On Bayesian System Reliability Analysis
Soerensen Ringi, M
22 NUCLEAR REACTOR TECHNOLOGY; PROBABILISTIC ESTIMATION; FAILURES; INFORMATION NEEDS; RELIABILITY; STATISTICS
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person`s state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs.
Linkoeping Univ. (Sweden). Dept. of Mechanical Engineering
INIS; OSTI as DE96606569
Sweden
1995-05-01
English
Thesis/Dissertation
Other Information: TH: Thesis (TeknL).; PBD: May 1995; Related Information: Linkoeping Studies in Science and Technology. Thesis, 490
Medium: ED; Size: 92 p.
https://doi.org/
ON: DE96606569; ISBN 91-7871-548-2
LIU-TEK-LIC-95-22
Journal ID: ISSN 0280-7971; Other: ON: DE96606569; ISBN 91-7871-548-2; TRN: SE9500148077237
SWDN; SCA: 220200; PA: AIX-26:077237; EDB-96:014647; NTS-96:008525; SN: 96001512704
2011-08-18
158000
https://www.osti.gov/etdeweb/servlets/purl/158000