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García-Gutiérrez, Luis Antonio. "Développement d'un contrôle actif tolérant aux défaillances appliqué aux systèmes PV." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30071.
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Cette thèse de doctorat aborde la problématique de la réalisation d'un système de contrôle actif de détection de défaut et diagnosis (FDD) pour un système de conversion photovoltaïque. Ce type de système de production d'énergie électrique est composé de panneaux solaires, d'un dispositif MPPT, d'un convertisseur de courant DC-DC, d'un onduleur DC-AC et d'une charge. Le système de contrôle actif à tolérance de pannes qui a été développé dans cette thèse est composé de deux étages : * Un étage assurant la fonction de diagnostic et comprenant les fonctions de détection de défauts, la fonction d'isolement de défauts, l'identification de défauts et l'estimation de l'ampleur du/des défaut(s) * Une fonction de reconfiguration du système photovoltaïque. Ce manuscrit est divisé en quatre chapitres : * Introduction au problème et révision de l'état de la technique * Modélisation mathématique du système photovoltaïque avec une validation expérimental de ce dernier effectué sur la plateforme PV de caractérisation du bâtiment réel ADREAM (Laboratoire LAAS-CNRS) * Conception et mise en œuvre du système de diagnostic de pannes du système photovoltaïque comprenant un Système actif à tolérance de pannes * Un système de diagnostic expérimental en cours de développement à l'aide d'un dispositif FPGA<br>This work contributes by developing an active fault tolerant control (AFTC) for Photovoltaic (PV) systems. The fault detection and diagnosis (FDD) methodology is based on the analysis of a model that compares real-time measurement. We use a high granularity PV array model in the FDD tool to allow faults to be detected in complex conditions. Firstly, the research focuses on fault detection in complex shadow conditions. A real-time approach is presented to emulate the electrical characteristics of PV modules under complex shadow conditions. Using a precise emulators approach is a real challenge to study the high non-linearity and the complexity of PV systems in partial shading. The real-time emulation was validated with simple experimental results under failure conditions to design specific fault-detection algorithms in a first sample. The second part of the research addresses the FDD method for DC/DC and DC/AC power converters that are connected to the grid. Primary results allowed us to validate the system's recovery for normal operating points after a fault with this complete AFTC approach. Emulations based on the simulation of distributed power converters, fault detection methodologies based on a model, and a hybrid diagnostician were then presented
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Mahajan, Vijyant. "PV Module and system fault analysis." Thesis, Mahajan, Vijyant (2014) PV Module and system fault analysis. Other thesis, Murdoch University, 2014. https://researchrepository.murdoch.edu.au/id/eprint/25561/.
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In the recent years, there is a noticeable escalation in the number of Photovoltaic module systems installed on the rooftops for the residential and small level commercial purposes. Lower consumer prices, government grants and increase in the awareness of environmental issues are some of the basic causes for this increase. Increase in the renewable energy production is a long term solution to the problems faced due to the fossil fuels energy production methods including the availability and cost of the fossil fuels and environmental pollution.
To keep the positive slope of the trend of accepting the Photovoltaic module systems on the residential basis by the common residential people and to encourage more general public to install the Photovoltaic module systems on their rooftops, it is very important to increase the reliability and durability of the Photovoltaic module systems. Photovoltaic module and system fault analysis is an ongoing assignment in order to increase the efficiency, safety,reliability and durability of the PV system. It is an essential requirement for the PV systems to operate continuously while providing the maximum output results.
This thesis project explains the causes and results of the noticeable faults occur during the operation of the Photovoltaic module systems. These faults include the visible changes in the appearance of the Photovoltaic modules, reduction in the system performance, faults in the other main components of the Photovoltaic module system i.e. inverters, batteries, junction box, etc.
For the purpose of analyzing the faults and its causes in the Photovoltaic module systems, this thesis project investigates and analyzes the survey data collected from the survey conducted by the Australian Photovoltaic Institute (APVI). This survey data provides the information about the faults experienced by the installers and the users of Photovoltaic module systems. Other surveys and reports such as Solar Business in Australia Survey, International Energy Agency Survey are also analyzed and their results have been compared in order to find any relevance of the specific faults to occur.
This thesis project moreover investigates the frequency of the faults occurs during the operation of the Photovoltaic module system. Effect of different climate zones and environmental conditions on the operation, reliability and durability of the Photovoltaic module system is also analyzed from the survey’s results, reports and other thesis as a part of literature review for the research for this thesis.
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Chen, Yi-Ching. "Co-design of Fault-Tolerant Systems with Imperfect Fault Detection." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104942.
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In recent decades, transient faults have become a critical issue in modernelectronic devices. Therefore, many fault-tolerant techniques have been proposedto increase system reliability, such as active redundancy, which can beimplemented in both space and time dimensions. The main challenge of activeredundancy is to introduce the minimal overhead of redundancy and to schedulethe tasks. In many pervious works, perfect fault detectors are assumed to simplifythe problem. However, the induced resource and time overheads of suchfault detectors make them impractical to be implemented. In order to tacklethe problem, an alternative approach was proposed based on imperfect faultdetectors. So far, only software implementation is studied for the proposed imperfectfault detection approach. In this thesis, we take hardware-acceleration intoconsideration. Field-programmable gate array (FPGA) is used to accommodatetasks in hardware. In order to utilize the FPGA resources efficiently, themapping and the selection of fault detectors for each task replica have to be carefullydecided. In this work, we present two optimization approaches consideringtwo FPGA technologies, namely, statically reconfigurable FPGA and dynamicallyreconfigurable FPGA respectively. Both approaches are evaluated andcompared with the proposed software-only approach by extensive experiments.
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Dicharry, Jeff. "Power System Fault Detection Using Conductor Dynamics." ScholarWorks@UNO, 2005. http://scholarworks.uno.edu/td/289.
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Power system fault detection is conventionally achieved using current and potential measurements. An alternate and unconventional form of protective relaying is feasible using rigid bus conductor motion as the means of detection. The research presented focuses on the detection of power system faults using visual displacement of conductor spans. Substation rigid bus conductor motion is modeled using dual spring-mass systems for accurate representation of conductor response to electromagnetic forces generated during system faults. Bundled rigid conductors have advantages including detection independent of system load currents and improved ability to detect polyphase and single phase faults. The dynamic motion of the conductors during the fault is optically monitored with a laser detection system. Timeovercurrent characteristics are derived for the application of fault detection. The response time of the conductor detector system is slower than conventional relays due to the natural frequencies of the conductor span limiting the speed of its displacement. This response time makes the fault detection system using conductor displacement an ideal candidate for a backup relay in power system protection schemes.
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Koubli, Eleni. "Impact of data quality on photovoltaic (PV) performance assessment." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/27508.
