absorption, and metal laser absorption. By comparing the performance of the acoustic generation units built based on these three mechanisms, the metal laser absorption method was selected to build a complete fiber optic structure health monitoring (FO-SHM) system. Based on the simulation results of elastic wave propagation and fiber Bragg grating acoustic pulse detection, an FO-SHM sensing system was designed and built. This system was first tested on an aluminum piece in the room temperature range and successfully demonstrated its capability of multi-parameter monitoring and multi-point sensing. With additional studies, the upgraded FO-SHM element was successfully demonstrated at high temperatures up to 600oC on P-91 high temperature steels.
During the studies of high power pulse laser delivery, it was discovered that with proper laser-to-fiber coupling, the output laser from a multimode fiber can directly ablate materials around the fiber tip. Therefore, it is possible to use a fiber-guided laser beam instead of free space laser beams for micromachining, and this solves the aspect ratio limitation rooted in a traditional laser beam micromachining method. In this dissertation, this Guided Laser MicroMachining (GLMM) concept was developed and experimentally demonstrated by applying it to high aspect ratio micro-drilling. It was achieved that an aspect ratio of 40 on aluminum and an aspect ratio of 100 on PET, with a hole diameter less than 200 um.PHD
This dissertation presents two research topics both related to high power laser and fiber optic. The first topic studies the application of using optical fiber and high power laser for ultrasonic non-destructive evaluation. The general idea is to use fiber optic to remotely generate and monitor ultrasonic waves on a workpiece. Due to the fact that there are no electronic components involved in the sensing part of the system, this system can work at high temperature and is unsusceptible to EMI. The second topic studies the usage of optical fiber in high aspect ratio micromachining. The key concept is to use a fiber tip and the output high power laser as a "drilling tip", which eliminate the aspect ratio limitation rooted in the traditional free-space laser micromachining method. With this concept and a demonstrative micromachining system, we achieved record-breaking aspect ratio on both aluminum and plastic.
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Colombo, Alberto Belotti. "Applications of structural health monitoring and field testing techniques to probabilistic based life-cycle evaluation of reinforced concrete bridges." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3144/tde-02122016-092514/.
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This work presents methodologies for the integration of field testing and Structural Health Monitoring SHM in the assessment of reinforced concrete bridges. The methodologies are demonstrated through the use of data collected during the testing of reinforced concrete railway bridges and long-term monitoring of a highway bridge. A probabilistic life-cycle prediction model based on sectional analysis is proposed for reinforced concrete structures. The updating of the model parameters is done using a Bayesian updating approach in which the problem is defined as a reliability one. An algorithm that uses subset simulation is used to sample points from the updated parameter distributions. Testing data from a reinforced concrete railway bridge is used to demonstrate the methodology and its results. The description of a SHM system that was installed in the Jaguari River Bridge is also presented. During the stages of preparation for the installation of this system the bridge was inspected, had NDT performed, and field testing was conducted using a test truck. The results of these tests are also presented. Analysis of the collected data from the live-load response of the Jaguari River Bridge is used to demonstrate methodologies for obtaining live-load response distributions from monitoring data. The use of this live-load response data is also used for the life-cycle analysis of one of the bridge\'s cross-sections.Este trabalho apresenta metodologias de integração de ensaios estruturais e Structural Health Monitoring (SHM) para a avaliação de pontes de concreto armado. O SHM diz respeito a um conjunto de praticas com o objetivo de acompanhar o comportamento estrutural através de sensores com o objetivo de acompanhar o comportamento da estrutura e determinar ações de manutenção de maneira proativa. As metodologias são apresentadas através do uso de dados coletados durante ensaios de pontes ferroviárias em concreto armado e do monitoramento continuo de uma ponte rodoviária. Um modelo para o ciclo de vida de estruturas de concreto armado baseado no método das lamelas é proposto. Os parâmetros deste modelo, que são considerados de maneira probabilística, são atualizados através de um método Bayesiano. Dados de ensaios de uma ponte ferroviária são utilizados nesta analise. A descrição de um sistema de monitoramento contínuo instalado na Ponte do Rio Jaguari também é feita. Durante as etapas de desenvolvimento do sistema a ponte foi inspecionada, ensaios não destrutivos foram feitos e ensaios com um veículo teste foram conduzidos. Os resultados e analises destes também são apresentados. Os dados coletados por este sistema foram utilizados para demonstrar metodologias de caracterização dos modelos de resposta devido a cargas moveis. A utilização destes modelos na avaliação de confiabilidade ao longo do tempo de uma das seções da ponte também é apresentada.
