Neural Networks in Model-Based Fault Diagnosis
| Autor: | Birgit Köppen-Seliger, Paul M. Frank |
|---|---|
| Rok vydání: | 1996 |
| Předmět: |
Engineering
Artificial neural network business.industry Time delay neural network Deep learning General Medicine computer.software_genre Residual Probabilistic neural network Recurrent neural network Data mining Artificial intelligence Types of artificial neural networks Stochastic neural network business computer |
| Zdroj: | IFAC Proceedings Volumes. 29:6389-6394 |
| ISSN: | 1474-6670 |
| Popis: | In this contribution neural networks form the basis for model-based fault diagnosis schemes. Three different possibilities to employ neural networks for supervision tasks are discussed. In most existing schemes the steps of residual generation and residual evaluation are performed on the basis of analytical process knowledge. Here, neural networks are proposed for these tasks. First of all a neural network can be Used instead of a mathematical model for residual generation, secondly a different neural network can be trained to perform the classification task for residual evaluation and thus isolate the probable fault cause. A third possibility is an one-step diagnosis (OSD), where one neural network is trained to directly detect and isolate possible faults from the available measurements without the need for prior generation of intermediate signals such as residuals. Results from the application of a modified Radial-Basis-Function (RBF) Neural Network to the residual generation and the Restricted Coulomb Energy (RCE) Neural Network to the one-step diagnosis at an industrial actuator benchmark problem are presented. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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