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In this work, data quality control and mitigation tools have been developed for improving the accuracy of photovoltaic (PV) system performance assessment. These tools allow to demonstrate the impact of ignoring erroneous or lost data on performance evaluation and fault detection. The work mainly focuses on residential PV systems where monitoring is limited to recording total generation and the lack of meteorological data makes quality control in that area truly challenging. Main quality issues addressed in this work are with regards to wrong system description and missing electrical and/or meteorological data in monitoring. An automatic detection of wrong input information such as system nominal capacity and azimuth is developed, based on statistical distributions of annual figures of PV system performance ratio (PR) and final yield. This approach is specifically useful in carrying out PV fleet analyses where only monthly or annual energy outputs are available. The evaluation is carried out based on synthetic weather data which is obtained by interpolating from a network of about 80 meteorological monitoring stations operated by the UK Meteorological Office. The procedures are used on a large PV domestic dataset, obtained by a social housing organisation, where a significant number of cases with wrong input information are found. Data interruption is identified as another challenge in PV monitoring data, although the effect of this is particularly under-researched in the area of PV. Disregarding missing energy generation data leads to falsely estimated performance figures, which consequently may lead to false alarms on performance and/or the lack of necessary requirements for the financial revenue of a domestic system through the feed-in-tariff scheme. In this work, the effect of missing data is mitigated by applying novel data inference methods based on empirical and artificial neural network approaches, training algorithms and remotely inferred weather data. Various cases of data loss are considered and case studies from the CREST monitoring system and the domestic dataset are used as test cases. When using back-filled energy output, monthly PR estimation yields more accurate results than when including prolonged data gaps in the analysis. Finally, to further discriminate more obscure data from system faults when higher temporal resolution data is available, a remote modelling and failure detection framework is ii developed based on a physical electrical model, remote input weather data and system description extracted from PV module and inverter manufacturer datasheets. The failure detection is based on the analysis of daily profiles and long-term PR comparison of neighbouring PV systems. By employing this tool on various case studies it is seen that undetected wrong data may severely obscure fault detection, affecting PV system s lifetime. Based on the results and conclusions of this work on the employed residential dataset, essential data requirements for domestic PV monitoring are introduced as a potential contribution to existing lessons learnt in PV monitoring.
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Choi, Sang-Sung. "Fault detection algorithm for Global Positioning System receivers." Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1183661191.
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Vinsonneau, Jocelyn A. F. "Fault detection and modelling for an automotive system." Thesis, Coventry University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399534.
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McMichael, D. W. "On-line fault detection, a system-nonspecific approach." Thesis, University of Oxford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232802.
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Luo, Dapeng. "SYSTEM IDENTIFICATION AND FAULT DETECTION OF COMPLEX SYSTEMS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3583.
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The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented to solve the system identification and fault detection problems. Specifically, Augmented Subspace Instrumental Variable Identification (ASIVID) approaches are proposed to identify the closed-loop nonlinear Hammerstein systems. Then fast approaches are presented to determine the system order. Hard-over failures are detected by order determination approaches when failures manifest themselves as rank deficiencies of the dynamical systems. Geometric interpretations of subspace tracking theorems are presented in this dissertation in order to propose a fault tolerance strategy. Possible fields of application considered in this research include manufacturing systems, autonomous vehicle systems, space systems and burgeoning bio-mechanical systems.<br>Ph.D.<br>Department of Mechanical, Materials and Aerospace Engineering;<br>Engineering and Computer Science<br>Mechanical Engineering
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Tian, Ninghan. "ETFIDS: Efficient Transient Fault Injection and Detection System." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1544716635499045.
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Sandberg, Hampus. "Radiation Hardened System Design with Mitigation and Detection in FPGA." Thesis, Linköpings universitet, Datorteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132942.
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FPGAs are attractive devices as they enable the designer to make changes to the system during its lifetime. This is important in the early stages of development when all the details of the final system might not be known yet. In a research environment like at CERN there are many FPGAs used for this very reason and also because they enable high speed communication and processing. The biggest problem at CERN is that the systems might have to operate in a radioactive envi- ronment which is very harsh on electronics. ASICs can be designed to withstand high levels of radiation and are used in many places but they are expensive in terms of cost and time and they are not very flexible. There is therefore a need to understand if it is possible to use FPGAs in these places or what needs to be done to make it possible. Mitigation techniques can be used to avoid that a fault caused by radiation is disrupting the system. How this can be done and the importance of under- standing the underlying architecture of the FPGA is discussed in this thesis. A simulation tool used for injecting faults into the design is proposed in order to verify that the techniques used are working as expected which might not always be the case. The methods used during simulation which provided the best protec- tion against faults is added to a system design which is implemented on a flash based FPGA mounted on a board. This board was installed in the CERN Proton Synchrotron for 99 days during which the system was continuously monitored. During this time 11 faults were detected and the system was still functional at the end of the test. The result from the simulation and hardware test shows that with reasonable effort it is possible to use commercially available FPGAs in a radioactive environment.
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Pandey, Amit Nath. "Fault detection of multivariable system using its directional properties." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3354.
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A novel algorithm for making the combination of outputs in the output zero direction of
the plant always equal to zero was formulated. Using this algorithm and the result of
MacFarlane and Karcanias, a fault detection scheme was proposed which utilizes the
directional property of the multivariable linear system. The fault detection scheme is
applicable to linear multivariable systems. Results were obtained for both continuous and
discrete linear multivariable systems. A quadruple tank system was used to illustrate the
results. The results were further verified by the steady state analysis of the plant.
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Angeli, Chrissanthi. "Intelligent fault detection techniques for an electro-hydraulic system." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262693.
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Vengalathur, Sriram T. "Low cost fault detection system for railcars and tracks." Texas A&M University, 2003. http://hdl.handle.net/1969/326.
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Baldi, Pietro <1981>. "Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6983/1/Baldi_Pietro_tesi.pdf.
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Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety.
This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations.
Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.
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Baldi, Pietro <1981>. "Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6983/.
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Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety.
This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations.
Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.
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Kurén, Jonathan, Simon Leijon, Petter Sigfridsson, and Hampus Widén. "Fault Detection AI For Solar Panels." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413319.
