Stochastic modeling of fatigue damage dynamics for failure prognostics and risk analysis

Asok Ray, James C. Newman

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    Failure prognostics and risk analysis facilitate maintenance scheduling and operation planning of aging mechanical systems (e.g., power and processing plants, aircraft, and civil infrastructures such as bridges and industrial buildings). Decision tools for failure prognostics must have the capability of incorporating the dynamic behavior of material damage under both normal and off-normal operating conditions. This paper presents a nonlinear stochastic model of fatigue damage dynamics and a filter for on-line estimation of the first two moments of the time-dependent damage accumulation. The results have been verified with experimental data of fatigue damage statistics for the 2024-T3 Aluminum alloy.

    Original languageEnglish (US)
    Pages (from-to)1610-1614
    Number of pages5
    JournalProceedings of the American Control Conference
    Volume3
    StatePublished - Jan 1 1995

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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