Name
Abstract | ||
|---|---|---|
The paper deals with a model-based fault diagnosis for a DC motor realized using artificial neural networks. The considered process was modelled by using a neural network composed of dynamic neuron models. Decision making about possible faults was performed using statistical analysis of a residual. A neural network was applied to density shaping of a residual, and after that, assuming a significance level, a threshold was calculated. Moreover, to isolate faults a neural classifier was developed. The proposed approach was tested in a DC motor laboratory system at the nominal operating conditions as well as in the case of faults. |
Year | Venue | Keywords |
|---|---|---|
2007 | ICINCO 2007: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL ICSO: INTELLIGENT CONTROL SYSTEMS AND OPTIMIZATION | neural networks, DC motor, modelling, density shaping, fault detection, fault isolation, fault identification |
Field | DocType | Citations |
Control theory,Control engineering,DC motor,Engineering,Artificial neural network | Conference | 2 |
PageRank | References | Authors |
0.52 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krzysztof Patan | 1 | 151 | 18.13 |
| Józef Korbicz | 2 | 127 | 16.23 |
| Gracjan Glowacki | 3 | 2 | 0.52 |