Online detection of fatigue failure via symbolic time series analysis

Shalabh Gupta, Asok Ray, Eric Keller

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    This paper examines the efficacy of symbolic time series analysis for online detection of fatigue failure in mechanical structures. The detection algorithm is formulated on the principles of Symbolic Dynamics and Automata Theory. The performance of this method is evaluated based on the information extracted from available sensor data for early detection of small anomalies in the observed data sequence. This concept is experimentally validated on a fatigue damage test apparatus. The time series data, generated from ultrasonic sensor and optical microscope, have been used for detection of small fatigue crack growth in ductile alloy 7075-T6 aluminium specimens.

    Original languageEnglish (US)
    Article numberThC15.2
    Pages (from-to)3309-3314
    Number of pages6
    JournalProceedings of the American Control Conference
    Volume5
    StatePublished - 2005

    Fingerprint

    Automata theory
    Ultrasonic sensors
    Time series analysis
    Fatigue damage
    Fatigue crack propagation
    Time series
    Microscopes
    Fatigue of materials
    Aluminum
    Sensors

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering

    Cite this

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    Online detection of fatigue failure via symbolic time series analysis. / Gupta, Shalabh; Ray, Asok; Keller, Eric.

    In: Proceedings of the American Control Conference, Vol. 5, ThC15.2, 2005, p. 3309-3314.

    Research output: Contribution to journalArticle

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