Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes
Buch in deiner Nähe kaufen
Beschreibung
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.
Investigates the properties of locally recurrent neural networks, developing training procedures for them and their application to the modelling and fault diagnosis of non-linear dynamic processes and plants Includes an Introduction to fault diagnosis of non-linear systems using artificial neural networks
Autor*in
Krzysztof Patan
Themen in »Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes«
Adaptive Threshold Approximation Abilities Bopp2009 Decision Making Dynamic Neuron Model Experimental Design Fault Diagnosis Identification Model Error Modelling Modelling Neural Networks Non-Linear Systems Recurrent Neural Networks Robust Fa