Готові списки джерел за темами / Intelligent methods of diagnosis

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Добірка наукової літератури з теми "Intelligent methods of diagnosis"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Intelligent methods of diagnosis"

1

Wang, Huaqing, Peng Chen, and Shuming Wang. "Intelligent diagnosis methods for plant machinery." Frontiers of Mechanical Engineering in China 5, no. 1 (November 25, 2009): 118–24. http://dx.doi.org/10.1007/s11465-009-0084-z.

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2

Shi, Rong Bo, Zhi Ping Guo, and Zhi Yong Song. "Research Based on State Monitoring of CNC Machine Tools Intelligent Security System." Applied Mechanics and Materials 427-429 (September 2013): 1328–32. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1328.

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Анотація:
Analyse the cause of fault in CNC Machine, research the corresponding solve scheme, and to realize the state can monitor equipment operation, improve equipment reliability, the development set of machine condition monitoring, fault warning, fault diagnosis and troubleshooting as one of the intelligent security system. Based on the CNC machine intelligence support system research, design, introduces the key technologies and methods. Screw lift state of motion monitoring, for example, trend analysis exercise state, intelligent fault diagnosis, in order to achieve protection of the intelligent CNC machine tools to verify the practicality of intelligent security systems.
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3

Hu, Hao, and Ying Min Yan. "Fault Diagnosis Technology of Equipment System." Applied Mechanics and Materials 380-384 (August 2013): 1003–8. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1003.

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Анотація:
With the development of computer technology, the development of artificial intelligence technology, diagnostic techniques to change rapidly in the intelligent stage of development, this paper will mainly based on artificial intelligent fault diagnosis methods are described, mainly the application of BP network in fault diagnosis. And application principle to design a user-friendly display system. Make diagnosis data clearly show on the panel, and at the same time show the fault type and other necessary data. Then the bus data tracking, were analyzed. The system for a new system has complex lines, the number of components and types of features, can quickly identify the fault location, allowing the system to normal operation.
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4

Huang, Xiaoge, Yiyi Zhang, Jiefeng Liu, Hanbo Zheng, and Ke Wang. "A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier." Polymers 10, no. 10 (October 3, 2018): 1096. http://dx.doi.org/10.3390/polym10101096.

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Анотація:
Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.
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5

Takács, Orsolya, and Annamária R. Várkonyi-Kóczy. "Anytime Soft Computing Methods for Intelligent Measurement, Diagnosis and Control." IFAC Proceedings Volumes 33, no. 28 (October 2000): 159–64. http://dx.doi.org/10.1016/s1474-6670(17)36827-1.

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6

Glumov, V. M., V. Yu Rutkovskii, and V. M. Sukhanov. "Methods of intelligent diagnosis for control of flexible moving craft." Automation and Remote Control 67, no. 12 (December 2006): 1863–77. http://dx.doi.org/10.1134/s0005117906120010.

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7

Tang, Shengnan, Shouqi Yuan, and Yong Zhu. "Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery." IEEE Access 8 (2020): 9335–46. http://dx.doi.org/10.1109/access.2019.2963092.

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8

Huang, Yo-Ping, Chao-Ying Huang, and Shen-Ing Liu. "Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis." Applied Soft Computing 14 (January 2014): 38–46. http://dx.doi.org/10.1016/j.asoc.2013.09.021.

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9

Chen, Zhan Peng, Zhuo Wang, Li Min Jia, and Guo Qiang Cai. "Analysis and Comparison of Locomotive Traction Motor Intelligent Fault Diagnosis Methods." Applied Mechanics and Materials 97-98 (September 2011): 994–1002. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.994.