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The increased usage of solar panels worldwide highlights the importance of being able to detect faults in systems that use these panels. In this project, the historical power output (kWh) from solar panels combined with meteorological data was used to train a machine learning model to predict the expected power output of a given solar panel system. Using the expected power output, a comparison was made between the expected and the actual power output to analyze if the system was exposed to a fault. The result was that when applying the explained method an expected output could be created which closely resembled the actual output of a given solar panel system with some over- and undershooting. Consequentially, when simulating a fault (50% decrease of the power output), it was possible for the system to detect all faults if analyzed over a two-week period. These results show that it is possible to model the predicted output of a solar panel system with a machine learning model (using meteorological data) and use it to evaluate if the system is producing as much power as it should be. Improvements can be made to the system where adding additional meteorological data, increasing the precision of the meteorological data and training the machine learning model on more data are some of the options.<br>Med en ökande användning av solpaneler runt om i världen ökar även betydelsen av att kunna upptäcka driftstörningar i panelerna. Genom att utnyttja den historiska uteffekten (kWh) från solpaneler samt meteorologisk data används maskininlärningsmodeller för att förutspå den förväntade uteffekten för ett givet solpanelssystem. Den förväntade uteffekten används sedan i en jämförelse med den faktiska uteffekten för att upptäcka om en driftstörning har uppstått i systemet. Resultatet av att använda den här metoden är att en förväntad uteffekt som efterliknar den faktiska uteffekten modelleras. Följaktligen, när ett fel simuleras (50% minskning av uteffekt), så är det möjligt för systemet att hitta alla introducerade fel vid analys över ett tidsspann på två veckor. Dessa resultat visar att det är möjligt att modellera en förväntad uteffekt av ett solpanelssystem med en maskininlärningsmodell och att använda den för att utvärdera om systemet producerar så mycket uteffekt som det bör göra. Systemet kan förbättras på några vis där tilläggandet av fler meteorologiska parametrar, öka precision av den meteorologiska datan och träna maskininlärningsmodellen på mer data är några möjligheter.
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Kavi, Moses. "Smart protection system for future power system distribution networks with increased distributed energy resources." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/124628/1/Moses_Kavi_Thesis.pdf.
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This thesis investigates the impact of increased penetration of distributed energy resources (DERs) on the power system distribution network protection system which has been designed on the premise of passive radial network with unidirectional power flow. The investigation involved developing a multistage morphological fault detection and diagnostic tool called the decomposed open-close alternating sequence algorithm using a signal processing technique called mathematical morphology. This investigation culminated in proposing new strategies for; adaptive overcurrent protection in AC radial distribution network with increased DER penetration and high impedance arc-fault detection in AC and DC power distribution networks.
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Jaafari, Mousavi Mir Rasoul. "Underground distribution cable incipient fault diagnosis system." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4675.
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This dissertation presents a methodology for an efficient, non-destructive, and online
incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they
become catastrophic. The system provides vital information to help the operator with the
decision-making process regarding the condition assessment of the underground cable. It
incorporates advanced digital signal processing and pattern recognition methods to classify
recorded data into designated classes. Additionally, the IFDS utilizes novel detection
methodologies to detect when the cable is near failure.
The classification functionality is achieved through employing an ensemble of rule-based
and supervised classifiers. The Support Vector Machines, designed and used as a supervised
classifier, was found to perform superior. In addition to the normalized energy features
computed from wavelet packet analysis, two new features, namely Horizontal Severity Index,
and Vertical Severity Index are defined and used in the classification problem.
The detection functionality of the IFDS is achieved through incorporating a temporal
severity measure and a detection method. The novel severity measure is based on the temporal
analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the
Global Severity Index (GSI). This index portrays the progressive degradation path of
underground cable as catastrophic failure time approaches. The detection approach utilizes the
numerical modeling capabilities of SOM as well as statistical change detection techniques. The
natural logarithm of the chronologically ordered minimum modeling errors, computed from
exposing feature vectors to a trained SOM, is used as the detection index. Three modified change
detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and
Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms
determine the change point or near failure time of cable from the instantaneous values of the
detection index.
Performance studies using field recorded data were conducted at three warning levels to
assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection
capability at each warning level. Specifically, it demonstrates that at least one detection
technique successfully provides an early warning that a fault is imminent.
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Hamdan, Abdul R. "Fault detection and rectification algorithms in a question-answering system." Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/33743.
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A Malay proverb "jika sesat di hujung jalan, baleklah kepangkal jalan" roughly means "if you get lost at the end of the road, go back to the beginning". In going back to the beginning of the road, we learn our mistakes and hopefully will not repeat the same mistake again. Thus, this work investigates the use of formal logic as a practical tool for reasoning why we could not infer or deduce a correct answer from a question posed to a database. An extension of the Prolog interpreter is written to mechanise a theorem-proving system based on Horn clauses. This extension procedure will form the basis of the question-answering system. Both input into and output from this system is in the form of predicate calculus. This system can answer all four classes of questions as classified by Chang and Lee (1973).
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Sjöberg, Ingrid. "Modelling and Fault Detection of an Overhead Travelling Crane System." Thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150166.
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Hoists and cranes exist in many contexts around the world, often carrying veryheavy loads. The safety for the user and bystanders is of utmost importance. Thisthesis investigates whether it is possible to perform fault detection on a systemlevel, measuring the inputs and outputs of the system without introducing newsensors. The possibility of detecting dangerous faults while letting safe faultspass is also examined.A mathematical greybox model is developed and the unknown parametersare estimated using data from a labscale test crane. Validation is then performedwith other datasets to check the accuracy of the model. A linear observer of thesystem states is created using the model. Simulated fault injections are made,and different fault detection methods are applied to the residuals created withthe observer. The results show that dangerous faults in the system or the sensorsthemselves are detectable, while safe faults are disregarded in many cases.The idea of performing model-based fault detection from a system point ofview shows potential, and continued investigation is recommended.
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Fischer, Daniel Poehlman Skipper William. "Artificial intelligence techniques applied to fault detection systems /." *McMaster only, 2004.
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Chiecchio, Jerome Jose Andres. "Aiding the Pilot in Flight Control Fault Detection." Thesis, Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6833.
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Three flight simulator experiments examined how a health monitoring system may aid pilots in detecting flight control faults. The first experiment introduced an unexpected fault in the flight control system during an approach to a fictitious airport. The second experiment used a factorial design of (1) presence ?? notof a Fault Meter display and (2) presence ?? not ?? an Alerting System, which could have one or two phased alerts. In half the runs, a fault was triggered at some point, and pilot response was recorded. The next experiment comprised one flight in which pilots were given a false alarm by these systems, testing for automation bias.
No consistent pilot response was found to the faults, with pilots sometimes successfully landing the aircraft, sometimes immediately or eventually initiating a go-around, and sometimes loosing aircraft control and crashing. The pilots were not able to identify the fault in 11% of the cases. Tunnel tracking error increased following the faults and the false alarm, suggesting it may be both a manifestation of attempts to diagnose a fault and a cue to pilots of a problem. Finally, the triggering of a false alarm showed the existence of automation bias induced after a small number of interactions with the HMS.
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Ramaswamy, Sridhar. "An investigation of integrarted Global Positioning System and inertial navigation system fault detection." Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172777336.
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Ramaswamy, Sridhar. "An investigation of integrated global positioning system and inertial navigation system fault detection." Ohio University / OhioLINK, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1172777336.
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Andersson, Kim. "Pressure Monitoring and Fault Detection of an Anti-g Protection System." Thesis, Linköping University, Department of Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56289.
