Application of Cerebellar Model Articulation Controller Based on Neural Network for Fault Diagnoses of the Steam Turbine-Generators and the Condensers in Fossil-Fuel Powerplants
| Autor: | Wen-Lang Lin, 林文郎 |
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| Rok vydání: | 2004 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 92 In this paper, a cerebellar model articulation controller (CMAC) neural network application to fault diagnosis is proposed. This novel fault diagnosis system contains an input layer, quantization layer, binary coding layer, and fired up memory addresses coding unit. Firstly, we construct the configuration of diagnosis system depending on the fault patterns. Then, the known fault patterns are used to train the neural network. Finally, the diagnosis system diagnoses the fault types. Utilizing all the characteristic of self-learning, association and generalization, like the cerebellum of human being, the proposed CMAC neural network fault diagnosis scheme enables a powerful, straightforward, and efficient fault diagnosis. Furthermore, the following merits are obtained: high learning and diagnosis speed, high noise rejection ability, alleviating the dependency to expert’s expertise, eliminating the weights interference between different fault type patterns, memory size reduction by new excited addresses coding technique, and ability to fault diagnosis system of more layers. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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