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.
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e-mail: szs200@psu.edu
e-mail: xuj103@psu.edu
e-mail: axr2@psu.edu
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August 2011
Research Papers
Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements
Soumik Sarkar,
Soumik Sarkar
Department of Mechanical Engineering,
e-mail: szs200@psu.edu
Pennsylvania State University
, University Park, PA 16802
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Xin Jin,
Xin Jin
Department of Mechanical Engineering,
e-mail: xuj103@psu.edu
Pennsylvania State University
, University Park, PA 16802
Search for other works by this author on:
Asok Ray
Asok Ray
Department of Mechanical Engineering,
e-mail: axr2@psu.edu
Pennsylvania State University
, University Park, PA 16802
Search for other works by this author on:
Soumik Sarkar
Department of Mechanical Engineering,
Pennsylvania State University
, University Park, PA 16802e-mail: szs200@psu.edu
Xin Jin
Department of Mechanical Engineering,
Pennsylvania State University
, University Park, PA 16802e-mail: xuj103@psu.edu
Asok Ray
Department of Mechanical Engineering,
Pennsylvania State University
, University Park, PA 16802e-mail: axr2@psu.edu
J. Eng. Gas Turbines Power. Aug 2011, 133(8): 081602 (10 pages)
Published Online: April 6, 2011
Article history
Received:
March 12, 2010
Revised:
September 2, 2010
Online:
April 6, 2011
Published:
April 6, 2011
Citation
Sarkar, S., Jin, X., and Ray, A. (April 6, 2011). "Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements." ASME. J. Eng. Gas Turbines Power. August 2011; 133(8): 081602. https://doi.org/10.1115/1.4002877
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