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<p>When flying a fighter aircraft such as the JAS 39 Gripen, the pilot is exposed to high g-loads. In order to prevent the draining of blood from the brain during this stress an anti-g protection system is used. The system consists of a pair of trousers, called the anti-g trousers, with inflatable bladders. The bladders are filled with air, pressing tightly on to the legs in order to prevent the blood from leaving the upper part of the body.</p><p>The purpose of this thesis is to detect if the pressure of the anti-g trousers is deviating from the desired value. This is done by developing a detection algorithm which gives two kinds of alarm. One is given during minor deviations using a CUSUM test, and one is given at grave deviations, based on different conditions including residual, derivative and time. The thresholds, in which between the pressure should lie in a faultless system, are calculated from the g-load value. The thresholds are based upon given static guidelines for the pressure tolerance area and are modified in order to adapt to the estimated dynamics of the system.</p><p>The values of the input signals, pressure and g-load, were taken from real flight sessions. The validation has been performed using both faultless and faulty flight sequences, with low false alarm rate and no missed detections. All together the detection system is considered to work well.</p>
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Córdova, Ricapa Fernando. "Practical implementation of fault detection scheme in a three tank system." Master's thesis, Pontificia Universidad Católica del Perú, 2016. http://tesis.pucp.edu.pe/repositorio/handle/123456789/7012.
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This thesis presents a practical implementation of fault detection scheme in a real
system by using residuals signals. These residuals have been generated applying the
method of a model-based fault diagnosis. Different approaches have been studied
and the corresponding algorithms developed. The object of the investigation is
the three tank system in which different kind of methods of fault detection were
performed. Various scenarios in which faults are simulated in actuators, sensors or
components are analyzed considering also the presence of noise. Due to the inherent
system characteristics it presents four possible work regions which are usually not
taken into account in literature, but studied in this thesis. Also special cases, when
the system goes from one region to another through critical points in which the
system presents singularities, are shown. Faults will be performed around the time
when the system states lie in the neighborhood of the critical points and the main
task is now to overcome these singularities and achieve successfully a fault detection.<br>Tesis
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Wang, Yifei. "Variable selection for wind turbine condition monitoring and fault detection system." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/79827/.
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With the fast growth in wind energy, the performance and reliability of the wind power generation system has become a major issue in order to achieve cost-effective generation. Integration of condition monitoring system (CMS) in the wind turbine has been considered as the most viable solution, which enhances maintenance scheduling and achieving a more reliable system. However, for an effective CMS, large number of sensors and high sampling frequency are required, resulting in a large amount of data to be generated. This has become a burden for the CMS and the fault detection system. This thesis focuses on the development of variable selection algorithm, such that the dimensionality of the monitoring data can be reduced, while useful information in relation to the later fault diagnosis and prognosis is preserved. The research started with a background and review of the current status of CMS in wind energy. Then, simulation of the wind turbine systems is carried out in order to generate useful monitoring data, including both healthy and faulty conditions. Variable selection algorithms based on multivariate principal component analysis are proposed at the system level. The proposed method is then further extended by introducing additional criterion during the selection process, where the retained variables are targeted to a specific fault. Further analyses of the retained variables are carried out, and it has shown that fault features are present in the dataset with reduced dimensionality. Two detection algorithms are then proposed utilising the datasets obtained from the selection algorithm. The algorithms allow accurate detection, identification and severity estimation of anomalies from simulation data and supervisory control and data acquisition data from an operational wind farm. Finally an experimental wind turbine test rig is designed and constructed. Experimental monitoring data under healthy and faulty conditions is obtained to further validate the proposed detection algorithms.
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Bergkvist, Conny, and Stefan Wikner. "Self-organizing maps for virtual sensors, fault detection and fault isolation in diesel engines." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2757.
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<p>This master thesis report discusses the use of self-organizing maps in a diesel engine management system. Self-organizing maps are one type of artificial neural networks that are good at visualizing data and solving classification problems. The system studied is the Vindax(R) development system from Axeon Ltd. By rewriting the problem formulation also function estimation and conditioning problems can be solved apart from classification problems. </p><p>In this report a feasibility study of the Vindax(R) development system is performed and for implementation the inlet air system is diagnosed and the engine torque is estimated. The results indicate that self-organizing maps can be used in future diagnosis functions as well as virtual sensors when physical models are hard to accomplish.</p>
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Bernath, Gregory N. "A baseline fault detection and exclusion algorithm for the global positioning system." Ohio : Ohio University, 1994. http://www.ohiolink.edu/etd/view.cgi?ohiou1176497089.
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Shafiei, Mehdi. "Distribution network state estimation, time dependency and fault detection." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/124659/2/Mehdi_Shafiei_Thesis.pdf.
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In this research work, the combination of three novel approaches is established to estimate the states of three-phase balanced and unbalanced distribution networks and using the developed methods for high impedance fault detection. The effectiveness of the developed methods are proposing a fast real-time state estimator with a low number of measurement devices, avoiding bad data detection in state estimation, and dynamically updating fault current thresholds to detect high impedance faults in the distribution networks.
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Fani, Mehran. "Fault diagnosis of an automotive suspension system." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
Find full textAbstract:
With the development of the embedded application and driving assistance systems, it becomes relevant to develop parallel mechanisms in order to check and to diagnose these new systems. In this thesis we focus our research on one of this type of parallel mechanisms and analytical redundancy for fault diagnosis of an automotive suspension system. We have considered a quarter model car passive suspension model and used a parameter estimation, ARX model, method to detect the fault happening in the damper and spring of system.
Moreover, afterward we have deployed a neural network classifier to isolate the faults and identifies where the fault is happening. Then in this regard, the safety measurements and redundancies can take into the effect to prevent failure in the system.
It is shown that The ARX estimator could quickly detect the fault online using the vertical acceleration and displacement sensor data which are common sensors in nowadays vehicles. Hence, the clear divergence is the ARX response make it easy to deploy a threshold to give alarm to the intelligent system of vehicle and the neural classifier can quickly show the place of fault occurrence.
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Hatzipantelis, Eleftherios. "The design and implementation of a statistical pattern recognition system for induction machine condition monitoring." Thesis, University of Aberdeen, 1995. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU086061.
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Automated fault diagnosis in induction machines is a difficult task and normally requires background information of electrical machines. Here a different methodology to the condition monitoring problem is devised. The approach is based entirely on Digital Signal Processing (DSP) and Statistical Pattern Recognition (PR). Description of machine conditions is extracted from empirical data. The main tasks that must be carried out by a PR-based condition monitoring system are: condition identification, knowledge reinforcement and knowledge creation for previously unseen conditions. The DSP operations are employed to quickly isolate sensor faults and to remove noise using data acquired from a single channel. DSP transformations may seem promising in making the monitoring system portable. Most importantly, they can compensate for operational changes in the machine. These changes affect the supply line currents and the primary signal quantities to be measured, i.e. the current and the axial leakage flux. The data which is input to the statistical monitoring system may be transformed, in the form of features, or remain unaltered. The system exploits the statistical properties of the feature vectors. The particular features, namely the LAR coefficients, convey short-term, high-resolution spectral information. For a long record, the feature vector sequence may provide information about changes in the record spectral characteristics, with time. Many induction machine processes are stationary and they can be properly be dealt with by a simple statistical classifier, e.g. a Gaussian model. For nonstationary processes, the system may employ a more comprehensive tool, namely the Hidden Markov Model. which may track the changing behaviour of the process in question. Initially a limited number of machine conditions are available to the process engineer. By identifying their boundaries, new faulty conditions could be signalled for and adopted into the database.
