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1
Liu, Haoran. "Statistical and intelligent methods for default diagnosis and loacalization in a continuous tubular reactor." Phd thesis, INSA de Rouen, 2009. http://tel.archives-ouvertes.fr/tel-00560886.
Повний текст джерелаАнотація:
The aim is to study a continuous chemical process, and then analyze the hold process of the reactor and build the models which could be trained to realize the fault diagnosis and localization in the process. An experimental system has been built to be the research base. That includes experiment part and record system. To the diagnosis and localization methods, the work presented the methods with the data-based approach, mainly the Bayesian network and RBF network based on GAAPA (Genetic Algorithm with Auto-adapted of Partial Adjustment). The data collected from the experimental system are used to train and test the models.
2
Guo, Ran. "Intelligent method for collecting vital signals in versatile distributed e-home healthcare." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691807.
Повний текст джерела
3
Herrera, Liana J. Marmol. "Artificial intelligence methods for the diagnosis of myocardial damage in Chagas' disease using electrocardiographic signals." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314320.
Повний текст джерела
4
Bezha, Minella. "Development of deterioration diagnostic methods for secondary batteries used in industrial applications by means of artificial intelligence." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13127438/?lang=0, 2020. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13127438/?lang=0.
Повний текст джерелаАнотація:
蓄電池は携帯機器，電気自動車をはじめ，自然エネルギー有効利用に至るまで広範囲に利用され，その重要性はますます高まっている。これら機器の使用時間や特性は蓄電池の特性に大きく依存することから，電池自体の特性改善に加え，劣化を診断してより効率的に電池を運用することが求められている。本論文は，非線形情報処理を得意とする人工知能を用いた2次電池の劣化診断法を開発し，エネルギーの有効利用に資する技術を確立した。機器動作時の電池電圧・電流波形と電池劣化特性との関連性を，人工知能を用い学習することにより，機器稼働時に電池の劣化を診断することができる。なお，この関連性は非線形で複雑であるが，非線形分析を得意とする人工知能は劣化診断に適している。学習には時間を要するものの，診断は短時間になし得ることから，提案法は稼働時劣化診断に適している。本論文では，この特徴を生かし，電池の等価回路（ECM）を導出し，充電率（SOC），容量維持率（SOH）を推定している。また，本論文では現在産業応用分野で用いられている，リチウムイオン電池，ニッケル水素電池，鉛蓄電池を対象とし，提案法はあらゆる電池使用機器に応用可能である。また，提案法を電池状態監視装置（BMU）や，マイコンなどを用いた組み込みシステムに応用可能とし，実証している。以上のことから，本論文は，新たな蓄電池の劣化診断法の確立し，その有効性を確認している。The importance of rechargeable batteries nowadays is increasing from the portable electronic devices and solar energy industry up to the development of new EV models. The rechargeable batteries have a crucial role in the storage system, mostly in mobile applications and transportation, because the period of its usage and the flexibility of the function are determined by the battery. Due to the black box approach of the ANN it is possible to connect the complex physical phenomenon with a specific physical meaning expressed with a nonlinear logic between inputs and output. Using specific input data to relate with the desired output, makes possible to create a pattern connection with input and output. This ability helps to estimate in real time the desired outputs, behaviors, phenomes and at the same time it can be used as a real time diagnosis method.
博士(工学)
Doctor of Philosophy in Engineering
同志社大学
Doshisha University
5
Pous, i. Sabadí Carles. "Case based reasoning as an extension of fault dictionary methods for linear electronic analog circuits diagnosis." Doctoral thesis, Universitat de Girona, 2004. http://hdl.handle.net/10803/7728.
Повний текст джерелаАнотація:
El test de circuits és una fase del procés de producció que cada vegada pren més importància quan es desenvolupa un nou producte. Les tècniques de test i diagnosi per a circuits digitals han estat desenvolupades i automatitzades amb èxit, mentre que aquest no és encara el cas dels circuits analògics. D'entre tots els mètodes proposats per diagnosticar circuits analògics els més utilitzats són els diccionaris de falles. En aquesta tesi se'n descriuen alguns, tot analitzant-ne els seus avantatges i inconvenients.Durant aquests últims anys, les tècniques d'Intel·ligència Artificial han esdevingut un dels camps de recerca més importants per a la diagnosi de falles. Aquesta tesi desenvolupa dues d'aquestes tècniques per tal de cobrir algunes de les mancances que presenten els diccionaris de falles. La primera proposta es basa en construir un sistema fuzzy com a eina per identificar. Els resultats obtinguts son força bons, ja que s'aconsegueix localitzar la falla en un elevat tant percent dels casos. Per altra banda, el percentatge d'encerts no és prou bo quan a més a més s'intenta esbrinar la desviació.
Com que els diccionaris de falles es poden veure com una aproximació simplificada al Raonament Basat en Casos (CBR), la segona proposta fa una extensió dels diccionaris de falles cap a un sistema CBR. El propòsit no és donar una solució general del problema sinó contribuir amb una nova metodologia. Aquesta consisteix en millorar la diagnosis dels diccionaris de falles mitjançant l'addició i l'adaptació dels nous casos per tal d'esdevenir un sistema de Raonament Basat en Casos. Es descriu l'estructura de la base de casos així com les tasques d'extracció, de reutilització, de revisió i de retenció, fent èmfasi al procés d'aprenentatge.
En el transcurs del text s'utilitzen diversos circuits per mostrar exemples dels mètodes de test descrits, però en particular el filtre biquadràtic és l'utilitzat per provar les metodologies plantejades, ja que és un dels benchmarks proposats en el context dels circuits analògics. Les falles considerades son paramètriques, permanents, independents i simples, encara que la metodologia pot ser fàcilment extrapolable per a la diagnosi de falles múltiples i catastròfiques. El mètode es centra en el test dels components passius, encara que també es podria extendre per a falles en els actius.
Testing circuits is a stage of the production process that is becoming more and more important when a new product is developed. Test and diagnosis techniques for digital circuits have been successfully developed and automated. But, this is not yet the case for analog circuits. Even though there are plenty of methods proposed for diagnosing analog electronic circuits, the most popular are the fault dictionary techniques. In this thesis some of these methods, showing their advantages and drawbacks, are analyzed.
During these last decades automating fault diagnosis using Artificial Intelligence techniques has become an important research field. This thesis develops two of these techniques in order to fill in some gaps in fault dictionaries techniques. The first proposal is to build a fuzzy system as an identification tool. The results obtained are quite good, since the faulty component is located in a high percentage of the given cases. On the other hand, the percentage of successes when determining the component's exact deviation is far from being good.
As fault dictionaries can be seen as a simplified approach to Case-Based Reasoning, the second proposal extends the fault dictionary towards a Case Based Reasoning system. The purpose is
not to give a general solution, but to contribute with a new methodology. This second proposal improves a fault dictionary diagnosis by means of adding and adapting new cases to develop a
Case Based Reasoning system. The case base memory, retrieval, reuse, revise and retain tasks are described. Special attention to the learning process is taken.
Several circuits are used to show examples of the test methods described throughout the text. But, in particular, the biquadratic filter is used to test the proposed methodology because it is
defined as one of the benchmarks in the analog electronic diagnosis domain. The faults considered are parametric, permanent, independent and simple, although the methodology can be extrapolated to catastrophic and multiple fault diagnosis. The method is only focused and tested on passive faulty components, but it can be extended to cover active devices as well.
6
Behroozinia, Pooya. "Finite Element Analysis of Defects in Cord-Rubber Composites and Hyperelastic Materials." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87703.
