Volume 1, Issue 2 (February 2004)

    Modeling Variability in Service Loading Spectra

    (Received 7 October 2002; accepted 24 June 2003)

    Published Online: 2004

    CODEN: JAIOAD

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    Abstract

    This paper describes a methodology for statistically extrapolating a single measured service loading history to the expected long-term service usage spectra. The measured time history first is processed into a rainflow counted histogram. Nonparametric kernel smoothing techniques are employed to convert the rainflow histogram of cycles into a probability density histogram. Once the probability density histogram is obtained, Monte Carlo methods are used to produce a rainflow histogram of any desired number of cycles. A new loading history then is reconstructed from the expected rainflow histogram, which can be combined with a probabilistic fatigue analysis to obtain an estimate of the durability of a structure. Obtaining an estimate of the loading spectra for a ground vehicle is difficult because there are many users, each with different service usage. The extrapolating methodology is extended to combine data from several users to obtain loading spectra that represent more severe users in the population.


    Author Information:

    Socie, DF
    Professor, University of Illinois at Urbana-Champaign, Urbana, IL

    Pompetzki, MA
    Manager, Business Development Durability, Code International, Southfield, MI


    Stock #: JAI11561

    ISSN: 1546-962X

    DOI: 10.1520/JAI11561

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    Author
    Title Modeling Variability in Service Loading Spectra
    Symposium Probabilistic Aspects of Life Prediction, 2002-11-06
    Committee E08