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Zhang, Xiaoxia. "Incipient anomaly detection and estimation for complex system health monitoring." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG025.
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La détection et le diagnostic des défauts naissants pour les systèmes d’ingénierie ou industriels multivariés à bruit élevé sont abordés dans ce travail de thèse par l’intermédiare d’une approche statistique non paramétrique ’globale’.Un défaut naissant induit un changement anormal dans les valeurs mesurées de la variable du système. Cependant, un tel changement est faible, et tend à ne pas causer de changements évidents dans les paramètres des distributions des signaux du système. En particulier dans un environnement bruité, les caractéristiques de ces défaults faible peuvent être masquées par le bruit et rend celui-ci difficile à évaluer. Dans une telle situation, l’utilisation de méthodes paramétriques traditionnelles pour la détection échouent. Pour faire face à ces difficultés et effectuer la détection et le diagnostic des défauts, une approche’globale’ qui peut prendre en compte la signature totale des défauts est nécessaire. La détection de défauts naissants peut être obtenue par la mesure des différences entre les distributions avant et après l’apparition du défaut. Certaines méthodes basées sur la distribution (dites ’globales’) ont été proposées, mais les performances de détection de ces approches existantes dans un environnement à haut niveau de bruit devraient être améliorées. Dans ce contexte, la divergence de Jensen-Shannon est considérée comme un indicateur de défaut ’global’ pour effectuer la détection et le diagnostic de défaut naissant dans un environnement à haut niveau de bruit. Ses performances de détection pour de petites variations anormales noyées dans le bruit sont validés en simulation. En outre, le problème de l’estimation des défauts est également étudié dans ce travail. Un modèle théorique d’estimation de la sévérité des défauts à parti dépend de la valeur de la divergence pour des conditions Gaussiennes est établi. La précision du modèle d’estimation est évaluée sur des modèles numériques par le biais de simulations. Ensuite, l’approche statistique ’globale’ est mise en oeuvre pour à deux applications dans le domaine de l’ingénierie. La première concerne la détection de fissures naissantes dans un matériau conducteur. La divergence de Jensen-Shannon combinée à l’analyse en composantes indépendantes et à la décomposition on ondelettes a été appliquée à la détection et à la caractérisation de fissures mineures dans des structures conductrices avec des perturbations bruit sur la base de signaux d’impédance expérimentaux. La deuxième application concerne le diagnostic de défauts naissants dans un processus non linéaire multivarié avec un bruit élevé. Le ’Tennessee Eastman Process’ (TEP) est un processus non linéaire multivarié typique pour lequel nous avons appliqué, la divergence de Jensen-Shannon combinée à l’analyse en composantes principales à noyau (ACPN) est pour étudier la détection de défauts naissants dont les difficultés de sont largement décrites dans la littérature<br>Incipient fault detection and diagnosis in engineering and multivariate industrial systems with a high-level noise are addressed in this Ph.D. thesis by a ’global’ non-parametric statistical approach. An incipient fault is supposed to induce an abnormal change in the measured value of the system variable. However, such change is weak, and it tends not to cause obvious changes in the signal distribution’s parameters. Especially in high noise level environment, the weak fault feature can be masked by the noise and becomes unpredictable. In such a condition, using traditional parametric-based methods generally fails in the fault detection. To cope with incipient fault detection and diagnosis, a ’global’ approach that can consider the total faults signature is needed. The incipient fault detection can be obtained by measuring the differences between the signal distributions before and after the fault occurrence. Some distribution-based ’global’ methods have been proposed, however, the detection capabilities of these existed approaches in high noise level environment should be improved. In this context, Jensen-Shannon divergence is considered a ’global’ fault indicator to deal with the incipient fault detection and diagnosis in a high noise level environment. Its detection performance for small abnormal variations hidden in noise is validated through simulation. In addition, the fault estimation problem is also considered in this work. A theoretical fault severity estimation model depending on the divergence value for the Gaussian condition is derived. The accuracy of the estimation model is evaluated on numerical models through simulations. Then, the ’global’ statistical approach is applied to two applications in engineering. The first one relates to non- destruction incipient cracks detection. The Jensen-Shannon divergence combined with Noisy Independent Component Analysis and Wavelet analysis was applied for detection and characterization of minor cracks in conductive structures with high-level perturbations based on experimental impedance signals. The second application addresses the incipient fault diagnosis in a multivariate non-linear process with a high-level noise. Tennessee Eastman Process (TEP) is one typical multivariate non-linear process, the Jensen-Shannon divergence in the Kernel Principal Component Analysis (KPCA) is developed for coping with incipient fault detection in this process
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Alikiotis, Dimitri A. "Flight control sensor system parametric performance analysis for the fault inferring nonlinear detection system (FINDS) algorithm." Ohio University / OhioLINK, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1183039157.
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Ardakani, Mohammad Hamed. "Data driven methods for updating fault detection and diagnosis system in chemical processes." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/650845.
Full textAbstract:
Modern industrial processes are becoming more complex, and consequently monitoring them has become a challenging task. Fault Detection and Diagnosis (FDD) as a key element of process monitoring, needs to be investigated because of its essential role in decision making processes. Among available FDD methods, data driven approaches are currently receiving increasing attention because of their relative simplicity in implementation. Regardless of FDD types, one of the main traits of reliable FDD systems is their ability of being updated while new conditions that were not considered at their initial training appear in the process. These new conditions would emerge either gradually or abruptly, but they have the same level of importance as in both cases they lead to FDD poor performance.
For addressing updating tasks, some methods have been proposed, but mainly not in research area of chemical engineering. They could be categorized to those that are dedicated to managing Concept Drift (CD) (that appear gradually), and those that deal with novel classes (that appear abruptly). The available methods, mainly, in addition to the lack of clear strategies for updating, suffer
from performance weaknesses and inefficient required time of training, as reported.
Accordingly, this thesis is mainly dedicated to data driven FDD updating in chemical processes. The proposed schemes for handling novel classes of faults are based on unsupervised methods, while for coping with CD both supervised and unsupervised updating frameworks have been investigated. Furthermore, for enhancing the functionality of FDD systems, some major methods of data processing, including imputation of missing values, feature selection, and feature extension have been investigated.
