This brief paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to analysis of transient time series during takeoff. The algorithms have been validated by simulation on the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPSS) transient test-case generator.
Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines
Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received October 11, 2011; final manuscript received July 27, 2012; published online November 7, 2012. Assoc. Editor: Eugenio Schuster.
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Sarkar, S., Mukherjee, K., Sarkar, S., and Ray, A. (November 7, 2012). "Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines." ASME. J. Dyn. Sys., Meas., Control. January 2013; 135(1): 014506. https://doi.org/10.1115/1.4007699
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