Name
Title | ||
|---|---|---|
A systematic method for rational definition of plant diagnostic symptoms by self-organizing neural networks |
Abstract | ||
|---|---|---|
A method for evaluation of feature representations and definition of appropriate symptoms for diagnosis of large-scale artifacts is proposed in this paper. The central idea is the extraction of diagnostic information in symptoms obtained by a feature representation through automated categorization. Each possible feature representation is regarded as a feature vector in a specific parameter space. The Kohonen self-organizing network technique was applied to the feature vectors in order to obtain the optimal number of categories. Useful evaluation measures for the rational definition of symptoms were derived from the results of the categorization. By using these measures in evaluation processes, an appropriate set of feature representations can be implemented in a diagnosis system. The performance of the proposed method was evaluated through numerical experiments with a nuclear power plant simulator. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1016/0925-2312(95)00090-9 | Neurocomputing |
Keywords | Field | DocType |
Fault diagnosis,Diversification of feature description,Information derivation,Kohonen network,Nuclear plant | Categorization,Feature vector,Pattern recognition,Nuclear plant,Feature (computer vision),Self-organizing map,Feature model,Artificial intelligence,Parameter space,Artificial neural network,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 2-4 | 0925-2312 |
Citations | PageRank | References |
5 | 0.82 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroshi Furukawa | 1 | 211 | 31.32 |
| Tohru Ueda | 2 | 5 | 0.82 |
| Masaharu Kitamura | 3 | 24 | 5.44 |