The suggested algorithms and frameworks for FDD updating have been evaluated through different benchmarks and scenarios. As a part of the results, the suggested algorithms for supervised handling CD surpass the performance of the traditional incremental learning in regard to MGM score (defined dimensionless score based on weighted F1 score and training time) even up to 50% improvement. This improvement is achieved by proposed algorithms that detect and forget redundant information as well as properly adjusting the data window for timely updating and retraining the fault detection system. Moreover, the proposed unsupervised FDD updating framework for dealing with novel faults in static and dynamic process conditions achieves up to 90% in terms of the NPP score (defined dimensionless score based on number of the correct predicted class of samples). This result relies on an innovative framework that is able to assign samples either to new classes or to available classes by exploiting one class classification techniques and clustering approaches.<br>Los procesos industriales modernos son cada vez más complejos y, en consecuencia, su control se ha convertido en una tarea desafiante. La detección y el diagnóstico de fallos (FDD), como un elemento clave de la supervisión del proceso, deben ser investigados debido a su papel esencial en los procesos de toma de decisiones. Entre los métodos disponibles de FDD, los enfoques basados en datos están recibiendo una atención creciente debido a su relativa simplicidad en la implementación. Independientemente de los tipos de FDD, una de las principales características de los sistemas FDD confiables es su capacidad de actualización, mientras que las nuevas condiciones que no fueron consideradas en su entrenamiento inicial, ahora aparecen en el proceso. Estas nuevas condiciones pueden surgir de forma gradual o abrupta, pero tienen el mismo nivel de importancia ya que en ambos casos conducen al bajo rendimiento de FDD. Para abordar las tareas de actualización, se han propuesto algunos métodos, pero no mayoritariamente en el área de investigación de la ingeniería química. Podrían ser categorizados en los que están dedicados a manejar Concept Drift (CD) (que aparecen gradualmente), y a los que tratan con clases nuevas (que aparecen abruptamente). Los métodos disponibles, además de la falta de estrategias claras para la actualización, sufren debilidades en su funcionamiento y de un tiempo de capacitación ineficiente, como se ha referenciado. En consecuencia, esta tesis está dedicada principalmente a la actualización de FDD impulsada por datos en procesos químicos. Los esquemas propuestos para manejar nuevas clases de fallos se basan en métodos no supervisados, mientras que para hacer frente a la CD se han investigado los marcos de actualización supervisados y no supervisados. Además, para mejorar la funcionalidad de los sistemas FDD, se han investigado algunos de los principales métodos de procesamiento de datos, incluida la imputación de valores perdidos, la selección de características y la extensión de características. Los algoritmos y marcos sugeridos para la actualización de FDD han sido evaluados a través de diferentes puntos de referencia y escenarios. Como parte de los resultados, los algoritmos sugeridos para el CD de manejo supervisado superan el rendimiento del aprendizaje incremental tradicional con respecto al puntaje MGM (puntuación adimensional definida basada en el puntaje F1 ponderado y el tiempo de entrenamiento) hasta en un 50% de mejora. Esta mejora se logra mediante los algoritmos propuestos que detectan y olvidan la información redundante, así como ajustan correctamente la ventana de datos para la actualización oportuna y el reciclaje del sistema de detección de fallas. Además, el marco de actualización FDD no supervisado propuesto para tratar fallas nuevas en condiciones de proceso estáticas y dinámicas logra hasta 90% en términos de la puntuación de NPP (puntuación adimensional definida basada en el número de la clase de muestras correcta predicha). Este resultado se basa en un marco innovador que puede asignar muestras a clases nuevas o a clases disponibles explotando una clase de técnicas de clasificación y enfoques de agrupamiento
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Li, Zhengwei. "Adaptable, scalable, probabilistic fault detection and diagnostic methods for the HVAC secondary system." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43653.
Full textAbstract:
As the popularity of building automation system (BAS) increases, there is an increasing need to understand/analyze the HVAC system behavior with the monitoring data. However, the current constraints prevent FDD technology from being widely accepted, which include: 1)Difficult to understand the diagnostic results; 2)FDD methods have strong system dependency and low adaptability; 3)The performance of FDD methods is still not satisfactory; 4)Lack of information.
This thesis aims at removing the constraints, with a specific focus on air handling unit (AHU), which is one of the most common HVAC components in commercial buildings. To achieve the target, following work has been done in the thesis. On understanding the diagnostic results, a standard information structure including probability, criticality and risk is proposed. On improving method's adaptability, a low system dependency FDD method: rule augmented CUSUM method is developed and tested, another highly adaptable method: principal component analysis (PCA) method is
implemented and tested. On improving the overall FDD performance (detection sensitivity and diagnostic accuracy), a hypothesis that using integrated approach to combine different FDD methods could improve the FDD performance is proposed, both deterministic and probabilistic integration approaches are implemented to verify this hypothesis. On understanding the value of information, the FDD results for a testing system under different information availability scenarios are compared.
The results show that rule augmented CUSUM method is able to detect the abrupt faults and most incipient faults, therefore is a reliable method to use. The results also show that overall improvement of FDD method is possible using Bayesian integration approach, given accurate parameters (sensitivity and specificity), but
not guaranteed with deterministic integration approach, although which is simpler to use. The study of information availability reveals that most of the faults can be detected in low and medium information availability scenario, moving
further to high information availability scenario only slightly improves the diagnostic performance.
The key message from this thesis to the community is that: using Bayesian approach to integrate high adaptable FDD methods and delivering the results in a probability context is an optimal solution to remove the current constraints and push
FDD technology to a new position.
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Kline, Paul A. "Fault detection and isolation for integrated navigation systems using the global positioning system." Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1183731476.
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Adewole, Adeyemi Charles. "Investigation of methodologies for fault detection and diagnosis in electric power system protection." Thesis, Cape Peninsula University of Technology, 2012. http://hdl.handle.net/20.500.11838/1273.
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Thesis Submitted in fulfilment of the requirements for the degree
Master of Technology: Electrical Engineering
in the Faculty of Engineering
at the Cape Peninsula University of Technology, 2012<br>The widespread deregulation and restructuring of electric power utilities throughout the world and the surge in competition amongst utility companies has brought about the desire for improved economic efficiency of electric utilities and the provision of better service to energy consumers. These end users are usually connected to the distribution network. Thus, there is a growing research interest in distribution network fault detection and diagnosis algorithms for reducing the down-time due to faults. This is done so as to improve the reliability indices of utility companies and enhance the availability of power supply to customers.
The application of signal processing and computational intelligence techniques in power systems protection, automation, and control cannot be overemphasized. This research work focuses on power system distribution network and is aimed at the development of versatile algorithms capable of accurate fault detection and diagnosis of all fault types for operation in balanced/unbalanced distribution networks, under varying fault resistances, fault inception angles, load angles, and system operating conditions.
Therefore, different simulation scenarios encompassing various fault types at several locations with different load angles, fault resistances, fault inception angles, capacitor switching, and load switching were applied to the IEEE 34 Node Test Feeder in order to generate the data needed. In particular, the effects of system changes were investigated by integrating various Distributed Generators (DGs) into the distribution feeder. The length of the feeder was also extended and investigations carried out. This was implemented by modelling the IEEE 34-node benchmark test feeder in DIgSILENT PowerFactory (DPF).