Повний текст джерелаАнотація:
In recent years, composite materials have been widely used in several applications due to their superior mechanical properties including high strength, high stiffness, and low density. Despite the remarkable advancements in theoretical and computational methods for analyzing composites, investigating the effect of lamina properties and lay-up configurations on the strength of composites still remains an active field of research. Finite Element Method (FEM) and Extended Finite Element Method (XFEM) are powerful tools for solving the boundary value problems. One of the objectives of this work is to employ XFEM as a defect identification tool for predicting the crack initiation and propagation in composites. Another major objective of this study is to investigate the damage development in hyperelastic materials. Two Finite Element models are adopted to study this phenomenon: multiscale modeling of the cord-rubber composites in tires and modeling of intelligent tires for evaluating the feasibility of the proposed defect detection technique.
A new three-dimensional finite element approach based on the multiscale progressive failure analysis is employed to provide the theoretical predictions for damage development in the cord-rubber composites in tires. This new three-dimensional model of the cord-rubber composite is proposed to predict the different types of damage including matrix cracking, delamination, and fiber failure based on the micro-scale analysis. This process is iterative and data is shared between the finite element and multiscale progressive failure analysis. It is shown that the proposed cord-rubber composite model solves the problems corresponding to embedding the rebar elements to the solid elements and also increases the fidelity of numerical analysis of composite parts since the laminate characteristic variables are determined from the microscopic parameters. A tire rolling analysis is then conducted to evaluate the effects of different variables corresponding to the cord-rubber composite on the performance of tires.
Tires operate on the principle of safe life and are the only parts of the vehicle which are in contact with the road surface. Establishing a computational method for defect detection in tire structures will help manufacturers to fix and develop more reliable tire designs. A Finite Element model of a tire with a tri-axial accelerometer attached to its inner-liner was developed and the effects of changing the normal load, longitudinal velocity and tire-road contact friction on the acceleration signal were investigated. Additionally, using the model, the acceleration signals obtained from several accelerometers placed in different locations around the inner-liner of the intelligent tire were analyzed and the defected areas were successfully identified. Using the new intelligent tire model, the lengths, locations, and the minimum number of accelerometers in damage detection in tires are determined. Comparing the acceleration signals obtained from the damaged and original tire models results in detecting defects in tire structures.PHD
7
Juuso, E. (Esko). "Integration of intelligent systems in development of smart adaptive systems:linguistic equation approach." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526202891.
Повний текст джерелаАнотація:
Abstract Smart adaptive systems provide advanced tools for monitoring, control, diagnostics and management of nonlinear multivariate processes. Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but highly complex systems require domain expertise and more compact approaches at the basic level. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. This research is focused on the smart adaptive applications, where different intelligent modules are used in a smart way. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are defined separately for the scaling and the interactions. The LE based intelligent analysers are useful in the multilevel LE control and diagnostics: the LE control is enhanced with the intelligent analysers, adaptive and model-based modules and high level control. The operating area is extended with the predefined adaptation and specific events activate appropriate control actions. The condition, stress and trend indices are used for the detection of operating conditions. The same overall structure is extended to the scheduling and managerial decision support. The linguistic representation becomes increasingly important when the human interaction is essential. The new scaling approach is used in control and diagnostic applications and discussed in connection with previous multivariate modelling cases. The LE based intelligent analysers are the key modules of the system integration, which produces hybrid systems: fuzzy systems move gradually to higher levels, neural networks and evolutionary computing are used for tuning. The overall system is reinforced with advanced statistical analysis, signal processing, feature extraction, classification and mechanistic modellingTiivistelmä Viisaat mukautuvat järjestelmät sisältävät kehittyneitä työkaluja epälineaaristen monimuuttujaisten prosessien valvontaan, säätöön, diagnostiikkaan ja johtamiseen. Laajaan menetelmäpohjaan perustuva tiedonrikastus on pohjana älykkäiden järjestelmien yhdistämiselle. Pienille erikoistuneille järjestelmille on monia toteutettavissa olevia ratkaisuja, mutta erittäin monimutkaiset järjestelmät vaativat alan asiantuntemusta ja kompakteja lähestymistapoja perustasolla. Sumeaan logiikkaan pohjautuva lingvististen yhtälöiden (linguistic equation, LE) menetelmä on tehokas ratkaisu näissä ongelma-alueissa. Tämä tutkimus kohdistuu viisaisiin mukautuviin sovelluksiin, jossa useita älykkäitä moduuleja käytetään yhdessä viisaalla tavalla. Kehittyneeseen tilastolliseen analyysiin perustuva epälineaarinen skaalausmenetelmä muodostaa ratkaisun kulmakiven: muuttujien merkitykset soveltuvat säädössä ja diagnostiikassa käytettävien älykkäiden indeksien kehittämiseen. Uudet rajoituksien käsittelymenetelmät yhdessä yleistettyjen normien ja momenttien kanssa mahdollistavat rekursiivisen parametriestimoinnin olosuhteisiin mukautuvassa skaalauksessa. Tunnettuja lineaarisia menetelmiä käytetään staattisessa, dynaamisessa ja tapauspohjaisessa mallintamisessa, jossa kaskadi- ja vuorovaikutusrakenteet laajentavat mallit tarvittaessa monimutkaisiin sovelluksiin. Prosessituntemuksen ja järjestelmien robustisuuden varmistamiseksi parametrit määritellään erikseen skaalausta ja vuorovaikutuksia varten. LE-pohjaiset älykkäät analysaattorit ovat hyödyllisiä monitasoisessa säädössä ja diagnostiikassa: LE-säätöä parannetaan älykkäiden analysaattorien, adaptiivisten ja mallipohjaisten moduulien sekä ylemmän tason säädön avulla. Käyttöaluetta laajennetaan ennalta määrätyllä adaptoinnilla sekä tiettyjen tapahtumien aktivoimilla erityisillä säätötoimenpiteillä. Kunto-, rasitus- ja trendi-indeksejä käytetään olosuhteiden tunnistamiseen. Sama rakenne laajennetaan tuotannon ajoitukseen ja päätöksenteontukeen, jossa inhimillisen vuorovaikutuksen käsittely tekee lingvistisen esityksen yhä tärkeämmäksi. Uutta skaalausmenetelmää tarkastellaan säätö- ja diagnostiikkasovelluksissa sekä vertaillaan lyhyesti sen käyttömahdollisuuksia aikaisemmin toteutetuissa monimuuttujamalleissa. LE-pohjaiset älykkäät analysaattorit ovat keskeisiä integroitaessa moduuleja hybridiratkaisuiksi: sumeat järjestelmät siirtyvät vähitellen ylemmille tasoille ja neuro- ja evoluutiolaskennassa keskitytään järjestelmien viritykseen. Kokonaisjärjestelmää vahvistetaan kehittyneellä tilastollisella analyysilla, signaalinkäsittelyllä, piirteiden erottamisella, luokittelulla ja mekanistisella mallintamisella
8
Bishop, James. "The Potential of Misdiagnosis of High IQ Youth by Practicing Mental Health Professionals: A Mixed Methods Study." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062851/.
Повний текст джерелаАнотація:
The difficulty of distinguishing between genuine disorder and characteristics that can be attributed to high IQ increases the likelihood of diagnostic error by mental health practitioners. This mixed methods study explores the possibility of misdiagnosis of high IQ youth by mental health professionals. Participants were private practice mental health professionals who read case study vignettes illustrating high IQ youth exhibiting characteristics associated with their population. Participants then completed a survey and provided an assessment of the hypothetical client. In the study, 59% of participants were unable to recognize behavioral characteristics associated with high IQ youth unless suggested to them, and 95% of participants were unable to recognize emotional characteristics associated with high IQ youth unless suggested. The results of this study provide much-needed empirical exploration of the concern for misdiagnosis of high IQ youth and inform clinical practice and education.
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Ващенко, Ярослав Васильович. "Удосконалення технології діагностування стану тягового асинхронного електроприводу рухомого складу". Thesis, Український державний університет залізничного транспорту, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/22714.
Повний текст джерелаАнотація:
Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.22.09 – електротранспорт. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016 р.
