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INTEGRATED CONDITION MONITORING AND DIAGNOSIS OF ELECTRICAL MACHINES USING MINIMUM CONFIGURATION ARTIFICIAL INTELLIGENCE
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| Author(s) |
F. Filippetti; C. Tassoni; G. Franceschini; P. Vas |
| Abstract |
The traditional approach to electrical machine condition monitoring based on
deterministic models leads to complex systems hard to maintain and manage. Artificial Intelligence (AI) techniques seem to offer a simpler solution which avoids the use of deterministic models. An effective and simple system acceptable in industrial environments
requires a "minimum configuration intelligence". This paper deals with the application of AI techniques for diagnosis purposes. In particular, new results related to the application of fault diagnosis of electrical machines using Neural Networks with a minimum number of neurons and Fuzzy-Neuro (ANFIS) techniques are presented |
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| Filename: | Unnamed file |
| Filesize: | 739.8 KB |
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| Type |
Members Only |
| Date |
Last modified 2015-12-14 by System |
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