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Clark, Eric Michael. "Applications In Sentiment Analysis And Machine Learning For Identifying Public Health Variables Across Social Media." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1006.
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Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion. We mined data from several public Twitter endpoints to identify content relevant to healthcare providers and public health regulatory professionals. We began by compiling content related to electronic nicotine delivery systems (or e-cigarettes) as these had become popular alternatives to tobacco products. There was an apparent need to remove high frequency tweeting entities, called bots, that would spam messages, advertisements, and fabricate testimonials. Algorithms were constructed using natural language processing and machine learning to sift human responses from automated accounts with high degrees of accuracy. We found the average hyperlink per tweet, the average character dissimilarity between each individual's content, as well as the rate of introduction of unique words were valuable attributes in identifying automated accounts. We performed a 10-fold Cross Validation and measured performance of each set of tweet features, at various bin sizes, the best of which performed with 97% accuracy. These methods were used to isolate automated content related to the advertising of electronic cigarettes. A rich taxonomy of automated entities, including robots, cyborgs, and spammers, each with different measurable linguistic features were categorized.
Electronic cigarette related posts were classified as automated or organic and content was investigated with a hedonometric sentiment analysis. The overwhelming majority (≈ 80%) were automated, many of which were commercial in nature. Others used false testimonials that were sent directly to individuals as a personalized form of targeted marketing. Many tweets advertised nicotine vaporizer fluid (or e-liquid) in various “kid-friendly” flavors including 'Fudge Brownie', 'Hot Chocolate', 'Circus Cotton Candy' along with every imaginable flavor of fruit, which were long ago banned for traditional tobacco products. Others offered free trials, as well as incentives to retweet and spread the post among their own network. Free prize giveaways were also hosted whose raffle tickets were issued for sharing their tweet. Due to the large youth presence on the public social media platform, this was evidence that the marketing of electronic cigarettes needed considerable regulation. Twitter has since officially banned all electronic cigarette advertising on their platform.
Social media has the capacity to afford the healthcare industry with valuable feedback from patients who reveal and express their medical decision-making process, as well as self-reported quality of life indicators both during and post treatment. We have studied several active cancer patient populations, discussing their experiences with the disease as well as survivor-ship. We experimented with a Convolutional Neural Network (CNN) as well as logistic regression to classify tweets as patient related. This led to a sample of 845 breast cancer survivor accounts to study, over 16 months. We found positive sentiments regarding patient treatment, raising support, and spreading awareness. A large portion of negative sentiments were shared regarding political legislation that could result in loss of coverage of their healthcare. We refer to these online public testimonies as “Invisible Patient Reported Outcomes” (iPROs), because they carry relevant indicators, yet are difficult to capture by conventional means of self-reporting. Our methods can be readily applied interdisciplinary to obtain insights into a particular group of public opinions. Capturing iPROs and public sentiments from online communication can help inform healthcare professionals and regulators, leading to more connected and personalized treatment regimens. Social listening can provide valuable insights into public health surveillance strategies.
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O'Dwyer, Martin Joseph. "Implementation and appraisal of an in-fibre Bragg grating quasi-distributed health and usage monitoring system with applications to advanced materials." Thesis, Cranfield University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341033.
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Park, Gyuhae. "Assessing Structural Integrity using Mechatronic Impedance Transducers with Applications in Extreme Environments." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27719.
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This research reviews and extends the impedance-based structural health monitoring technique in order to detect and identify structural damage on various complex structures. The basic principle behind this technique is to apply high frequency structural excitations (typically higher than 30 kHz) through the surface-bonded piezoelectric transducers, and measure the impedance of structures by monitoring the current and voltage applied to the transducers. Changes in impedance indicate changes in the structure, which in turn can indicate that damage has occurred.
