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Statistical and Mathematical Approach for Distribution Function Model for Low Probability of Failures PUBLIC ACCESS

[+] Author Affiliations
S. R. Bhonsle, C. V. VanKarsen, J. R. Michler

Michigan Technological University, Houghton, MI

Paper No. 91-GT-151, pp. V005T14A023; 6 pages
doi:10.1115/91-GT-151
From:
  • ASME 1991 International Gas Turbine and Aeroengine Congress and Exposition
  • Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award; General
  • Orlando, Florida, USA, June 3–6, 1991
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-7902-3
  • Copyright © 1991 by ASME

abstract

In probabilistic design it is common practice to use statistical models such as normal, lognormal, and Weibull to describe random design factors. However these distribution function models deviate in the lower tail, i.e. percentiles below 1%. The deviation is nonconservative in that since it predicts life longer than observed.

A Statistical Distribution Function called Adaptive Distribution Function Model similar to Abelkis model was developed. It is compatible with the collected data, and it produces conservative designs at low tail ends. It is also relatively easy to use.

Copyright © 1991 by ASME
This article is only available in the PDF format.

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