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Анотація:
Train operation safety is the most important and the most basic requirement. Locomotive traction motor is the train operation of traction power equipment, whose reliability relates directly to the train operation safety. And locomotive traction motor fault diagnosis is to ensure the reliability of the traction motor scooter important technique means. Through the locomotive pulling motor failure diagnosis method's research, the traction motor typical fault type has been summarized, the main intelligent diagnosis method principle has been narrated, the main principles of the intelligent diagnosis, diagnostic procedures, and their advantages and disadvantages are described in detail, the existing problems in the field and future trends are pointed out finally.
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10

Bello, Opeyemi, Javier Holzmann, Tanveer Yaqoob, and Catalin Teodoriu. "Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art." Journal of Artificial Intelligence and Soft Computing Research 5, no. 2 (April 1, 2015): 121–39. http://dx.doi.org/10.1515/jaiscr-2015-0024.

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Анотація:
AbstractArtificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.
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Більше джерел

Дисертації з теми "Intelligent methods of diagnosis"

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.

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Анотація:
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.
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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.

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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.

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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.

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Анотація:
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
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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.

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Анотація:
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.
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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.

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Анотація:
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
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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.

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Анотація:
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 modelling
Tiivistelmä 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
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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/.

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Анотація:
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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9

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.

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Анотація:
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.
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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.

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Анотація:
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.
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Книги з теми "Intelligent methods of diagnosis"

1

Wyllis, Bandler, ed. Tracing chains-of-thought: Fuzzy methods in cognitive diagnosis. Heidelberg, [Germany]: Physica-Verlag, 1996.

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Aldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. London: Springer London, 2013.

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Raza, Khalid, ed. Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8534-0.

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Hatzilygeroudis, Ioannis, Vasile Palade, and Jim Prentzas, eds. Advances in Combining Intelligent Methods. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46200-4.

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Yager, Ronald R., Marek Z. Reformat, and Naif Alajlan, eds. Intelligent Methods for Cyber Warfare. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08624-8.

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6

Segovia, Javier, Piotr S. Szczepaniak, and Marian Niedzwiedzinski, eds. E-Commerce and Intelligent Methods. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1779-9.

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7

Hatzilygeroudis, Ioannis, and Jim Prentzas, eds. Combinations of Intelligent Methods and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19618-8.

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Hatzilygeroudis, Ioannis, Isidoros Perikos, and Foteini Grivokostopoulou, eds. Advances in Integrations of Intelligent Methods. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1918-5.

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Hatzilygeroudis, Ioannis, and Vasile Palade, eds. Advances in Hybridization of Intelligent Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-66790-4.

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Hatzilygeroudis, Ioannis, and Vasile Palade, eds. Combinations of Intelligent Methods and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36651-2.

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Частини книг з теми "Intelligent methods of diagnosis"

1

Rigatos, Gerasimos G. "Machine Learning Methods for Industrial Systems Fault Diagnosis." In Modelling and Control for Intelligent Industrial Systems, 313–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17875-7_15.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Simulation Platform for Fault Diagnosis." In Intelligent Control and Learning Systems, 45–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_4.

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AbstractThe previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well as the monitoring processes when used for fault diagnosis. In order to validate the effectiveness of data-driven multivariate statistical analysis methods in the field of fault diagnosis, it is necessary to conduct the corresponding fault monitoring experiments. Therefore this chapter introduces two kinds of simulation platform, Tennessee Eastman (TE) process simulation system and fed-batch Penicillin Fermentation Process simulation system. They are widely used as test platforms for the process monitoring, fault classification, and identification of industrial process. The related experiments based on PCA, CCA, PLS, and FDA are completed on the TE simulation platforms.
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Pavithra, D., A. N. Jayanthi, and R. Nidhya. "Comparison of Machine Learning Methods for Effective Autism Diagnosis." In Advances in Intelligent Systems and Computing, 629–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2475-2_58.

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Kamdar, J. H., J. Jeba Praba, and John J. Georrge. "Artificial Intelligence in Medical Diagnosis: Methods, Algorithms and Applications." In Learning and Analytics in Intelligent Systems, 27–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40850-3_2.

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Orczyk, Tomasz, and Piotr Porwik. "Liver Fibrosis Diagnosis Support System Using Machine Learning Methods." In Advances in Intelligent Systems and Computing, 111–21. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2650-5_8.

