Documents disponibles écrits par cet auteur

Data-driven fault detection in aircraft engines with noisy sensor measurements / Sarkar, Soumik in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 133 N° 8 (Août 2011) 

Public ISBD [article]  in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 133 N° 8 (Août 2011) . - 10 p. Titre : Data-driven fault detection in aircraft engines with noisy sensor measurements Type de document : texte imprimé Auteurs : Sarkar, Soumik, Auteur ; Xin Jin, Auteur ; Ray, Asok, Auteur Année de publication : 2011 Article en page(s) : 10 p. Note générale : Génie Mécanique Langues : Anglais (eng) Mots-clés : Aerospace  engines  Aerospace  simulation  Aircraft  testing  Fault  location  Feature  extraction  Gas  turbines  Pattern  classification  Sensors  Time  series Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux 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=JETPEZ00013 [...] [article] Data-driven fault detection in aircraft engines with noisy sensor measurements [texte imprimé] / Sarkar, Soumik, Auteur ; Xin Jin, Auteur ; Ray, Asok, Auteur . - 2011 . - 10 p. Génie Mécanique Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 133 N° 8 (Août 2011) . - 10 p. Mots-clés : Aerospace  engines  Aerospace  simulation  Aircraft  testing  Fault  location  Feature  extraction  Gas  turbines  Pattern  classification  Sensors  Time  series Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux 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=JETPEZ00013 [...]