Дисертація присвячена вирішенню науково-технічної задачі по удосконаленню технології діагностування стану тягового асинхронного електроприводу рухомого складу на основі застосування діагностичних ознак, що свідчили б про настання аварійних режимів, а також розробці технологій, методів та алгоритмів, що дозволили б виявляти та упереджувати подальший розвиток таких режимів. Для виконання досліджень розроблені комп'ютерні математичні імітаційні моделі тягового асинхронного електроприводу, в яких враховуються особливості аварійних режимів в залежності від системи управління, насичення магнітного кола асинхронного двигуна та ін. Виконано експериментальне підтвердження адекватності розроблених імітаційних моделей з реальним тяговим приводом для рухомого складу. На основі розроблених моделей досліджено електромагнітні процеси, що відбуваються в аварійних режимах, що дозволило якісно та кількісно їх оцінити, а також визначити придатні для діагностування характерні ознаки. Розроблено технології діагностування на основі гармонічного аналізу сигналу та на основі математичної моделі об'єкту, проведено комп'ютерну перевірку та підтверджено ефективність роботи таких методів. Для здійснення автоматизації прийняття рішення використано моделювання математичного алгоритму штучних нейромереж.Thesis for a candidate degree by speciality 05.22.09 – Electric transport. – National Technical University "Kharkiv Polytechnical Institute", Kharkiv, 2016. Dissertation is devoted to solving scientific and technical targets improving technology of diagnosing state for traction asynchronous drive electric rolling stock by detecting abnormally dangerous and emergency modes operation and their identification, which allowed to develop methods for early detection and prevention of drive elements failure when it malfunctions occur, as well as minimizing operational costs. The analysis of existing technologies, techniques and methods for diagnosis and protection traction asynchronous drive showed that the most promising in comparison with the existing protection systems of rolling stock, which operate on the principle of control deviations of parameters and prevent the development of emergency modes, there are diagnostics technology provides detection and localization of failures in the early stages. Improved diagnosis technology based on the object model of traction induction motor by using the extended Kalman filter that can detect damage to the stator and rotor windings of traction induction motor, for which proposed to use statistical criteria in real time for assessing its effectiveness To automate the decision approach applied mathematical algorithm simulation based on artificial neural networks for diagnostic feature variable speed oscillation induction motor rotor frequency, with which is possible to exercise effective intellectual automatic fault detection when using simple logical principles is not enough. Developed diagnosis methods are expand existing protection technologies including real technical state of asynchronous traction electric drive and allowing to perform timely malfunctions detection and automatic decision-making to prevent further development of emergency operation, thereby increasing efficiency and reliability traction drive operation.
10
Ващенко, Ярослав Васильович. "Удосконалення технології діагностування стану тягового асинхронного електроприводу рухомого складу". Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/22713.
Повний текст джерелаАнотація:
Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.22.09 – електротранспорт. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016 р.
Дисертація присвячена вирішенню науково-технічної задачі по удосконаленню технології діагностування стану тягового асинхронного електроприводу рухомого складу на основі застосування діагностичних ознак, що свідчили б про настання аварійних режимів, а також розробці технологій, методів та алгоритмів, що дозволили б виявляти та упереджувати подальший розвиток таких режимів. Для виконання досліджень розроблені комп'ютерні математичні імітаційні моделі тягового асинхронного електроприводу, в яких враховуються особливості аварійних режимів в залежності від системи управління, насичення магнітного кола асинхронного двигуна та ін. Виконано експериментальне підтвердження адекватності розроблених імітаційних моделей з реальним тяговим приводом для рухомого складу. На основі розроблених моделей досліджено електромагнітні процеси, що відбуваються в аварійних режимах, що дозволило якісно та кількісно їх оцінити, а також визначити придатні для діагностування характерні ознаки. Розроблено технології діагностування на основі гармонічного аналізу сигналу та на основі математичної моделі об'єкту, проведено комп'ютерну перевірку та підтверджено ефективність роботи таких методів. Для здійснення автоматизації прийняття рішення використано моделювання математичного алгоритму штучних нейромереж.Thesis for a candidate degree by speciality 05.22.09 – Electric transport. – National Technical University "Kharkiv Polytechnical Institute", Kharkiv, 2016. Dissertation is devoted to solving scientific and technical targets improving technology of diagnosing state for traction asynchronous drive electric rolling stock by detecting abnormally dangerous and emergency modes operation and their identification, which allowed to develop methods for early detection and prevention of drive elements failure when it malfunctions occur, as well as minimizing operational costs. The analysis of existing technologies, techniques and methods for diagnosis and protection traction asynchronous drive showed that the most promising in comparison with the existing protection systems of rolling stock, which operate on the principle of control deviations of parameters and prevent the development of emergency modes, there are diagnostics technology provides detection and localization of failures in the early stages. Improved diagnosis technology based on the object model of traction induction motor by using the extended Kalman filter that can detect damage to the stator and rotor windings of traction induction motor, for which proposed to use statistical criteria in real time for assessing its effectiveness To automate the decision approach applied mathematical algorithm simulation based on artificial neural networks for diagnostic feature variable speed oscillation induction motor rotor frequency, with which is possible to exercise effective intellectual automatic fault detection when using simple logical principles is not enough. Developed diagnosis methods are expand existing protection technologies including real technical state of asynchronous traction electric drive and allowing to perform timely malfunctions detection and automatic decision-making to prevent further development of emergency operation, thereby increasing efficiency and reliability traction drive operation.
11
Hamad, Adnan. "Intelligent fault diagnosis for automotive engines." Thesis, Liverpool John Moores University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590082.
Повний текст джерелаАнотація:
Fault detection and isolation (FDI) has become one of the most important aspects of automobile design. In this thesis, a new fault detection and isolation approach is developed for automotive engines. The method uses an independent radial basis function neural network model to model engine dynamics, and the modelling errors are used to from the basis for residual generation. Furthermore, another radial basis function neural network is used as a fault classifier to isolate an occurred fault from other possible faults in the system by classifying fault characteristics embedded in the modelling errors. The performance of the developed scheme is assessed using an engine benchmark, the Mean Value Engine Model CMVEM), with Matlab/Simulink. Five faults have been simulated on the MVEM: three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10~20% changes superimposed on the measured outputs of manifold pressure, manifold temperature and crankshaft speed sensors; the component fault considered is air leakage in the intake manifold; and the actuator fault considered is the malfunction of the fuel injector. The simulation results show that all the simulated faults can be clearly detected and isolated in dynamic conditions throughout the engine operating range. Furthermore, in order to reflect the real state for an automotive engine, the FDI method is evaluated for the MVEM system under closed-loop control with air fuel ratio control. An independent radial basis function (RBF) neural network model is used to model engine dynamics using random amplitude signals (RAS) throttle angle as an input.
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Kabir, Mashud. "Intelligent system for fault diagnosis in automotive applications." [S.l. : s.n.], 2008.
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Wright, Jonathan. "Intelligent methods for locomotion optimisation." Thesis, University of Portsmouth, 2015. https://researchportal.port.ac.uk/portal/en/theses/intelligent-methods-for-locomotion-optimisation(306f8931-16b5-4b75-9bfc-d75c070af420).html.
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This thesis presents, critical compares and develops new methods to control and optimise locomotion for a range of systems. Jumping and running locomotion skills are examined in detail, and intelligent methods are discussed and adapted to optimise for correct form of motion, and performance outcomes. Existing control techniques are summarised and compared, including traditional analytical methods, central pattern generator oscillator systems, pattern generating neural networks, rule based systems and other specialist methods. Optimisation and learning methods presented in the literature are also summarised, and while several methods exist, modern global search methods were limited to genetic algorithms. This thesis applies particle swarm optimisation and quantum inspired evolutionary algorithms to vertical jump and walking optimisation, comparing their performance to a genetic algorithm. Improvements were developed for both binary and real-value variants of quantum inspired evolutionary algorithms, to benefit performance on the real-value problems involved in locomotion control. These improvements consisted of modifications to their rotation gate operators, including a novel scheme to reduce premature convergence in the binary methods, based on limiting the range of less significant bits. Methods were applied in simulated environments, although they can be adapted to real world robotic control, or for reference in optimising motion in humans. A discussion of the susceptibility of simulation runs to poor physical modelling was presented, as this was a significant problem during research. Results were generally mixed, to the extent that all tested methods may be usefully examined more in future work. The central pattern generators tested generated successful patterns more often than a recurrent neural network, and the results of the optimisation algorithms did not show sufficient advantage of one over the others.