In the course of this research, a hybrid combination of Discrete Wavelet Transform (DWT), decision-taking rule-based algorithms, and Artificial Neural Networks (ANNs) algorithms for electric power distribution network fault detection and diagnosis was developed. The integrated algorithms were capable of fault detection, fault type classification, identification of the faulty line segment, and fault location respectively.
Several scenarios were simulated in the test feeder. The resulting waveforms were exported as ASCII or COMTRADE files to MATLAB for DWT signal processing. Experiments with various DWT mother wavelets were carried out on the waveforms obtained from the simulations. In particular, Daubechies db-2, db-3, db-4, db-5, and db-8 were considered. Others are Coiflet-3 and Symlet-4 mother wavelets respectively. The energy and entropy of the detail coefficients for each decomposition level based on a sampling frequency of 7.68 kHz were analysed. The best decomposition level for the diagnostic tasks was then selected
based on the analysis of the wavelet energies and entropy in each level of decomposition. Consequently, level-1 db-4 detail coefficients were selected for the fault detection task, while level-5 db4 detail coefficients were used to compute the wavelet entropy per unit indices which were then used for fault classification, fault section identification, and fault location tasks respectively.
Decision-taking rule-based algorithms were used for the fault detection and fault classification tasks respectively. The fault detection task verifies if a fault did indeed occur or not, while the fault classification task determines the fault class and the faulted phase(s). Similarly, Artificial Neural Networks (ANNs) were used for the fault section identification and fault location tasks respectively. For the fault section identification task, the ANNs were trained for pattern classification to identify the lateral or segment affected by the fault. Conversely, the fault location ANNs were trained for function approximation to predict the location of the fault from the substation in kilometres.
Also, the IEEE 13 Node Benchmark Test Feeder was modelled in RSCAD software and batch mode simulations were carried out using the Real-Time Digital Simulator (RTDS) as a ‘proof of concept’ for the proposed method, in order to demonstrate the scalability, and to further validate the developed algorithms. The COMTRADE files of disturbance records retrieved from an external IED connected in closed-loop with the RTDS and the runtime simulation waveforms were used as test inputs to the developed Hybrid Fault Detection and Diagnosis (HFDD) method.
Comparison of the method based on entropy with statistical methods based on standard deviation and Mean Absolute Deviation (MAD) has shown that the method based on entropy is very reliable, accurate, and robust. Results of preliminary studies carried out showed that the proposed HFDD method can be applied to any power system network irrespective of changes in the operating characteristics. However, certain decision indices would change and the decision-taking rules and ANN algorithms would need to be updated.
The HFDD method is promising and would serve as a useful decision support tool for system operators and engineers to aid them in fault diagnosis thereby helping to reduce system down-time and improve the reliability and availability of electric power supply.
Key words: Artificial neural network, discrete wavelet transform, distribution network, fault simulation, fault detection and diagnosis, power system protection, RTDS.
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Jaradat, Mohammad Abdel Kareem Rasheed. "A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4780.
Full textAbstract:
In this study, an efficient new hybrid approach for multiple sensors data fusion and
fault detection is presented, addressing the problem with possible multiple faults, which
is based on conventional fuzzy soft clustering and artificial immune system (AIS).
The proposed hybrid system approach consists of three main phases. In the first phase
signal separation is performed using the Fuzzy C-Means (FCM) algorithm. Subsequently
a single (fused) signal based on the information provided from the sensor signals is
generated by the fusion engine. The information provided from the previous two phases
is used for fault detection in the third phase based on the Artificial Immune System
(AIS) negative selection mechanism.
The simulations and experiments for multiple sensor systems have confirmed the
strength of the new approach for online fusing and fault detection. The hybrid system
gives a fault tolerance by handling different problems such as noisy sensor signals and
multiple faulty sensors. This makes the new hybrid approach attractive for solving such
fusion problems and fault detection during real time operations. This hybrid system is extended for early fault detection in complex mechanical
systems based on a set of extracted features; these features characterize the collected
sensors data. The hybrid system is able to detect the onset of fault conditions which can
lead to critical damage or failure. This early detection of failure signs can provide more
effective information for any maintenance actions or corrective procedure decisions.
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Xiao, Wenchang. "Structural Health Monitoring and Fault Diagnosis based on Artificial Immune System." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/169.
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This thesis presents a development of Structural Health Monitoring (SHM) and Fault Diagnosis based on Artificial Immune System (AIS), a biology-inspired method motivated from the Biological Immune System (BIS). Using the antigen to model structural health or damage condition of specific characteristics and the antibody to represent an information system or a database that can identify the specific damage pattern, the AIS can detect structural damage and then take action to ensure the structural integrity. In this study the antibodies for SHM were first trained and then tested. The feature space in training includes the natural frequencies and the modal shapes extracted from the simulated structural response data including both free-vibration and seismic response data. The concepts were illustrated for a 2-DOF linear mass-spring-damper system and promising results were obtained. It has shown that the methodology can be effectively used to detect, locate, and assess damage if it occurred. Consistently good results were obtained for both feature spaces of the natural frequencies and the modal shapes extracted from both response data sets. As the only exception, some significant errors were observed in the result for the seismic response data when the second modal shape was used as the feature space. The study has shown great promises of the methodology for structural health monitoring, especially in the case when the measurement data are not sufficient. The work lays a solid foundation for future investigations on the AIS application for large-scale complex structures.
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Weerasinghe, Manori. "Fault detection and diagnosis for complex multivariable processes using neural networks." Thesis, Liverpool John Moores University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298141.
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Raju, Madhanmohan. "Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1393237072.
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Pelo, Herbert Leburu. "Evaluation of an advanced fault detection system using Koeberg nuclear power plant data / H.L. Pelo." Thesis, North-West University, 2013. http://hdl.handle.net/10394/9686.
Full textAbstract:
The control and protection system of early nuclear power plants (Generation II) have been designed and built on the then reliable analog system. Technology has evolved in recent times and digital system has replaced most analog technology in most industries. Due to safety precautions and robust licensing requirements in the nuclear industry, the analog and digital system works concurrent to each other in most control and protection systems of nuclear power plants. Due to the ageing, regular maintenance and intermittent operation, the analog plant system often gives faulty signals. The objective of this thesis is to simulate a transient using a simulator to reduce the effects of system faults on the nuclear plant control and protection system, by detecting the faults early. The following steps will be performed:
• validating the simulator measurements by simulating a normal operation,
• detecting faults early on in the system
These can be performed by resorting to a model that generates estimates of the correct sensors signal values based on actual readings and correlations among them. The next step can be performed by a fault detection module which determines early whether or not the plant systems are behaving normally and detects the fault. (Baraldi P. et al, 2010.)<br>Thesis (MSc (Engineering Sciences in Nuclear Engineering))--North-West University, Potchefstroom Campus, 2013.