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Belaala, Abir, Labib Sadek Terrissa, Noureddine Zerhouni, and Christine Devalland. "Spitzoid Lesions Diagnosis Based on SMOTE-GA and Stacking Methods." In Advances in Intelligent Systems and Computing, 348–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36664-3_39.

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7

Deng, Zhuofu, Yen-wei Chen, Zhiliang Zhu, Yinuo Li, He Li, and Yi Wang. "Advanced Transmission Methods Applied in Remote Consultation and Diagnosis Platform." In Intelligent Interactive Multimedia Systems and Services, 230–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92231-7_24.

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Bai, Bing, Feng Wang, and Xiuyuan Peng. "Fault Diagnosis Method of Agricultural Intelligent Equipment." In Lecture Notes in Electrical Engineering, 1915–23. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_224.

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Melekoğlu, Engin, Ümit Kocabıçak, Muhammed Kürşad Uçar, Mehmet Recep Bozkurt, and Cahit Bilgin. "Machine Learning for the Diagnosis of Chronic Obstructive Pulmonary Disease and Photoplethysmography Signal – Based Minimum Diagnosis Time Detection." In Trends in Data Engineering Methods for Intelligent Systems, 42–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79357-9_6.

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Basarslan, M. Sinan, and F. Kayaalp. "Classification Performance Evaluation on Diagnosis of Breast Cancer." In Trends in Data Engineering Methods for Intelligent Systems, 237–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79357-9_24.

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Тези доповідей конференцій з теми "Intelligent methods of diagnosis"

1

Odoom, E. Ricky. "Operational Reliability Improvement of Plants Through Intelligent Fault Detection and Diagnosis." In International Joint Power Generation Conference collocated with TurboExpo 2003. ASMEDC, 2003. http://dx.doi.org/10.1115/ijpgc2003-40052.

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Real-time Fault Detection and Diagnosis of modern dynamic process plants are continuously receiving increasing attention both theoretically and practically. In recent years, attempts have been made to apply Artificial Intelligence techniques to the Fault Detection Diagnosis task for improving the operational reliability of complex dynamic plants. The aim of this paper is to discuss the basic concepts, issues and tools of some of the emerging intelligence technologies for Fault Detection and Diagnosis schemes. The emphasis is given to the methods, which are based on Artificial Intelligent systems and which are appropriate for diagnosing faults in complex dynamic plants.
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Yann-Chang Huang, Chao-Ming Huang, Huo-Ching Sun, and Yi-Shi Liao. "Fault diagnosis using hybrid artificial intelligent methods." In 2010 5th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2010. http://dx.doi.org/10.1109/iciea.2010.5514760.

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Fan, Jianchun, Xuehong Zhao, and Laibin Zhan. "A Web-Based Intelligent System for Used-Oil Analysis." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-64047.

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The analysis of oil in an operating machine is considered as a very useful means to assess the condition of the machine. However, classical techniques of oil analysis are strongly dependent on the analyst’s expertise to perform wear particle inspection, condition classification, colligation of the test results by ferrography, AES. and physical or chemical detection and interpretation of the possible existing faults in a machine. To solve these problems and realize the intelligence of oil analysis, a Web-based intelligence system for oil analysis has been devised. This system is composed of an automatic ferroscope controlled by a computer to obtain improved wear debris images, a platform to process the images and to connect the field analyst with the experts in machine diagnosis through internet and an intelligent software platform to evaluate the tribological conditions and diagnose the faults. Furthermore, some intelligent diagnosis methods used in the system are introduced.
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4

Ke, Zhiwu, Xu Hu, Dawei Teng, and Mo Tao. "Intelligent Fault Diagnosis Method Based on Operating Parameters in Nuclear Power Plant." In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-66494.