Several case studies, including a pipeline structure, a composite reinforced aluminum plate, a precision part (gear), a quarter-scale bridge section, and a steel pipe header, demonstrate how this technique can be used to detect damage in real-time. A method to process impedance measurements to prevent significant temperature and boundary condition changes registering as damage has been developed and implemented. Furthermore, the feasibility of using the technique for high temperature structures and for condition monitoring of critical facilities subjected to a severe natural disaster has been investigated.
While the impedance-based structural health monitoring technique indicates qualitatively that damage has occurred, more information on the nature of damage is necessary for remote structures. In this research, two different damage identification schemes have been combined with the impedance method in order to quantitatively assess the state of structures. One is based on a wave propagation modeling, and the other is the use of artificial neural networks. A newly developed wave propagation model has been developed and combined with the impedance method in order to estimate the severity of damage. Numerical and experimental investigations on 1-dimensional structures were presented to illustrate the effectiveness of the combined approach. Furthermore, to avoid the complexity introduced by conventional computational methods in high frequency ranges, multiple sets of artificial neural networks were integrated with the impedance-based health monitoring technique. By incorporating neural network features, the technique is able to detect damage in its early stage and to determine the severity of damage without prior knowledge of the model of structures. The dissertation concludes with experimental examples, investigations on a quarter-scale steel bridge section and a space truss structure, in order to verify the performance of the proposed methodology.Ph. D.
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Mallur, Kavya. "A Quality Assurance Framework for Business Process Management." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32273.
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A business process is a defined collection of linked structured tasks, activities, and decisions performed together to produce a desired set of results in order to achieve business goals on behalf of the organization. Companies are increasingly moving their business processes online using Business Process Management (BPM) tools and technologies. With BPM, online business processes are defined by an explicit business process model that flexibly combines and orchestrates forms delivered through a web browser to integrate tasks performed by people, and web services accessible through Internet protocols to integrate tasks performed by software.
Often the approach to quality assurance for online business processes is similar to what would be done with any other web application. This is insufficient since it only provides rudimentary verification of single user behavior whereas the orchestration of tasks across many users and software systems can be quite complex. As well, a simple web application testing approach does not leverage the defined model for a business process to ensure consistency, completeness and enable automation. Nor will such an approach validate that a business process is contributing towards the achievement of business goals. A more systematic approach is required.
This thesis proposes a quality assurance framework to provide a repeatable, systematic, cost-efficient approach to quality assurance for BPM. A prototype framework was implemented and evaluated using two case studies, including one case study that was developed in collaboration with a local hospital to support a business process for cancer care assessment.
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Loew, Teagan K. "Improvement to Total Maximum Daily Load (TMDL) Measurements and Monitoring by Satellite Remote Sensing Applications." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1333388592.
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Bhatnagar, Purva. "Multi-Frequency and Multi-Sensor Impedance Sensing Platform for Biosensing Applications." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543999395772179.
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Tzou, Meir-Chyun. "Development and validation of a specific high performance liquid chromatographic method for determination of digoxin and metabolites in serum and its applications to digoxin metabolism and therapeutic drug monitoring studies in humans /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487854314873705.
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Moussallik, Laila. "Towards Condition-Based Maintenance of Catenary wires using computer vision : Deep Learning applications on eMaintenance & Industrial AI for railway industry." Thesis, Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83123.
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Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required. Inspection activities are essential in establishing the required maintenance, and it is periodically required to reduce unexpected failures and to prevent dangerous consequences. Maintenance of railway catenary systems is a critical task for warranting the safety of electrical railway operation.Usually, the catenary inspection is performed manually by trained personnel. However, as in all human-based inspections characterized by slowness and lack of objectivity, might have a number of crucial disadvantages and potentially lead to dangerous consequences. With the rapid progress of artificial intelligence, it is appropriate for computer vision detection approaches to replace the traditional manual methods during inspections. In this thesis, a strategy for monitoring the health of catenary wires is developed, which include the various steps needed to detect anomalies in this component. Moreover, a solution for detecting different types of wires in the railway catenary system was implemented, in which a deep learning framework is developed by combining the Convolutional Neural Network (CNN) and the Region Proposal Network (RPN).
