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

By: Patan, Krzysztof [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Control and Information Sciences: 377Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: XXII, 206 p. 93 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540798729.Subject(s): Engineering | Artificial intelligence | System theory | Statistical physics | Dynamical systems | Control engineering | Robotics | Mechatronics | Engineering | Control, Robotics, Mechatronics | Systems Theory, Control | Artificial Intelligence (incl. Robotics) | Statistical Physics, Dynamical Systems and ComplexityDDC classification: 629.8 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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