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Wong, Kam Cheung. "Intelligent methods of power system components monitoring by artificial neural networks and optimisation using evolutionary computing techniques." Thesis, University of Sunderland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285580.
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Sepasi, Mohammad. "Fault monitoring in hydraulic systems using unscented Kalman filter." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/206.
Full textAbstract:
Condition monitoring of hydraulic systems is an area that has grown
substantially in the last few decades. This thesis presents a scheme that
automatically generates the fault symptoms by on-line processing of raw sensor data
from a real test rig. The main purposes of implementing condition monitoring in
hydraulic systems are to increase productivity, decrease maintenance costs and
increase safety. Since such systems are widely used in industry and becoming more
complex in function, reliability of the systems must be supported by an efficient
monitoring and maintenance scheme.
This work proposes an accurate state space model together with a novel
model-based fault diagnosis methodology. The test rig has been fabricated in the
Process Automation and Robotics Laboratory at UBC. First, a state space model of
the system is derived. The parameters of the model are obtained through either
experiments or direct measurements and manufacturer specifications. To validate the
model, the simulated and measured states are compared. The results show that under
normal operating conditions the simulation program and real system produce similar
state trajectories.
For the validated model, a condition monitoring scheme based on the
Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and
process noises are considered. The results show that the algorithm estimates the
iii
system states with acceptable residual errors. Therefore, the structure is verified to
be employed as the fault diagnosis scheme.
Five types of faults are investigated in this thesis: loss of load, dynamic
friction load, the internal leakage between the two hydraulic cylinder chambers, and
the external leakage at either side of the actuator. Also, for each leakage scenario,
three levels of leakage are investigated in the tests. The developed UKF-based fault
monitoring scheme is tested on the practical system while different fault scenarios
are singly introduced to the system. A sinusoidal reference signal is used for the
actuator displacement. To diagnose the occurred fault in real time, three criteria,
namely residual moving average of the errors, chamber pressures, and actuator
characteristics, are considered. Based on the presented experimental results and
discussions, the proposed scheme can accurately diagnose the occurred faults.
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Moncayo, Hever Y. "Immunity-based detection, identification, and evaluation of aircraft sub-system failures." Morgantown, W. Va. : [West Virginia University Libraries], 2009. http://hdl.handle.net/10450/10678.
Full textAbstract:
Thesis (Ph. D.)--West Virginia University, 2009.<br>Title from document title page. Document formatted into pages; contains xiv, 118 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 109-118).
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Pietersen, Willem Hermanus. "System identification for fault tolerant control of unmanned aerial vehicles." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4164.
Full textAbstract:
Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010.<br>ENGLISH ABSTRACT: In this project, system identification is done on the Modular Unmanned Aerial Vehicle
(UAV). This is necessary to perform fault detection and isolation, which is part
of the Fault Tolerant Control research project at Stellenbosch University.
The equations necessary to do system identification are developed. Various methods
for system identification is discussed and the regression methods are implemented.
It is shown how to accommodate a sudden change in aircraft parameters
due to a fault. Smoothed numerical differentiation is performed in order to acquire
data necessary to implement the regression methods.
Practical issues regarding system identification are discussed and methods for
addressing these issues are introduced. These issues include data collinearity and
identification in a closed loop.
The regression methods are implemented on a simple roll model of the Modular
UAV in order to highlight the various difficulties with system identification. Different
methods for accommodating a fault are illustrated.
System identification is also done on a full nonlinear model of the Modular UAV.
All the parameters converges quickly to accurate values, with the exception of Cl R
,
CnP and Cn A
. The reason for this is discussed. The importance of these parameters
in order to do Fault Tolerant Control is also discussed.
An S-function that implements the recursive least squares algorithm for parameter
estimation is developed. This block accommodates for the methods of applying the
forgetting factor and covariance resetting. This block can be used as a stepping stone
for future work in system identification and fault detection and isolation.<br>AFRIKAANSE OPSOMMING: In hierdie projek word stelsel identifikasie gedoen op die Modulêre Onbemande Vliegtuig.
Dit is nodig om foutopsporing en isolasie te doen wat ’n deel uitmaak van fout
verdraagsame beheer.
Die vergelykings wat nodig is om stelsel identifikasie te doen is ontwikkel. Verskeie
metodes om stelsel identifikasie te doen word bespreek en die regressie metodes is
uitgevoer. Daar word gewys hoe om voorsiening te maak vir ’n skielike verandering
in die vliegtuig parameters as gevolg van ’n fout. Reëlmatige numeriese differensiasie
is gedoen om data te verkry wat nodig is vir die uitvoering van die regressie metodes.
Praktiese kwessies aangaande stelsel identifikasie word bespreek en metodes om
hierdie kwessies aan te spreek word gegee. Hierdie kwessies sluit interafhanklikheid
van data en identifikasie in ’n geslote lus in.
Die regressie metodes word toegepas op ’n eenvoudige rol model van die Modulêre
Onbemande Vliegtuig om die verskeie kwessies aangaande stelsel identifikasie uit te
wys. Verskeie metodes vir die hantering vir ’n fout word ook illustreer.
Stelsel identifikasie word ook op die volle nie-lineêre model van die Modulêre
Onbemande Vliegtuig gedoen. Al die parameters konvergeer vinnig na akkurate
waardes, met die uitsondering van Cl R
, CnP and Cn A
. Die belangrikheid van
hierdie parameters vir fout verdraagsame beheer word ook bespreek.
’n S-funksie blok vir die rekursiewe kleinste-kwadraat algoritme is ontwikkel. Hierdie
blok voorsien vir die metodes om die vergeetfaktor en kovariansie herstelling
te implementeer. Hierdie blok kan gebruik word vir toekomstige werk in stelsel
identifikasie en foutopsporing en isolasie.
49
Andrade, Vasco Brogueira. "Fault Detection and Performance Monitoring in PV Systems." Master's thesis, 2017. https://repositorio-aberto.up.pt/handle/10216/91023.
Full textAbstract:
Given the exponential growth of the PV sector in recent years and the market?s overall need for new PV monitoring solutions, this dissertation aims at creating an automatic fault detection tool for PV systems, more specifically for shading and soiling situations. By detecting deviations in the measured PV systems? data patterns, this tool aims at providing essential information for the deployment of the right maintenance strategy for each situation.
50
Andrade, Vasco Brogueira. "Fault Detection and Performance Monitoring in PV Systems." Dissertação, 2017. https://repositorio-aberto.up.pt/handle/10216/91023.
Full textAbstract:
Given the exponential growth of the PV sector in recent years and the market?s overall need for new PV monitoring solutions, this dissertation aims at creating an automatic fault detection tool for PV systems, more specifically for shading and soiling situations. By detecting deviations in the measured PV systems? data patterns, this tool aims at providing essential information for the deployment of the right maintenance strategy for each situation.