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Zambrano, Ericsson Ocas, Kemeli Reyes Munoz, Jimmy Armas-Aguirre, and Paola A. Gonzalez. "Technological Architecture with Low Cost Sensors to Improve Physical Therapy Monitoring." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656576.
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.In this article, we propose a wireless monitoring solution for gait parameters using low-cost sensors in the physical rehabilitation of patients with gait disorders. This solution consists of infrared speed sensors (IRSS), force-sensing Resistor (FSR) and microcontrollers placed in a walker. These sensors collect the pressure distribution on the walker's handle and the speed of the steps during therapy session. The proposal allows to improve the traditional physiotherapy session times through a mobile application to perform the monitoring controlled by a health specialist in real time. The proposed solution consists of 4 stages: 1. Obtaining gear parameters, 2. Data transmission, 3. Information Storage and 4. Data collection and processing. Solution was tested with 10 patients from a physical rehabilitation center in Lima, Peru. Preliminary results revealed a significant reduction in the rehabilitation session from 25 to 5.2 minutes.
Revisión por pares
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Meehan, Rachael E. (Rachael Elizabeth). "Technology and application of structural health monitoring in bridges." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66841.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, June 2011."June 2011." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 51-53).
Structural Health Monitoring (SHM) has become a useful tool for detecting when the characteristics of a structure have changed to indicate damage such that well-timed and effective maintenance may be planned and the remaining performance capacity may be assessed. SHM has also lead to a better understanding of the loads and the response within a structure in order to optimize future design. In this paper, research is compiled on the current practice of SHM with coverage of sensors used, system configurations, data management, analysis and a discussion of current issues. Recommendations on the current state and future of SHM are made and case studies investigate recent applications. A proposed procedure for the design and implementation of a SHM system is examined and then applied to the design project for the Master of Engineering program in High Performance Structures at MIT. Conclusions include a suggestion on the most effective way to design a SHM system, what the industry needs to mature and predictions of the future of the industry.
by Rachael E. Meehan.
M.Eng.
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Druet, Tom. "Tomographie passive par ondes guidées pour des applications de contrôle santé intégré." Thesis, Valenciennes, 2017. http://www.theses.fr/2017VALE0032/document.
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Ce manuscrit présente une méthode d’imagerie quantitative et sans état de référence, de défauts de corrosion d’une plaque mince, exploitant de manière passive un réseau embarqué de capteurs d’ondes élastiques guidées. Les applications visées sont le contrôle santé intégré (SHM) de structures critiques qui amènent de fortes contraintes à la fois d’intrusivité des capteurs et de fiabilité du diagnostic. Une solution prometteuse, permettant de multiplier la densité de points de mesure sans augmenter l’intrusivité du système, est offerte par les réseaux de Bragg sur fibre optique (FBG). Toutefois, contrairement aux transducteurs piézoélectriques (PZT) classiquement employés en SHM, les FBG ne permettent pas d’émettre d’ondes élastiques. L’idée consiste à utiliser des méthodes dites « passives » permettant de retrouver la fonction de Green entre deux capteurs à partir du bruit ambiant, naturellement présent dans la structure, mesuré simultanément entre ces deux capteurs. Nous étudions dans ce manuscrit deux méthodes passives : la corrélation de bruit et le filtre inverse passif. Nous verrons que ce dernier a plus de potentiel lorsqu’il est couplé à l’imagerie par tomographie. Différents algorithmes de tomographie sont évalués par simulation numérique puis lors d’expériences comparatives actives et passives à l’aide d’un réseau de PZT. Afin de rendre la tomographie passive robuste, nous présentons une méthode clé de détection de temps de vol, basée sur une représentation temps-fréquence. Enfin, nous décrivons une première démonstration expérimentale de mesures passives par FBG qui laisse à penser que la tomographie passive par FBG est prometteuseThis manuscript presents a baseline-free quantitative method for the imaging of corrosion flaws present in thin plates. This method only requires an embedded guided waves sensors network in a fully passive way. The field ofapplications are Structural Health Monitoring (SHM) of critical structures with heavy constrains on both sensors intrusiveness and diagnostic reliability. A promising solution allowing to increase the number of measurement points without increasing the intrusiveness of the system is provided by the Fiber Bragg Gratings (FBGs). However, unlike piezoelectric transducers generally used in SHM, the FBGs cannot emit elastic waves. The idea consists in using passive methods in order to retrieve the Green function from elastic diffuse fields - naturally present in structures - measured simultaneously between two sensors. In this manuscript, two passive methods are studied: the ambient noise correlation and the passive inverse filter. It is shown that the latter gives better results when coupled with tomography. Several tomography algorithms are assessed with numerical simulations and then applied to active and passive datasets measured by a PZT network. In order to make passive tomography robust, a time of flight identification method is proposed, based on a time-frequency representation. Finally, a novel experimental demonstration of passive measurements with FBGs only is presented, suggesting high potential for FBGs passive tomography
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Hoang, Huu Tinh. "Contrôle santé intégré passif par ondes élastiques guidées de tuyauteries pour applications nucléaires et pétrolières." Thesis, Valenciennes, Université Polytechnique Hauts-de-France, 2020. http://www.theses.fr/2020UPHF0023.