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Турова, Людмила Олександрівна, Людмила Александровна Турова, Liudmyla Oleksandrivna Turova, and W. A. Alsaedi. "New methods in prenatal diagnosis." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/32308.
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The development of genotyping and sequencing techniques has been dramatic during the recent years. Now it is possible to obtain a full view over an individual’ s genetic landscape in the form of a million common single-nucleotide polymorphisms (SNPs) in single and affordable experiments.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/32308
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Wang, Peng. "Intelligent signal/image processing for fault diagnosis and prognosis." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/13308.
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Jain, Ravi. "Intelligent techniques for the diagnosis of coronary artery disease /." Title page, contents and abstract only, 1998. http://web4.library.adelaide.edu.au/theses/09PH/09phj248.pdf.
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Zhou, Ji. "Intelligent fault diagnosis with applications to gas turbine engines." Thesis, University of Sheffield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284354.
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Zarkadis, George. "An intelligent decision support system for acid-base diagnosis." Thesis, City University London, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235504.
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Kosov, Sergey. "Intelligent pressure control and diagnosis of water distribution networks." Thesis, De Montfort University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391542.
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Pavlou, Alexandros K. "Novel intelligent gas-sensing in diagnosis of infectious diseases." Thesis, Cranfield University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399121.
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Tang, Meng. "The Adaptive Intelligent Model for Process Diagnosis, Prediction and Control." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for produksjons- og kvalitetsteknikk, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-328.
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This research work focuses at first on the intelligent model development for process state (special for fault) detection, behavior prediction and process control for complex industrial processes. In the model architecture, Fuzzy Neural Networks (FNNs) are employed as process state classifiers for process state (fault) detection; other (different) Neural Networks (NNs) models are applied for system identification of process characteristics in different process states. The model detects process states (faults) and predicts process behavior according to process input and historical behaviors, whose combination of influences generates the final results of process state (fault) detection and quantitative prediction. The whole model is constructed based on Fuzzy TS NARX models. Secondly, an optimal model is designed to two purposes, one is for optimal process diagnosis and another is for optimal prediction. To time varying processes, an adaptive strategy and algorithm, applying the Least Squares algorithm, has been developed for model adaptability to cover time depending process changes. Thirdly, a specific state space equation of discrete time varying system is being derived from the model. In the state space equation, the state transition matrix A is determined by the fuzzy degree of process state classification produced by process historical behavior in time t instant, and the input transition matrix B by process real input in time t instant. The state observer vector H is determined by optimization results generated by model adaptive or optimal scheme. Finally, to confirm the validity of the theoretical results from above, an application case has been studied for supply forecasting. The study and application results indicate that the model not only has good performance for fault detection, but also provides excellent quantitative prediction of process output. It can be applied in process state (fault) detection, diagnosis and prediction for process behavior, as well as fault predictive control.
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Calabrese, Francesca. "Vibration Monitoring and Intelligent Diagnosis Tools for Condition-Based Maintenance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
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Ogni impianto di produzione è caratterizzato da periodi di operatività, nei quali funziona correttamente, e da periodi di fermo, dovuti alla presenza di guasti o all’esigenza di effettuare attività volte a ristabilire il suo normale comportamento. L’obiettivo principale della funzione manutenzione è minimizzare i periodi di fermo impianto, al fine di renderlo il più disponibile possibile.
Attualmente, la manutenzione basata su condizione (CBM) è una delle più politiche più efficaci adottate dalle industrie. Essa è basata sul monitoraggio di diversi parametri della macchina che ne riflettono lo stato di salute. Tra i parametri più utilizzati si trovano i segnali di vibrazione. La CBM può essere implementata attraverso quattro passi principali: raccolta dati, analisi dei segnali, diagnostica e prognostica. Tale procedura prende il nome di Prognostic Health Monitoring (PHM).
La necessità di analizzare la grande mole di dati raccolta attraverso il vibration monitoring richiede l’utilizzo di metodi sviluppati nell’ambito della teoria statistica e del data mining, che si pongono l’obiettivo di riconoscere andamenti regolari all’interno di grandi insiemi di dati, al fine di generare conoscenza funzionale al processo decisionale manutentivo. In particolare, i modelli di classificazione, come alberi decisionali, algoritmi K-NN, reti neurali e Support Vector Machine, costituiscono un potente strumento per la diagnostica. Tali modelli, sulla base del PHM, vengono applicati dopo la fase di analisi dei segnali, che consiste principalmente nell’estrazione di features sia nel dominio del tempo che nel dominio tempo-frequenza.
Il risultato principale ottenuto consiste nell’aver verificato un incremento delle performance, in termini di accuratezza, della classificazione dello stato di salute di un componente, dovuto all’introduzione dell’analisi nel dominio tempo-frequenza e allo sviluppo dei nuovi metodi “intelligenti”.
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Han, Yu. "Intelligent partial discharge diagnosis using SOM for turbogenerator condition monitoring." Thesis, Brunel University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249950.
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Tian, Jia. "Intelligent traditional Chinese medicine diagnosis support using multi modal reasoning." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442270.
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Sangha, Mahavir Singh. "Intelligent fault diagnosis for automative engines and real data evaluation." Thesis, Liverpool John Moores University, 2008. http://researchonline.ljmu.ac.uk/5867/.
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Van, Niekerk Theo. "Monitoring a diagnosis for control of an intelligent machining process." Thesis, Port Elizabeth Technikon, 2001. http://hdl.handle.net/10948/70.
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A multi-level modular control scheme to realize integrated process monitoring, diagnosis and control for intelligent machining is proposed and implemented. PC-based hardware architecture to manipulate machining process cutting parameters, using a PMAC interface card as well as sensing processes performance parameters through sampling, and processing by means of DSP interface cards is presented. Controller hardware, to interface the PC-based PMAC interface card to a machining process for the direct control of speed, feed and depth of cut, is described. Sensors to directly measure on-line process performance parameters, including cutting forces, cutting sound, tool-workpiece vibration, cutting temperature and spindle current are described. The indirect measurement of performance parameter surface roughness and tool wear monitoring, through the use of NF sensor fusion modeling, is described and verified. An object based software architecture, with corresponding user interfaces (using Microsoft Visual C++ Foundation Classes and implemented C++ classes for sending motion control commands to the PMAC and receiving processed on-line sensor data from the DSP) is explained. The software structure indicates all the components necessary for integrating the monitoring, diagnosis and control scheme. C-based software code executed on the DSP for real-time sampling, filtering and FFT processing of sensor signals, is explained. Making use of experimental data and regression analysis, analytical relationships between cutting parameters (independent) and each of the performance parameters (dependent) are obtained and used to simulate the machining process. A fuzzy relation that contains values determined from statistical data (indicating the strength of connection between the independent and dependent variables) is proposed. The fuzzy relation forms the basis of a diagnostic scheme that is able to intelligently determine which independent variable to change when a machining performance parameter exceeds control limits. The intelligent diagnosis scheme is extensively tested using the machining process simulation.
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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.
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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.
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Ma, Yingnan. "Intelligent energy management system : techniques and methods." Thesis, City University London, 2011. http://openaccess.city.ac.uk/1212/.