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The safety of mechanical equipment is more important, it directly determines the safety of nuclear power plant operation, and even nuclear safety. So it is necessary to monitor the operating state of NPP system and mechanical equipment in real time by inspecting operating parameters. However, the key technology is real-time fault diagnosis of the mechanical equipment in NPP. Traditional fault diagnosis method based on analytic model is difficult to diagnose relevant and superimposed fault because of model error, disturbance and noise. This paper studies the application of fault diagnosis method based on BP neural network in NPP, and proposes an improved method for neural BP network method. For the feed-water system in the variable load operation process, we select the normal operation, the single feed-water valve fault, feed-water pump and feed-water valve superimposed fault as the analysis objects. One hundred points of data are extracted as BP algorithm training elements in these three processes averagely. The normal and abnormal conditions (including single fault and superimposed fault) can be accurately judged, but the single fault and superimposed failure would produce miscarriage of justice, about 2.4% of the single fault is diagnosed as superimposed fault, the diagnosis time delay is less than 1 second. These results meet the accuracy and real-time requirements. Then we study the application of support vector machine (SVM), which can make up for the deficiency of BP neural network. The results of this paper are useful for the real-time and reliable fault diagnosis of NPP.
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Lei, Yaguo, Hongkai Shan, Feng Jia, and Jing Lin. "Reconstruction independent component analysis-based methods for intelligent fault diagnosis." In 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2016. http://dx.doi.org/10.1109/cscwd.2016.7565996.

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6

Veropoulos, K., C. Campbell, and G. Learmonth. "Image processing and neural computing used in the diagnosis of tuberculosis." In IEE Colloquium Intelligent Methods in Healthcare and Medical Applications. IEE, 1998. http://dx.doi.org/10.1049/ic:19981039.

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7

Tayyab, Syed Muhammad, Paolo Pennacchi, Steven Chatterton, and Eram Asghar. "Intelligent Defect Diagnosis of Spiral Bevel Gears Under Different Operating Conditions Using ANN and KNN Classifiers." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70016.

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Abstract Spiral bevel gears are important part of many mechanical transmission systems and are known for their smooth operation and strong load carrying capacity. This type of gear has a high contact ratio, which makes it very difficult to diagnose even serious defects. Therefore, spiral bevel gears have rarely been used as a reference for defect diagnosis techniques. To overcome these challenges, artificial intelligence (AI) techniques are used in this research to diagnose defects in spiral bevel gears. Although Al techniques in the field of fault diagnosis have been very successful, however, these methods largely use the assumption that the training and test data come from the same operating conditions. However, when the operating conditions in which the trained model is deployed for predictions, differ from the operating conditions in which the model was trained, the performance of these approaches might be significantly reduced. Outside the laboratory, in real-world applications, operating conditions significantly vary, and it is difficult to obtain data for all potential operating conditions. To overcome this limitation and to make AI techniques suitable for diagnosing spiral bevel gear faults under different operating conditions, an effort is made to find fault distinguishing features, which are lesser sensitive to operating conditions. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers for fault detection. Performance comparison between both classifiers is made to determine their individual capability and suitability for diagnosing defects of spiral bevel gears under different operating conditions.
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Tan, Daoliang, Ai He, Xiangxing Kong, and Xi Wang. "Integration of Unknown Input Observers and Classification for Turbofan Engine Diagnosis." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46429.

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A great deal of attention has been attracted in the analytical model-based engine diagnostics over the past years. Meanwhile, an increasing number of researchers and practitioners make an attempt to gain an intelligent diagnoser in a pattern recognition way. A question naturally emerges of how to combine the two techniques to improve the robustness of an on-board diagnostic system. In this context, this paper suggests an integrated approach that combines the unknown input observer (UIO) with the support vector machine (SVM) technique to aircraft engine fault diagnosis. Sensor faults and actuator faults are separately considered. To reduce the effect of engine disturbances on diagnostic performance, we first design a bank of UIOs, each of which is sensitive to all sensor and actuator faults but only one signal. Then, the magnitudes of a set of residuals between the UIO-based estimations and the engine measurements are fed into an SVM classifier to detect and isolate engine faults. Experimental results demonstrate an encouraging potential of the suggested method and that the UIO-oriented approach is superior or competitive to the Kalman-based algorithm.
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9

Singh, Amritpal, and P. Verma. "A review of intelligent diagnostic methods for condition assessment of insulation system in power transformers." In 2008 International Conference on Condition Monitoring and Diagnosis. IEEE, 2008. http://dx.doi.org/10.1109/cmd.2008.4580520.

