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

1. Front Matter

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2. Introduction

   • Krzysztof Patan

   Pages 1-6

3. Modelling Issue in Fault Diagnosis

   • Krzysztof Patan

   Pages 7-27

4. Locally Recurrent Neural Networks

   • Krzysztof Patan

   Pages 29-63

5. Approximation Abilities of Locally Recurrent Networks

   • Krzysztof Patan

   Pages 65-75

6. Stability and Stabilization of Locally Recurrent Networks

   • Krzysztof Patan

   Pages 77-112

7. Optimum Experimental Design for Locally Recurrent Networks

   • Krzysztof Patan

   Pages 113-122

8. Decision Making in Fault Detection

   • Krzysztof Patan

   Pages 123-140

9. Industrial Applications

   • Krzysztof Patan

   Pages 141-185

10. Concluding Remarks and Further Research Directions

    • Krzysztof Patan

    Pages 187-189

11. Back Matter

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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.

• 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

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