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ABSTRACT Our environment is an asset to be managed carefully and is not an expendable resource to be taken for granted. The main original contribution of this thesis is in formulating intelligent techniques and simulating case studies to demonstrate the significance of the present approach for achieving a low carbon economy. Energy boosts crop production, drives industry and increases employment. Wise energy use is the first step to ensuring sustainable energy for present and future generations. Energy services are essential for meeting internationally agreed development goals. Energy management system lies at the heart of all infrastructures from communications, economy, and society’s transportation to the society. This has made the system more complex and more interdependent. The increasing number of disturbances occurring in the system has raised the priority of energy management system infrastructure which has been improved with the aid of technology and investment; suitable methods have been presented to optimize the system in this thesis. Since the current system is facing various problems from increasing disturbances, the system is operating on the limit, aging equipments, load change etc, therefore an improvement is essential to minimize these problems. To enhance the current system and resolve the issues that it is facing, smart grid has been proposed as a solution to resolve power problems and to prevent future failures. This thesis argues that smart grid consists of computational intelligence and smart meters to improve the reliability, stability and security of power. In comparison with the current system, it is more intelligent, reliable, stable and secure, and will reduce the number of blackouts and other failures that occur on the power grid system. Also, the thesis has reported that smart metering is technically feasible to improve energy efficiency. In the thesis, a new technique using wavelet transforms, floating point genetic algorithm and artificial neural network based hybrid model for gaining accurate prediction of short-term load forecast has been developed. Adopting the new model is more accuracy than radial basis function network. Actual data has been used to test the proposed new method and it has been demonstrated that this integrated intelligent technique is very effective for the load forecast. Choosing the appropriate algorithm is important to implement the optimization during the daily task in the power system. The potential for application of swarm intelligence to Optimal Reactive Power Dispatch (ORPD) has been shown in this thesis. After making the comparison of the results derived from swarm intelligence, improved genetic algorithm and a conventional gradient-based optimization method, it was concluded that swam intelligence is better in terms of performance and precision in solving optimal reactive power dispatch problems.
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He, Shengyang. "Modeling power system load using intelligent methods." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/12036.
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Master of ScienceDepartment of Electrical Engineering
Shelli K. Starrett
Modern power systems are integrated, complex, dynamic systems. Due to the complexity, power system operation and control need to be analyzed using numerical simulation. The load model is one of the least known models among the many components in the power system operation. The two different load models are the static and dynamic models. The ZIP load model has been extensively studied. This has widely applied to composite load models that could maintain constant impedance, constant current, and/or constant power. In this work, various Neural Networks algorithms and fuzzy logic have been used to obtain these ZIP load model coefficients for determining the percentage of constant impedance, current, or power for the various load buses. The inputs are a combination of voltage, voltage change, and power change, or voltage and power, and the outputs consist of the ZIP load model coefficients for determining the type and the percentage of load at the bus. The trained model is used to predict the type and percentage of constant load at other buses using simulated transient data from the 16-generator system. A small study was also done using a dynamic induction machine model in addition to the ZIP load model. As expected, the results show that the dynamic model is more difficult to determine than the static model.
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Alani, Shayma. "Design of intelligent ensembled classifiers combination methods." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/12793.
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Classifier ensembling research has been one of the most active areas of machine learning for a long period of time. The main aim of generating combined classifier ensembles is to improve the prediction accuracy in comparison to using an individual classifier. A combined classifiers ensemble can improve the prediction results by compensating for the individual classifier weaknesses in certain areas and benefiting from better accuracy of the other ensembles in the same area. In this thesis, different algorithms are proposed for designing classifier ensemble combiners. The existing methods such as averaging, voting, weighted average, and optimised weighted method does not increase the accuracy of the combiner in comparison to the proposed advanced methods such as genetic programming and the coalition method. The different methods are studied in detail and analysed using different databases. The aim is to increase the accuracy of the combiner in comparison to the standard stand-alone classifiers. The proposed methods are based on generating a combiner formula using genetic programming, while the coalition is based on estimating the diversity of the classifiers such that a coalition is generated with better prediction accuracy. Standard accuracy measures are used, namely accuracy, sensitivity, specificity and area under the curve, in addition to training error accuracies such as the mean square error. The combiner methods are compared empirically with several stand-alone classifiers using neural network algorithms. Different types of neural network topologies are used to generate different models. Experimental results show that the combiner algorithms are superior in creating the most diverse and accurate classifier ensembles. Ensembles of the same models are generated to boost the accuracy of a single classifier type. An ensemble of 10 models of different initial weights is used to improve the accuracy. Experiments show a significant improvement over a single model classifier. Finally, two combining methods are studied, namely the genetic programming and coalition combination methods. The genetic programming algorithm is used to generate a formula for the classifiers’ combinations, while the coalition method is based on a simple algorithm that assigns linear combination weights based on the consensus theory. Experimental results of the same databases demonstrate the effectiveness of the proposed methods compared to conventional combining methods. The results show that the coalition method is better than genetic programming.
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Betschart, Willie. "Applying intelligent statistical methods on biometric systems." Thesis, Blekinge Tekniska Högskola, Avdelningen för signalbehandling, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1694.
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This master’s thesis work was performed at Optimum Biometric Labs, OBL, located in Karlskrona, Sweden. Optimum Biometric Labs perform independent scenario evaluations to companies who develop biometric devices. The company has a product Optimum preConTM which is surveillance and diagnosis tool for biometric systems. This thesis work’s objective was to develop a conceptual model and implement it as an additional layer above the biometric layer with intelligence about the biometric users. The layer is influenced by the general procedure of biometrics in a multimodal behavioural way. It is working in an unsupervised way and performs in an unsupervised manner. While biometric systems are increasingly adopted the technologies have some inherent problems such as false match and false non-match. In practice, a rejected user can not be interpreted as an impostor since the user simply might have problems using his/her biometric feature. The proposed methods in this project are dealing with these problems when analysing biometric usage in runtime. Another fact which may give rise to false rejections is template aging; a phenomenon where the enrolled user’s template is too old compared towards the user’s current biometric feature. A theoretical approach of template aging was known; however since the analysis of template aging detection was correlated with potential system flaws such as device defects or human generated risks such as impostor attacks this task would become difficult to solve in an unsupervised system but when ignoring the definition of template aging, the detection of similar effects was possible. One of the objectives of this project was to detect template aging in a predictive sense; this task failed to be carried out because the absence of basis performing this kind of tasks. The developed program performs abnormality detection at each incoming event from a biometric system. Each verification attempt is assumed to be from a genuine user unless any deviation according to the user's history is found, an abnormality. The possibility of an impostor attack depends on the degree of the abnormality. The application makes relative decisions between fraud possibilities or if genuine user was the source of what caused the deviations. This is presented as an alarm with the degree of impostor possibility. This intelligent layer has increased Optimum preCon´s capacity as a surveillance tool for biometrics. This product is an efficient complement to biometric systems in a steady up-going worldwide market.
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Petrov, Nedyalko. "Intelligent methods for pattern recognition and optimisation." Thesis, University of Portsmouth, 2015. https://researchportal.port.ac.uk/portal/en/theses/intelligent-methods-for-pattern-recognition-and-optimisation(13d07545-80a6-4804-b095-a48704dda2c0).html.