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Datta, A., C. Mavroidis, and M. Hosek. "A Role of Unsupervised Clustering for Intelligent Fault Diagnosis." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43492.

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In the field of data analysis two terms frequently encountered are supervised and unsupervised methods of data classification and clustering methodologies. While supervised methods mostly deal with training classifiers for known symptoms, unsupervised clustering provides exploratory techniques for finding hidden patterns in the data. With huge volumes of data being generated from different systems everyday, what makes a system intelligent is its ability to analyze the data for efficient decision-making based on known or new cluster discovery. The three-fold contribution of this paper can be summarized as the role of unsupervised clustering for intelligent decision-making process, review of existing unsupervised models, including self-organizing maps (SOM), hierarchical tree (HT) model and quality adaptive threshold (QT) model, and lastly a new hybrid model for unsupervised clustering is proposed. For case study, we have taken the example of an intelligent decision making process in the field of fault diagnosis of industrial robots. The unsupervised models were tested on data obtained from an industrial robot used in the semiconductor industry. This paper presents the first set of results obtained from these four methods and discusses further applications of these methods.
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Звіти організацій з теми "Intelligent methods of diagnosis"

1

Martinak, R., Anthony E. Kelly, D. Sleeman, J. Moore, and R. D. Ward. Diagnosis and Remediation in the Context of Intelligent Tutoring Systems. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada199024.

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MOSKALENKO, O. L., S. YU TERESHCHENKO, and E. V. KASPAROV. INTERNET ADDICTION: DIAGNOSIS CRITERIA AND METHODS. Science and Innovation Center Publishing House, April 2022. http://dx.doi.org/10.12731/978-0-615-67340-0-2.

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This article presents a review of the literature and analyzes scientific studies on the prevalence of Internet addiction in different countries. The authors conducted a scientific search using the relevant keywords in the PubMed and Google Scholar search engines, in the Scopus, Web of Science, MedLine, The Cochrane Library, EMBASE, Global Health, CyberLeninka, RSCI and others databases.
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3

Izenson, M. G., P. H. Rothe, and G. B. Wallis. Diagnosis of condensation-induced waterhammer: Methods and background. Office of Scientific and Technical Information (OSTI), October 1988. http://dx.doi.org/10.2172/6752266.

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Rinchik, E. M. Workshop on molecular methods for genetic diagnosis. Final technical report. Office of Scientific and Technical Information (OSTI), July 1997. http://dx.doi.org/10.2172/501564.

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Masrur, M. A., ZhiHang Chen, and Yi L. Murphey. Intelligent Diagnosis of Open and Short Circuit Faults in Electric Drive Inverters For Real-Time Applications. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada513126.

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Jesneck, Jonathan, and Joseph Lo. Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada430017.

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Frank, Stephen M., Guanjing Lin, Xin Jin, Rupam Singla, Amanda Farthing, Liang Zhang, and Jessica Granderson. Metrics and Methods to Assess Building Fault Detection and Diagnosis Tools. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1503166.

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Giger, Maryellen L. Advanced Methods for the Computer-Aided Diagnosis of Lesions in Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, July 1998. http://dx.doi.org/10.21236/ada353241.

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Giger, Maryellen L. Advanced Methods for the Computer-Aided Diagnosis of Lesions in Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada390701.

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Flood, Ian, Bryan T. Bewick, and Emmart Rauch. Rapid Simulation of Blast Wave Propagation in Built Environments Using Coarse-Grain Based Intelligent Modeling Methods. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada543599.

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