Name
Abstract | ||
|---|---|---|
This paper applies the Taguchi method to filter out the number of input neurons and increases the training efficiency of the dynamic structural neural networks. In order to avoid that omitting the harmonics may affect the fault diagnosis result, this work establishes an index for the fault identification which is based on the features of the first and second harmonics. Together with the identification results of dynamic structural neural network, the diagnosis can be done. The experimental results indicate the proposed method can reduce the iterations dramatically. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6974271 | SMC |
Keywords | DocType | ISSN |
Taguchi methods,dynamic structural neural networks,intelligent motor rotary fault diagnosis system,learning (artificial intelligence),rotors (mechanical),Taguchi method,second-harmonics features,fault diagnosis,dynamic structural neural network,input neuron filtering,first-harmonics features,training efficiency enhancement,mechanical engineering computing,neural nets,motor rotary faults | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chwan-Lu Tseng | 1 | 121 | 24.47 |
| Shun-Yuan Wang | 2 | 9 | 4.86 |
| Foun-Yuan Liu | 3 | 2 | 2.09 |
| Jen-Hsiang Chou | 4 | 6 | 4.40 |
| Yin-Hsien Shih | 5 | 0 | 0.34 |
| Ta-Peng Tsao | 6 | 38 | 3.15 |