| タイトル | A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics |
| 本文（外部サイト） | http://hdl.handle.net/2060/20010069983 |
| 著者（英） | Kobayashi, Takahisa; Simon, Donald L. |
| 著者所属（英） | Army Research Lab.|NASA Glenn Research Center |
| 発行日 | 2001-07-01 |
| 言語 | eng |
| 内容記述 | In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines. |
| NASA分類 | Aircraft Propulsion and Power |
| レポートNO | ARL-TR-1266
NASA/TM-2001-211088
E-12931
NAS 1.15:211088
AIAA Paper 2001-3763 |
| 権利 | No Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/92750 |