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Le contrôle santé des structures (plus connu sous l’acronyme SHM pour Structural Health Monitoring) consisteà intégrer des capteurs dans une structure afin de suivre son état de santé en temps réel tout au long de sa vie.Les travaux de recherche menés dans cette thèse avaient pour objectif de développer une nouvelle approchede SHM pour la détection de corrosion/érosion dans les tuyaux. Ce manuscrit présente une nouvelle méthoded’imagerie quantitative, dite tomographie passive par ondes élastiques guidées, basée sur l’utilisation d’un réseauembarqué de capteurs piézoélectriques (PZT) écoutant et analysant uniquement le bruit élastique ambiant générénaturellement par la circulation de fluide dans des tuyaux. Cette technique passive offre de nombreux atouts pourun système SHM comme une diminution de sa consommation énergétique, un système électronique simplifié etla possibilité de réaliser une inspection lorsque la structure est en fonctionnement. La méthode passive permetaussi d’utiliser des nouveaux capteurs de type réseaux de Bragg sur fibre optique (FBG) qui présentent plusieursavantages par rapport aux capteurs classiques PZT (faible intrusivité, possibilité de multiplexage, résistance auxenvironnements sévères, etc.) mais qui ne sont pas capable d’émettre des ondes. Une première démonstration defaisabilité d’imagerie de corrosion/érosion par FBG est illustrée expérimentalement au travers d’une tomographiehybride dans laquelle l’émission d’ondes est réalisée par PZT et la réception par FBG. Toutes ces études offrent desperspectives prometteuses en vue de l’application de la tomographie passive sur des structures industrielles à l’aided’un système purement FBG. Parmi les principaux résultats présentés dans la thèse, nous montrons que les défautsde corrosion/érosion peuvent être caractérisés par tomographie sur un tube droit sans nécessité de recourir à unétat de référence. Cela est faisable à l’aide d’une nouvelle méthode d’auto-calibration des données développée danscette thèse et utilisée pour réaliser l’imagerie par tomographie. L’absence d’un état de référence rend la méthodetrès fiable et limite les fausses alarmes du système. Finalement, des études préliminaires de tomographie sur desstructures plus complexes comme un tube coudé ont été menées et validées numériquementStructural Health Monitoring (SHM) consists in embedding sensors into a structure in order to monitor its health inreal time throughout its lifetime. The research works carried out in this thesis aimed at developing a new approachof SHM for the detection of corrosion/erosion in pipes. This manuscript presents a new quantitative imaging method,called passive elastic guided wave tomography, based on the use of an embedded network of piezoelectric sensors(PZT) listening and analyzing only the ambient elastic noise which is naturally generated by the fluid circulation inpipes. This passive method offers many advantages for a SHM system, such as reduction of energy consumption,simplified electronics and ability to perform an inspection while the structure is in operation. In addition, thispassive method makes SHM systems possible to use Fiber Bragg Grating sensors (FBG) which have several advantagesover traditional PZT sensors (low intrusivity, resistance to harsh environments, etc.) but which are not able to emitwaves. A first demonstration of the feasibility of corrosion/erosion imaging by FBG is illustrated experimentallythanks to a result obtained by hybrid tomography in which wave emission is performed by PZT and reception byFBG. All these works offer promising perspectives for the application of passive tomography on industrial structuresusing a pure FBG system. Among the various results presented in this thesis, we also show that corrosion/erosiondefects can be characterized by tomography on a straight pipe without the need for a baseline measurement in apristine state. It is feasible by using a new method of auto-calibration of the data used for tomography. The absenceof baseline measurement makes the method very reliable and avoid false alarms of the system. Finally, preliminarystudies on tomography for more complex structures such as a bended pipe have been realized and validated throughsimulations
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Lozano-Tovar, Paulo César 1970. "Dynamic models for liquid rocket engines with health monitoring application." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47491.
