Obiekt
Tytuł: Artificial intelligence approaches to fault diagnosis for dynamic systems
Współtwórca:
Korbicz, Józef (1951- ) - red. ; Patton, Ronald J. - red.
Podtytuł:
Tytuł publikacji grupowej:
Abstract:
Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. ; When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. ; For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Identyfikator zasobu:
Strony:
Źródło:
AMCS, volume 9, number 3 (1999) ; kliknij tutaj, żeby przejść
Jezyk:
Prawa do dysponowania publikacją:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Repozytorium > Jednostki organizacyjne > Wydział Informatyki, Elektrotechniki i Automatyki
- Repozytorium > Typy utworów > Artykuły
- Repozytorium > Czasopisma naukowe i serie wydawnicze UZ > International Journal of Applied Mathematics and Computer Science (AMCS)
- Repozytorium > Czasopisma naukowe i serie wydawnicze UZ > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (1999)
Data ostatniej modyfikacji:
2024-11-05
Data dodania obiektu:
2021-01-22
Liczba wyświetleń treści obiektu:
215
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/repozytorium/publication/64800
Wyświetl opis w formacie RDF:
Wyświetl opis w formacie OAI-PMH:
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International Journal of Applied Mathematics and Computer Science (AMCS)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 26 (2016)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 25 (2015)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 24 (2014)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 23 (2013)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 22 (2012)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 21 (2011)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 20 (2010)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 19 (2009)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 18 (2008)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 17 (2007)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 16 (2006)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 15 (2005)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 13 (2003)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 12 (2002)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 10 (2000)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 1 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 2 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 3 (1999)
- Artificial intelligence approaches to fault diagnosis for dynamic systems
- Dynamic neural networks for process modelling in fault detection and isolation systems
- Development of dynamic neural networks with application to observer-based fault detection and isolation
- CMAC and its extensions for efficient system modelling
- Neural network evaluation of model-based residuals in fault detection of time delay systems
- Application of sensitivity theory to fuzzy logic based FDI
- Fuzzy-logic fault isolation in large-scale systems
- Fuzzy-logic fault diagnosis of industrial process actuators
- An expert system coupled with a hierarchical structure of fuzzy neural networks for fault diagnosis
- Evolutionary multi-objective Pareto optimisation of diagnostic state observers
- Parameter estimation based fault detection and isolation in Wiener and Hammerstein systems
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 4 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 1 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 8 (1998)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 7 (1997)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 6 (1996)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 5 (1995)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 4 (1994)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 3 (1993)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 2 (1992)
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International Journal of Applied Mathematics and Computer Science (AMCS),volume 1 (1991)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 26 (2016)
| Nazwa wydania | Data |
|---|---|
| Artificial intelligence approaches to fault diagnosis for dynamic systems | 2024-11-05 |
