• DocumentCode
    1911134
  • Title

    Reliability and Degradation Modeling with Random or Uncertain Failure Threshold

  • Author

    Wang, Peng ; Coit, David W.

  • fYear
    2007
  • fDate
    22-25 Jan. 2007
  • Firstpage
    392
  • Lastpage
    397
  • Abstract
    This paper developed extensions to the existing research so that reliability assessment based on degradation modeling can address new problem domains that previously did not meet the required assumptions and modeling constraints. Degradation modeling is based on probabilistic modeling of a failure mechanism degradation path and comparison of a projected distribution to a pre-defined failure threshold. Previous approaches to this problem required that the predefined failure threshold must be considered as a fixed deterministic value, which can be problematic for several reasons. Often, the designer and producer of a part or a system have many diverse users of their products. In practice, the critical threshold value can vary appreciably among users. In this case, a probabilistic, rather than a deterministic threshold value is more appropriate. For other applications, the designer may not know with certainty what explicit level of degradation will cause a failure. In this case, specification of a range of possible threshold values is more appropriate. This also can be accommodated by considering the threshold value as a random variable with some assumed distribution to reflect the variability. New modeling approaches are presented in this paper such that this limiting assumption is no longer required. This should allow systems with more varied usage conditions and failure mechanisms to be analyzed using degradation-based reliability assessment methods.
  • Keywords
    failure analysis; probability; reliability; degradation-based reliability assessment methods; failure mechanism degradation path; failure threshold; probabilistic modeling; Data analysis; Degradation; Electrical resistance measurement; Failure analysis; Life testing; Loss measurement; Pollution measurement; Predictive models; Random variables; Time measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Reliability and Maintainability Symposium, 2007. RAMS '07. Annual
  • Conference_Location
    Orlando, FL
  • ISSN
    0149-144X
  • Print_ISBN
    0-7803-9766-5
  • Electronic_ISBN
    0149-144X
  • Type

    conf

  • DOI
    10.1109/RAMS.2007.328107
  • Filename
    4126383