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Wojtaszek, Daniel. "Visual surveillance techniques in an entrance monitoring application." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/21689.
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Xia, Min. "Application of machine health monitoring in design optimization of mechatronic systems." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/63133.
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Mechatronic systems are widely used in modern manufacturing. The key machinery of a manufacturing system should be reliable, flexible, intelligent, less complex, and cost effective, which indeed are distinguishing features of a mechatronic system. To achieve these goals, continuous or on-demand design improvements should be incorporated rapidly and effectively, which will address new design requirements or resolve existing weaknesses of the original design.
With the advances in sensor technologies, wireless communication, data storage, and data mining, machine health monitoring (MHM) has achieved significant
capabilities to monitor the performance of an operating machine. The extensive data from the MHM system can be employed in design improvement of the monitored system. In that context, the present dissertation addresses several challenges in applying MHM in design optimization of a mechatronic system.
First, this dissertation develops a systematic framework for continuous design evolution of a mechatronic system with MHM. Possible design weaknesses of the
monitored system are detected using the information from MHM. The proposed method incorporates an index to identify a possible design weakness by evaluating
the performance, detecting failures and estimating the health status of the system.
Second, improved approaches of intelligent machine fault diagnosis (IMFD) that can be applied to more general machinery and faults, are presented. This
dissertation develops an IMFD approach based on deep neural networks (DNN). It uses the massive unlabeled MHM data to learn representative features. Using
very few items of labeled data, this approach can achieve superior diagnosis performance. The dissertation presents another IMFD approach, which uses the convolutional neural networks (CNN) and sensor fusion and has increased diagnosis accuracy and reliability. The end-to-end learning capability of the two approaches enables diagnosis of fault types or machines for which limited prior knowledge is available.
Third, a hierarchical DNN-based method of remaining useful life (RUL) prediction is developed. It achieves high accuracy of RUL prediction by modeling the
system degradation on different health stages. This method generates a better estimate of the system RUL, which provides accurate information for the evaluation of system design.Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate
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Ganesan, Vaahini. "A study of Compressive Sensing for application to Structural Health Monitoring." Master's thesis, University of Central Florida, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6275.
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One of the key areas that have attracted attention in the construction industry today is Structural Health Monitoring, more commonly known as SHM. It is a concept developed to monitor the quality and longevity of various engineering structures. The incorporation of such a system would help to continuously track health of the structure, indicate the occurrence/presence of any damage in real time and give us an idea of the number of useful years for the same. Being a recently conceived idea, the state of the art technique in the field is straight forward - populating a given structure with sensors and extracting information from them. In this regard, instrumenting with too many sensors may be inefficient as this could lead to superfluous data that is expensive to capture and process.