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This dissertation presents and discusses the processes of investigation, implementation, testing, validation and evaluation of several computational intelligence-based systems for solving four large-scale real-world problems. In particular, two industrial problems from the pattern recognition and two from the process optimisation areas are studied and intelligent methods to address them are proposed, developed and tested using real-world data. The first problem investigated is the application of an intelligent visual inspection system for classification of texture images. Two major approaches, incorporating supervised and unsupervised (without a priori knowledge) learning techniques, are considered and neural network based classifiers are trained. The focus is kept on the application of unsupervised non-linear dimensionality reduction techniques in combination with unsupervised classification methods. A number of experiments and simulations are performed to evaluate the proposed approaches and the results are critically compared. Next, a classification problem for timely and reliable identification of emitters of radar signals is investigated. A large data set, containing a considerable amount of missing data is used. Several techniques for dealing with the incomplete data values are employed, including listwise deletion and multiple imputation. Methods incorporating neural network classifiers are studied and the proposed approaches are tested and validated over a number of simulations in the MATLAB environment. The third large-scale problem, presented in this work, addresses the need for optimisation of a thermodynamics first principle-based prediction model for simulation of a major purifying process, used in British Petroleum (BP) refineries. A technique incorporating genetic algorithms is applied for optimising a number of the model parameters and for closing up the gaps between the predicted and measured data. Several functions and a graphical user interface (GUI) tool are implemented in MATLAB to assist the analysis, optimisation, testing and validation of the investigated model. Significant overall improvement in its prediction capabilities is achieved. The final problem, covered in this research work, is the need to improve the convergence rate of a computationally very expensive aerodynamic optimisation process. It is addressed by exploring some physics-grounded heuristics and presenting a novel intelligent approach for automated shape optimisation. A set of basis functions (for spanning the design space) is derived in such a way that they facilitate the work of a time-consuming and expensive computational fluid dynamics (CFD) optimisation process. Two MATLAB-based GUI tools are developed to support the calculation, exploration, testing and validation of the studied approach. Experiments for optimising real aircraft geometry are run on supercomputers through an industrial partner (AIRBUS Operations Ltd). The initial results show very promising opportunities for improving the convergence rate of the slow optimisation process.
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Dadgar, Ashraf. "Methods for identification and diagnosis of amyloidosis." Thesis, Uppsala University, Department of Medical Biochemistry and Microbiology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7175.
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The amyloidoses are biochemically heterogeneous diseases with patholophysiologic deposits of various proteins. Amyloid deposits can occur either localized to one organ or tissue or as part of a systemic disease with deposits in many different tissue. The clinical course, prognosis and therapy are different for each type of amyloidosis and therefore a type specific diagnosis is demanded as early as possible. We describe a method for typing of the most common systemic amyloidoses based on Western blot analysis combined with specific
in- house antibodies, using subcutaneous fat biopsies. We found that the method is reliable and easy to perform and the tissue sample needed is obtained by minor surgery.
In the aortic intima amyloid deposits are often associated with atherosclerosis plaques. In our study we also investigated the prevalence of intimal amyloid from 10 patients age 58-94, amyloid deposits were present in 50% of the cases.
Amyloidos är ett sjukdomstillstånd där proteiner som normalt är lösliga i kroppen felveckas och formar långa olösliga fibriller som ansamlas i vävnader och organ såsom t.ex. hjärta, hjärna och lever. Det finns cirka 25 proteiner som kan ge upphov till amyloidos. Man kan skilja på två huvudgrupper av amyloidos, systemisk och lokaliserad. Vid lokal amyloidos kan inlagringar förekomma i specifika vävnader vid framför allt vissa åldersberoende sjukdomar som t.ex. Alzheimers sjukdom. Vid systemisk amyloidos förekommer inlagringar i praktiskt taget alla vävnader. Symtomatologin vid systemisk amyloidos är variabel och sjukdomsbilden kan vara svårtolkad men tidig och specifik diagnostik ger möjlighet till riktad terapi mot den bakomliggande sjukdomen. Syftet med denna studie var att utvärdera en Western blot metod som använts för typning av vanligaste formerna av systemisk amyloidos. De slutsatser som nåtts är att denna metod är snabbt, pålitligt och enkel att utföra. Diagnos erhölls med finnålsbiopsi av bukfettvävnad som är enkel, snabb och billig metod med liten risk för patienternas hälsa. Vi lyckades också med hjälp av immunhistokemisk infärgning titta på prevalens av amyloid i aortas intima.
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Lam, Mary. "Benchmark of Probabilistic Methods for Fault Diagnosis." Thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-106235.
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To be able to do the correct action when a fault is detected, the fault isolation part must be precise and run in real time during operation of the process. In many cases can it be difficult to decide exactly where the fault is localized. In those cases, the isolation algorithm must rank the faults according to their probability to be the cause to the behavior. The masters thesis project aims at probabilistic methods and algorithms for fault isolation in embedded systems. Different kind of Bayesian Networks have been compared in this report and the comparison has been done on a literature defined “benchmark system”. Those Bayesian network models which have been implemented for fault isolation are: 1. Manually (on the basis of physical representations) 2. Two-layer structure continuous signals discreet signals 3. Via temporal causal graph (dynamical network) The algorithms should be compared in the following areas: computational complexity, isolation performance and degree of difficulty to construct the network on the basis of data. The evaluated algorithms showed good results. Even though the system data which have been used in the Bayesian Networks are not very accurate in the first place, it manage to give a fairly precise isolation of the faults. The continuous Bayesian Network manage to show a good isolation performance for different type of faults and the Dynamic Bayesian Network found most of the faults even for a rather complex network.Detta examensarbete handlar om sannolikhetsbaserade metoder för felisolering. När ett fel uppstår ombord på en Scania lastbil kan man upptäcka det. I bästa fall kan en viss komponent pekas ut som orsak, men ofta kommer man att ha ett antal komponenter som kan vara orsaken. I många fall är det dock svårt att hitta var exakta felet finns. För att hantera dessa situationer vill man använda metoder för att beräkna sannolikheten att olika komponenter är trasiga. För att beräkna sannolikheten kan man använda en probabilistisk model, dvs. Bayesianska nätverk. I detta arbete har olika metoder för att skapa Bayesianska nätverk jämförts. Jämförelsen görs på ett litteratur väl definierat benchmark problem: diagnosar en två-tank system. De typer av Bayesiansk nätverks modeller som har implementerats för felisolering är: 1. Manuellt (ut ifrån fysikalisk modell) 2. Två-lagers struktur kontinuerliga signaler diskreta signaler 3. Via Bindningsgrafer (dynamiskt nätverk) Problemen som undersöktes var bland annat svårighet att bygga nätverket utifrån data, beräkningskomplexitet samt isolerings prestanda. En jämförelse mellan de Bayesianska metoderna för felisolering och samt dem befintliga standardmetoder har även gjorts. De undersökta algoritmerna visade goda resultat. Trots bristen på data, visade algoritmerna lovande resultat. Det Två-lagers Bayesianska nätverket visade en bra isoleringsprestanda på olika komponent fel och det Dynamiska Bayesianska nätverket upptäckte de flesta fel trots att det var ett ganska complext nätverk.
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Zhong, Jian Hua. "Intelligent system based facility monitoring and fault diagnosis of power generators." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2550655.
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Dassanayake, Hemendra Parakrama Bandara. "Fault diagnosis for a new generation of intelligent train door systems." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.633083.
Повний текст джерелаАнотація:
This thesis presents the fault diagnosis of an electric train door system. To operate an efficient rail system it imperative that equipment such as train doors are maintained thoroughly, preferably using a predictive maintenance scheme. Initially, a life size testrig was constructed and information regarding frequent door faults collected from both the maintainers and manufacturers. Thirteen faults were induced on the test-rig and a comprehensive dataset collected. It was found that the main incipient faults are related to changes in the friction. By examining the steady state operating conditions it was found that the friction could be adequately described by first order characteristics. The dynamics of the electro-mechanical system can be described by a basic second order differential equation. The most logical approach to diagnose physical faults is to estimate the physical parameters of the system. For this purpose, various continuous-time model identification techniques are discussed. State variable-pre-filtering and linear integral pre-filtering can be used to solve the problem of noise-accentuated derivatives; these filters are also convenient for on-line implementation. However, linear integral filtering is much more straightforward in design and computationally faster for lower order systems. The prefilters are carefully designed to accommodate fast system responses under fault conditions; however, adaptive type pre-filtering provides an alternative solution. A neuro-fuzzy svstem that processes the parameter estimates is proposed to isolate faults', such a system combines the strengths of neural networks and fuzzy inference systems. Initially, a clustering algorithm is used to identify a fuzzy model, which is implemented in a neural architecture for training. Prior to training, redundant membership functions are removed, increasing the readability of the inference system. The neuro-fuzzy system has high classification success and a transparent rule-base that can be used by maintainers. Fault identification is achieved through direct implication of the parameter estimates. To make practical implementation feasible, it is proposed that a three-layer distributed hierarchical architecture be used. This incorporates the manufacturer's control alarms, dedicated fault diagnosis algorithms and the train management system for data communication; an interface to the maintenance system is also available. The architecture is generic and could be used for fault diagnosis of multiple low-cost assets.
