| タイトル | Using Neural Networks for Sensor Validation |
| 本文（外部サイト） | http://hdl.handle.net/2060/19980209658 |
| 著者（英） | Jaw, Link C.; Graham, Ronald; McCoy, William; Guo, Ten-Huei; Mattern, Duane L. |
| 著者所属（英） | NASA Lewis Research Center |
| 発行日 | 1998-07-01 |
| 言語 | eng |
| 内容記述 | This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed. |
| NASA分類 | Aircraft Instrumentation |
| レポートNO | E-11258
NASA/TM-1998-208483
NAS 1.15:208483
AIAA Paper 98-3547 |
| 権利 | No Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/98661 |