This research aims to explore an alternate SHM technique that optimizes the data acquisition process by eliminating the amount of redundant data that is sensed and uses this sufficient data to detect and locate the fault present in the structure. Efficient data acquisition requires a mechanism that senses just the necessary amount of data for detection and location of fault. For this reason Compressive Sensing (CS) is explored as a plausible idea. CS claims that signals can be reconstructed from what was previously believed to be incomplete information by Shannon's theorem, taking only a small amount of random and linear non - adaptive measurements. As responses of many physical systems contain a finite basis, CS exploits this feature and determines the sparse solution instead of the traditional least - squares type solution.As a first step, CS is demonstrated by successfully recovering the frequency components of a simple sinusoid. Next, the question of how CS compares with the conventional Fourier transform is analyzed. For this, recovery of temporal frequencies and signal reconstruction is performed using the same number of samples for both the approaches and the errors are compared. On the other hand, the FT error is gradually minimized to match that of CS by increasing the number of regularly placed samples. Once the advantages are established, feasibility of using CS to detect damage in a single degree of freedom system is tested under unforced and forced conditions. In the former scenario, damage is indicated when there is a change in natural frequency of vibration of the system after an impact. In the latter, the system is excited harmonically and damage is detected by a change in amplitude of the system's vibration. As systems in real world applications are predominantly multi-DOF, CS is tested on a 2-DOF system excited with a harmonic forcing. Here again, damage detection is achieved by observing the change in the amplitude of vibration of the system. In order to employ CS for detecting either a change in frequency or amplitude of vibration of a structure subjected to realistic forcing conditions, it would be prudent to explore the reconstruction of a signal which contains multiple frequencies. This is accomplished using CS on a chirp signal.
Damage detection is clearly a spatio-temporal problem. Hence it is important to additionally explore the extension of CS to spatial reconstruction. For this reason, mode shape reconstruction of a beam with standard boundary conditions is performed and validated with standard/analytical results from literature. As the final step, the operation deflection shapes (ODS) are reconstructed for a simply supported beam using CS to establish that it is indeed a plausible approach for a less expensive SHM. While experimenting with the idea of spatio-temporal domain, the mode shape as well as the ODS of the given beam are examined under two conditions - undamaged and damaged. Damage in the beam is simulated as a decrease in the stiffness coefficient over a certain number of elements. Although the range of modes to be examined heavily depends on the structure in question, literature suggests that for most practical applications, lower modes are more dominant in indicating damage. For ODS on the other hand, damage is indicated by observing the shift in the recovered spatial frequencies and it is confirmed by the reconstructed response.M.S.M.E.
Masters
Mechanical and Aerospace Engineering
Engineering and Computer Science
Mechanical Engineering; Mechanical Systems Track
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Betz, Daniel C. "Application of optical fibre sensors for structural health and usage monitoring." Thesis, University of Sheffield, 2004. http://etheses.whiterose.ac.uk/3561/.
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Structural Health and Usage Monitoring has gained considerable interest throughout the engineering technologies. Especially for the aircraft industry, where damage can lead to catastrophic and expensive failures, and the vehicles involved undergo regular cost intensive inspections, a Health and Usage Monitoring System (HUMS) has one of the highest payoffs. Furthermore, HUMS allow new design principles for the realisation of lightweight aircraft structures. Different approaches towards a HUMS can be found in the literature. A system based on load monitoring and damage detection could provide highest potential for implementation in future aircrafts. This thesis investigates the use of multifunctional fibre Bragg grating (FBG) sensors for structural health and usage monitoring. It is shown, how FBG sensors can be used simultaneously for both, a strain sensing based load monitoring system and a Lamb wave based damage detection system. Several fundamental areas are addressed analytically and experimentally. This work adds new approaches towards the implementation of large area FBG sensor networks using fibre optical rosettes and temperature compensated strain sensors. A miniaturised build-up technique for a FBG temperature sensor is demonstrated which allows multiplexing of several strain and temperature sensors within a single fibre network. The use of a backing patch for FBG sensors is studied numerically and experimentally. In this thesis, surface mounted and structural integrated FBG sensors are used to detect Lamb waves. The theoretical approach that leads to the development of an appropriate ultrasonic interrogation system for FBG sensors is introduced. Numerical simulations on the influence of the grating dimensions on its ability to detect ultrasonic strain fields and their experimental validation are presented. Three different tasks of damage identification based on Lamb waves are considered: detection of damage, localisation of damage and severity of damage. Experimental results on all three tasks show that FBG sensors can compete with existing technologies. As part of the experimental work, a reliable, temperature independent damage index is introduced and a novel detection scheme using fibre grating rosettes and Genetic Algorithms for the localisation of damage is developed. The results of a simple fatigue test experiment on which the same FBG sensors were used to measure the load parameters and the crack size agree very well with the results using standard technologies.