| Titre : | Data-driven fault detection in aircraft engines with noisy sensor measurements (2011) |
| Auteurs : | Sarkar, Soumik, Auteur ; Xin Jin, Auteur ; Ray, Asok, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Transactions of the ASME . Journal of engineering for gas turbines and power (Vol. 133 N° 8, Août 2011) |
| Article en page(s) : | 10 p. |
| Note générale : | Génie Mécanique |
| Langues : | Anglais |
| Index. décimale : | 620.1 (Essais des matériaux. Défauts des matériaux. Protection des matériaux) |
| Tags : | Aerospace engines simulation Aircraft testing Fault location Feature extraction Gas turbines Pattern classification Sensors Time series |
| Résumé : | An inherent difficulty in sensor-data-driven fault detection is that the detection performance could be drastically reduced under sensor degradation (e.g., drift and noise). Complementary to traditional model-based techniques for fault detection, this paper proposes symbolic dynamic filtering by optimally partitioning the time series data of sensor observation. The objective here is to mask the effects of sensor noise level variation and magnify the system fault signatures. In this regard, the concepts of feature extraction and pattern classification are used for fault detection in aircraft gas turbine engines. The proposed methodology of data-driven fault detection is tested and validated on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) test-bed developed by NASA for noisy (i.e., increased variance) sensor signals. |
| DEWEY : | 620.1 |
| ISSN : | 0742-4795 |
| En ligne : | http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000133000008081602000001&idtype=cvips&gifs=Yes&ref=no |
