Fuzzy modeling for industrial systems: Dissertation

Research output: ThesisDissertationCollection of Articles

1 Citation (Scopus)

Abstract

In developing knowledge-based fuzzy systems it is possible to utilize several different development processes. In this thesis the incremental development process, where systems are analyzed, designed, installed and tested in parts, is seen as the most suitable one. Building a knowledge-based system is a human-oriented interactive process, that is disturbed by biases, mistakes, misunderstandings, and different levels of expertise. Therefore the acquired information should be validated before using it in any installed expert (knowledge-based) system. In this thesis we present methods to validate acquired information by combining heuristic knowledge and measured knowledge (data). Fuzzy models are built using heuristic knowledge and data is used to finetune parameters of the models. The validity of acquired knowledge can be estimated with analyzing the tuning results. An essential point of this thesis is that fuzzy models, developed in the knowledge acquisition phase, can be utilized later during the development work. The main areas concerned are model-based control and diagnostic systems. By using experimental results we present methods to construct model-based control systems and fault diagnosis systems by using the incremental development process, fuzzy models, modular architecture, and advanced techniques. The results show that incremental development process can be applied also when model-based systems are built. In fault diagnosis, fuzzy modeling gives the possibilities to increase the level of adaptivity and robustness in the future, after reliable tuning and learning methods have fulfilled demands. In this thesis we present new methods in applying fuzzy logic and fuzzy model-based systems in developing fault diagnosis systems. More intelligent control systems are seen as one important factor in the future. When the complexity of machines and processes increases, manual operation of them becomes difficult, even impossible. This has to be taken into account in developing control systems to ensure the high availability of target systems. Our solution, presented in this research, is to use the information produced by a fault diagnosis system in adapting the control system. This makes the control system more robust, and increases the availability of a target system. The solution is based on using modular architecture, in which meta-rule modules are exploited to produce the adaptivity needed. The solution is formed so that fault diagnosis information can be installed later, after experiences of using the target system have been gained.
Original languageEnglish
QualificationDoctor Degree
Awarding Institution
  • University of Oulu
Place of PublicationEspoo
Publisher
Print ISBNs951-38-5367-5
Publication statusPublished - 1999
MoE publication typeG5 Doctoral dissertation (article)

Fingerprint

Failure analysis
Control systems
Knowledge based systems
Tuning
Availability
Knowledge acquisition
Intelligent control
Fuzzy systems
Fuzzy logic

Keywords

  • fuzzy logic
  • knowledge engineering
  • model-based fault diagnosis
  • computer programs
  • embedded systems

Cite this

Rauma, T. (1999). Fuzzy modeling for industrial systems: Dissertation. Espoo: VTT Technical Research Centre of Finland.
Rauma, Tapio. / Fuzzy modeling for industrial systems : Dissertation. Espoo : VTT Technical Research Centre of Finland, 1999. 139 p.
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note = "Project code: E6SU00081",
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Rauma, T 1999, 'Fuzzy modeling for industrial systems: Dissertation', Doctor Degree, University of Oulu, Espoo.

Fuzzy modeling for industrial systems : Dissertation. / Rauma, Tapio.

Espoo : VTT Technical Research Centre of Finland, 1999. 139 p.

Research output: ThesisDissertationCollection of Articles

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AB - In developing knowledge-based fuzzy systems it is possible to utilize several different development processes. In this thesis the incremental development process, where systems are analyzed, designed, installed and tested in parts, is seen as the most suitable one. Building a knowledge-based system is a human-oriented interactive process, that is disturbed by biases, mistakes, misunderstandings, and different levels of expertise. Therefore the acquired information should be validated before using it in any installed expert (knowledge-based) system. In this thesis we present methods to validate acquired information by combining heuristic knowledge and measured knowledge (data). Fuzzy models are built using heuristic knowledge and data is used to finetune parameters of the models. The validity of acquired knowledge can be estimated with analyzing the tuning results. An essential point of this thesis is that fuzzy models, developed in the knowledge acquisition phase, can be utilized later during the development work. The main areas concerned are model-based control and diagnostic systems. By using experimental results we present methods to construct model-based control systems and fault diagnosis systems by using the incremental development process, fuzzy models, modular architecture, and advanced techniques. The results show that incremental development process can be applied also when model-based systems are built. In fault diagnosis, fuzzy modeling gives the possibilities to increase the level of adaptivity and robustness in the future, after reliable tuning and learning methods have fulfilled demands. In this thesis we present new methods in applying fuzzy logic and fuzzy model-based systems in developing fault diagnosis systems. More intelligent control systems are seen as one important factor in the future. When the complexity of machines and processes increases, manual operation of them becomes difficult, even impossible. This has to be taken into account in developing control systems to ensure the high availability of target systems. Our solution, presented in this research, is to use the information produced by a fault diagnosis system in adapting the control system. This makes the control system more robust, and increases the availability of a target system. The solution is based on using modular architecture, in which meta-rule modules are exploited to produce the adaptivity needed. The solution is formed so that fault diagnosis information can be installed later, after experiences of using the target system have been gained.

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KW - embedded systems

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T3 - VTT Publications

PB - VTT Technical Research Centre of Finland

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Rauma T. Fuzzy modeling for industrial systems: Dissertation. Espoo: VTT Technical Research Centre of Finland, 1999. 139 p.