Data-driven fault detection in aircraft engines with noisy sensor measurements

Soumik Sarkar, Xin Jin, Asok Ray

Research output: Contribution to journalArticle

49 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number081602
JournalJournal of Engineering for Gas Turbines and Power
Volume133
Issue number8
DOIs
StatePublished - Apr 21 2011

All Science Journal Classification (ASJC) codes

  • Nuclear Energy and Engineering
  • Fuel Technology
  • Aerospace Engineering
  • Energy Engineering and Power Technology
  • Mechanical Engineering

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