Symbolic time series analysis of ultrasonic data for early detection of fatigue damage

Shalabh Gupta, Asok Ray, Eric Keller

    Research output: Contribution to journalArticle

    77 Scopus citations

    Abstract

    This paper presents a novel analytical tool for early detection of fatigue damage in polycrystalline alloys that are commonly used in mechanical structures. Time series data of ultrasonic sensors have been used for anomaly detection in the statistical behaviour of structural materials, where the analysis is based on the principles of symbolic dynamics and automata theory. The performance of the proposed method has been evaluated relative to existing pattern recognition tools, such as neural networks and principal component analysis, for detection of small changes in the statistical characteristics of the observed data sequences. This concept is experimentally validated on a special-purpose test apparatus for 7075-T6 aluminium alloy specimens, where the anomalies accrue from small fatigue crack growth.

    Original languageEnglish (US)
    Pages (from-to)866-884
    Number of pages19
    JournalMechanical Systems and Signal Processing
    Volume21
    Issue number2
    DOIs
    StatePublished - Feb 1 2007

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Signal Processing
    • Civil and Structural Engineering
    • Aerospace Engineering
    • Mechanical Engineering
    • Computer Science Applications

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