Ebook: Artificial neural networks for the modelling and fault diagnosis of technical processes

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.

The book is mainly focused on investigating 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.

The material included in the monograph results from research that has been carried out at the Institute of Control and Computation Engineering of the University of Zielona Góra, Poland, for the last eight years in the area of the modelling of non-linear dynamic processes as well as fault diagnosis of industrial processes.