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Gemmell, Brian David. "A consultative expert system for intelligent diagnosis on steam turbine plant." Thesis, University of Strathclyde, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340915.
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Zhang, Zhenyou. "Data Mining Approaches for Intelligent Condition-based Maintenance : A Framework of Intelligent Fault Diagnosis and Prognosis System (IFDPS)." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for produksjons- og kvalitetsteknikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25148.
Повний текст джерелаАнотація:
Condition-based Maintenance (CBM) is a maintenance policy that take maintenance action just when need arises with real-time condition monitoring. Intelligent CBM means a CBM system is capable of understanding and making maintenance decisions without human intervention. To achieve this objective, it is needed to detect current conditions of mechanical and electrical systems and predict the fault of the systems accurately. What’s more, the maintenance scheduling need to be optimized to reduce the maintenance cost and improve the reliability, availability and safety based on the results of fault detection and prediction. Data mining is a computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The goal of the data mining is to extract useful information from a data set and transform it into an understandable structure for further use. This thesis develops framework of Intelligent Fault Diagnosis and Prognosis System (IFDPS) for CBM based on Data Mining Techniques. It mainly includes two tasks: the one is to detect and predict the condition of the equipment and the other is to optimize maintenance scheduling accordingly. It contains several phases: sensor selection and its placement optimization, signal processing and feature extraction, fault diagnosis, fault prognosis and predictive maintenance scheduling optimization based on results of fault diagnosis and prognosis. This thesis applies different data mining techniques containing Artificial Neural Network such as Supervised Back-Propagation (SBP) and Self-Organizing Map (SOM), Swarm Intelligence such as Particle Swarm Optimization (PSO), Bee Colony Algorithm (BCA) and Ant Colony Optimization (ACO), and Association Rule (AI) in most of these phases. The outcomes of the thesis can be applied in mechanical and electrical system in industries of manufacturing, wind and hydro power plants.
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Xia, Qijun. "Hybrid reasoning methods for intelligent operation support systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0017/NQ46948.pdf.
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Lannerhed, Petter. "Structural Diagnosis Implementation of Dymola Models using Matlab Fault Diagnosis Toolbox." Thesis, Linköpings universitet, Fordonssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138753.
Повний текст джерелаАнотація:
Models are of great interest in many fields of engineering as they enable prediction of a systems behaviour, given an initial mode of the system. However, in the field of model-based diagnosis the models are used in a reverse manner, as they are combined with the observations of the systems behaviour in order to estimate the system mode. This thesis describes computation of diagnostic systems based on models implemented in Dymola. Dymola is a program that uses the language Modelica. The Dymola models are translated to Matlab, where an application called Fault Diagnosis Toolbox, FDT is applied. The FDT has functionality for pinpointing minimal overdetermined sets of equations, MSOs, which is developed further in this thesis. It is shown that the implemented algorithm has exponential time complexity with regards to what level the system is overdetermined,also known as the degree of redundancy. The MSOs are used to generate residuals, which are functions that are equal to zero given that the system is fault-free. Residual generation in Dymola is added to the original methods of the FDT andthe results of the Dymola methods are compared to the original FDT methods, when given identical data. Based on these tests it is concluded that adding the Dymola methods to the FDT results in higher accuracy, as well as a new way tocompute optimal observer gain. The FDT methods are applied to 2 models, one model is based on a system ofJAS 39 Gripen; SECS, which stands for Secondary Enviromental Control System. Also, applications are made on a simpler model; a Two Tank System. It is validated that the computational properties of the developed methods in Dymolaand Matlab differs and that it therefore exists benefits of adding the Dymola implementations to the current FDT methods. Furthermore, the investigation of the potential isolability based on the current setup of sensors in SECS shows that full isolability is achievable by adding 2 mass flow sensors, and that the isolability is not limited by causality constraints. One of the found MSOs is solvable in Dymola when given data from a fault-free simulation. However, if the simulation is not fault-free, the same MSO results in a singular equation system. By utilizing MSOs that had no reaction to any modelled faults, certain non-monitored faults is isolated from the monitored ones and therefore the risk of false alarms is reduced. Some residuals are generated as observers, and a new method for constructing observers is found during the thesis by using Lannerheds theorem in combination with Pontryagin’s Minimum Priniple. This method enables evaluation of observer based residuals in Dymola without any selection of a specific operating point, as well as evaluation of observers based on high-index Differential Algebraic Equations, DAEs. The method also results in completely different behaviourof the estimation error compared to the method that is already implemented inthe FDT. For example, one of the new observer-implementations achieves both an estimation error that converges faster towards zero when no faults are implementedin the monitored system, and a sharper reaction to implemented faults.
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Reid, C. "Spectroscopic methods for medical diagnosis at terahertz wavelengths." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/17571/.
Повний текст джерелаАнотація:
Terahertz (THz) radiation lies between the microwave and infrared regions of the electromagnetic spectrum. THz radiation excites intermolecular interactions and is non-ionising making it a viable tool for medical imaging. This thesis describes the development and validation of spectroscopic methods for diagnosis of tissue pathologies at THz wavelengths. Theoretical techniques were developed to determine the origin of the contrast seen in THz images of biological tissue. Specific biological tissues investigated in this thesis were colonic tissues with the aim of determining the origin of contrast between healthy and diseased tissue in THz images. This thesis investigates the interaction of THz radiation with matter using simple tissue phantoms made from five biologically relevant materials: water, methanol, lipid, sucrose and gelatin. Phantoms are designed to imitate the spectroscopic properties of tissue at specific wavelengths where physical properties of the phantom, such as concentration and homogeneity, can be accurately controlled. The frequency-dependent absorption coefficients, refractive indices and Debye relaxation times of the pure compounds were measured and used as prior knowledge in the different theoretical methods for the determination of concentration. Three concentration analysis methods were investigated, a) linear spectral decomposition, b) spectrally averaged dielectric coefficient method and c) the Debye relaxation coefficient method. These methods were validated on phantoms by determining the concentrations of the phantom chromophores and comparing to the known composition. Two-component phantoms were made comprising water with methanol, lipid, sucrose or gelatin. Two different three-component phantoms were created; one with water, methanol and sucrose and a second with water, gelatin and lipid. The accuracy and resolution of each method was determined to assess the potential of each method as a tool for medical diagnosis at THz wavelengths. Finally, the spectroscopic methods were applied to measurements of ex-vivo colon tissues containing cancerous and dysplastic regions. Statistical analysis of the reflected time-domain waveforms demonstrated good distinction between healthy and diseased tissues with an estimated sensitivity of 89.2% and specificity of 78.3%.
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Mahmoud, Houssam. "Diagnosis of Pneumatic Cylinders Using Acoustic Emission Methods." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-399214.
