Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes

2008
ISBN: 978-3-540-79871-2
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 377, 206 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 359 g

Reihe: Lecture Notes in Control and Information Sciences

An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is re?ected in plenty of papers on fault diagnosis in many control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon model basedfault diagnosis has been accumulated through scienti?c literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.

Zielgruppe

Research

Autoren/Hrsg.

Patan, Krzysztof

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik Mathematik Mathematische Analysis Variationsrechnung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme

Inhaltsverzeichnis

Modelling Issue in Fault Diagnosis.- Locally Recurrent Neural Networks.- Approximation Abilities of Locally Recurrent Networks.- Stability and Stabilization of Locally Recurrent Networks.- Optimum Experimental Design for Locally Recurrent Networks.- Decision Making in Fault Detection.- Industrial Applications.- Concluding Remarks and Further Research Directions.