Volume 39, Issue 6 (November 2011)

    Fuzzy Hypothesis Testing and Time Series Analysis of Rolling Bearing Quality

    (Received 11 September 2010; accepted 24 May 2011)

    Published Online: 2011

    CODEN: JTEOAD

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    Abstract

    Poor information means incomplete and insufficient information, such as unknown probability distributions and trends. Evaluation for the evolvement of the rolling bearing quality as a time series belongs to the category of information poor process. Statistics relied on known probability distributions and trends could become ineffective. For this end, a fuzzy hypothesis testing model is proposed to make variability analysis of a time series with poor information. By introducing the weight into the rejection region, the relationship of the improved equivalence relation and the empirical confidence level is established, laying the new foundation for a fuzzy decision-making for a time series with poor information. The model is characterized by permitting the probability distribution and the trend of a stationary or nonstationary time-series to be unknown. The experimental investigation on the friction torque of a rolling bearing shows that the model is correct and effective.


    Author Information:

    Xia, Xintao
    Henan Univ. of Science and Technology,

    Chen, Jianfeng
    Henan Univ. of Science and Technology,


    Stock #: JTE103371

    ISSN: 0090-3973

    DOI: 10.1520/JTE103371

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    Title Fuzzy Hypothesis Testing and Time Series Analysis of Rolling Bearing Quality
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    Committee