Повний текст джерелаАнотація:
Tato práce se zabývá vývojem nového efektivního diagnostického postupu pro kontrolu funkce pneumatických válců pomocí metody akustické emise. Cílem práce bylo navrhnout a určit diagnostická kritéria pro hodnocení kvality vybraných typů pneumatických válců. Prvním krokem bylo nalezení typického akustického emisního signálu, který je spojen s určitým typem poškození ve válci využitím frekvenčního spektra signálu. Později byl tento parametr nahrazen parametrem RMS během sledování změn v průběhu testů. Na konkrétních válcích byl sledován vztah mezi akustickou emisí a různými typy defektů a byl představen nový přístup k určování těchto typů vad a jejich odlišením v signálu akustické emise. Druhý krok studie že neporušené a poškozené válce byly porovnávány tak, aby se zjistily výrazné rozdíly, které určují, zda je válec poškozen nebo nepoškozen. Několik nepoškozených válců bylo testováno akustickou emisí a následně v nich byly vytvořeny umělé vady. Signály z vysouvání a zasouvání pístu byly zaznamenány a analyzovány pomocí řady parametrů. Na základě časového zpoždění a normalizace RMS byly rozpoznány odezvy mezi poškozenými a nepoškozenými pneumatickými válci. Rozdíly byly zjištěny porovnáním maximální hodnoty RMS ze snímače upevněného na předním víku válce a snímače upevněného na zadním víku válce pro jeden cyklus. Poškozené a nepoškozené válce byly rozlišeny pomocí rozdílů energetických hodnot přítomných v signálech z obou snímačů v závislosti na zdvihu pístu. Konečné vyhodnocení válce bylo určeno výpočtem celkové hodnoty RMS. Ve třetím kroku experimentu byly válce postupně zatěžovány různými závažími ve svislém směru. Signály akustické emise byly zaznamenány z vysouvání a zasouvání pístu a poté analyzovány. Časové zpoždění se vypočítává z digitálního vstupu a začátku pohybu pístu. Energie signálu a RMS akustické emise porovnávají různé reakce v poškozených a nepoškozených pneumatických válcích s a bez zatížení. Výsledky testu ukázaly lineární vztah mezi křivkou RMS a zatížením. Defekty ovlivňují vztah mezi aplikovaným zatížením a zaznamenaným signálem snímačů.
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Vileiniskis, Marius. "Fault detection and diagnosis methods for engineering systems." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/30402/.
Повний текст джерелаАнотація:
The main aim of this thesis is to investigate available techniques and develop a methodology for the fault detection and diagnostics for two engineering systems, namely railway point systems (RPS) and three-phase separators (TPS). The fault detection of the RPS was performed on the measured current from the motor of point operating equipment (POE). The method of One Class Support Vector Machines has been chosen as the fault detection model. Elastic similarity measures, such as edit distance with real penalties and dynamic time warping, were chosen to compare the data of POE operations. A combination of Euclidean distance and elastic similarity measures has been proposed in order to take into account the absolute values and shape properties of the two compared time series. The proposed methodology has been tested on the in-field RPS data. The results indicated that the fault detection model was able to detect abnormal values and/or shape of the time series of measured current. However, not in all cases these changes could be related to a recorded failure of RPS in the database. The fault detection of TPS was performed given the sensor readings of flow and level transmitters of TPS. The method of Bayesian Belief Networks (BBN) has been proposed to overcome the late detection of faults with the threshold based alarm technique. An approach to observe sensor readings of TPS in several adjacent time intervals and to update the prior probabilities in the BBN after inserting the sensor readings as evidence was proposed. The proposed methodology has been tested on the data obtained from a TPS simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the TPS.
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Sekar, Booma Devi. "Hybrid intelligent technology based fault diagnosis system for squirrel cage induction motor." Thesis, University of Macau, 2007. http://umaclib3.umac.mo/record=b1678023.
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Deng, Hua. "Hybrid spectral/intelligent modeling, control and diagnosis for nonlinear distributed curing processes /." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-meem-b19887334a.pdf.
Повний текст джерелаАнотація:
Thesis (Ph. D.)--City University of Hong Kong, 2005."Submitted to Department of Manufacturing Engineering and Engineering Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 169-181).
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Sharif, Mohamed Abdulla Mohamed. "Application of intelligent instrumentation in process plant condition monitoring and fault diagnosis." Thesis, Cardiff University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340896.
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Khan, Atif A. "Intelligent data analysis for pattern recognition and medical diagnosis of ageing spine." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/66677/.
Повний текст джерелаАнотація:
Every year, the healthcare industry collects a huge volume of data that is not mined properly and not put to optimal use. Discovery of the hidden patterns and relationships in data often goes unexploited. Data mining in the medical domain is more rigorous and complex to handle as most available raw medical data are voluminous and heterogeneous in nature. This research mines medical data related to human spine by learning patterns through the collected data and develops medical decision support systems based on intelligent system techniques. This study will help medical specialists in clinical decision making and disease diagnosis related to the spine. The human spine is a multifunctional complicated structure of bones, joints, ligaments and muscles which all undergo change as we age. For most people, these changes occur in a gradual and painless manner. However, a sudden change caused naturally or through injury, can lead to serious medical conditions, which usually result in back pain. Due to the wide diversity of spine functions, any disorder in the spine triggers various severe problems, which negatively affect quality of life and place huge financial and health burdens on the society. While ageing is inevitable, the rate at which the spine shows the effects of ageing is of clinical significance. This research reveals the growth and degenerative pattern of the human spine using intelligent system techniques. The information extracted from lumbar spine MRIs is used to classify age related changes. In this research, principal component analysis was used to detect anomalies in data and to transform the complex multivariate feature space to a smaller meaningful representation. PCA transformation reduced the complexity and dimension of the data, hence permitting a 2D visualization and knowledge of spine growth and degeneration with age. Factor analysis (FA) was used to understand the significance and correlation of spinal features with the normal ageing. Spines were ranked on the basis of their features and clusters were made to group similar samples. Studying the characteristics of the clusters helped in developing an understanding of the variations in spinal features among different age groups. An artificial neural network (ANN) was used in the estimation of age from the extracted lumbar spine features. ANNs have several benefits, including their ability to process complex data, reduced likelihood of overlooking relevant information, and a reduction in the cost and diagnosis time. The ANN model worked well for the spinal age estimation but due to its black box nature, it failed to provide valuable information about the correlations among the spinal features. A hybrid intelligent model consisting of a fuzzy inference system (FIS) and ANN, called an adaptive neuro-fuzzy inference system (ANFIS) was used to extract meaningful information from the data set in terms of fuzzy rules. Self-organizing maps (SOM) were used to visualize variations in lumbar spine features with the natural ageing. Useful information was acquired through SOM exploratory data analysis. Ward and modified Ward clustering methods were employed on SOM to group similar samples and study the characteristics of the clusters. The results from this research are helpful in setting the standards for spinal growth and degeneration with age and for understanding of the spinal disease prevalence. This research will help spine specialists in diagnosing disease from scans. It can be considered as a stepping-stone towards developing a tool for the classification of normal and problematic spines.
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Poon, Wing-shan Rosana, and 潘穎珊. "Evaluation of methods of susceptibility testing for laribacter hongkongensis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B45010389.
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Md, Kamal Mahanijah. "Intelligent fault detection and isolation for proton exchange membrane fuel cell systems." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4574/.
Повний текст джерелаАнотація:
This work presents a new approach for detecting and isolating faults in nonlinear processes using independent neural network models. In this approach, an independent neural network is used to model the proton exchange membrane fuel cell nonlinear systems using a multi-input multi-output structure. This research proposed the use of radial basis function network and multilayer perceptron network to perform fault detection. After training, the neural network models can give accurate prediction of the system outputs, based on the system inputs. Using the residual generation concept developed in the model-based diagnosis, the difference between the actual and estimated outputs are used as residuals to detect faults. When the magnitude of these residuals exceed a predefined threshold, it is likely that the system is faulty. In order to isolate faults in the system, a second neural network is used to examine features in the residual. A specific feature would correspond to a specific fault. Based on features extracted and classification principles, the second neural network can isolate faults reliably and correctly. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations.
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Hunt, James Barrie. "Endomyocardial biopsy diagnosis of acute cardiac allograft rejection." Master's thesis, University of Cape Town, 1991. http://hdl.handle.net/11427/25718.
Повний текст джерелаАнотація:
The aims of the present investigation are fourfold: (i) to review the range of non-invasive methods that may be used to diagnose acute cardiac allograft rejection; (ii) to review the use of the bioptome in sampling the donor heart endomyocardium; (iii) to review the light microscopic and histological grading of acute cardiac rejection; (iv) to characterise the mononuclear populations in endomyocardial biopsy samples and correlate the findings with the light microscopic appearances of the same biopsy specimens.