Symbolic dynamic analysis of transient time series for fault detection in gas turbine engines

Soumalya Sarkar, Kushal Mukherjee, Soumik Sarkar, Asok Ray

    Research output: Contribution to journalArticle

    17 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish (US)
    Article number014506
    JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
    Volume135
    Issue number1
    DOIs
    StatePublished - Jan 30 2013

    Fingerprint

    gas turbine engines
    fault detection
    Fault detection
    Dynamic analysis
    Gas turbines
    Time series
    Turbines
    takeoff
    systems simulation
    Takeoff
    propulsion
    Propulsion
    NASA
    generators
    simulation

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Information Systems
    • Instrumentation
    • Mechanical Engineering
    • Computer Science Applications

    Cite this

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    Symbolic dynamic analysis of transient time series for fault detection in gas turbine engines. / Sarkar, Soumalya; Mukherjee, Kushal; Sarkar, Soumik; Ray, Asok.

    In: Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Vol. 135, No. 1, 014506, 30.01.2013.

    Research output: Contribution to journalArticle

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