Identification of components to optimize improvement in system reliability
- Carnegie-Mellon Univ., Pittsburgh, PA (United States). Dept. of Engineering and Public Policy
- Sandia National Labs., Albuquerque, NM (United States)
The fields of reliability analysis and risk assessment have grown dramatically since the 1970s. There are now bodies of literature and standard practices which cover quantitative aspects of system analysis such as failure rate and repair models, fault and event tree generation, minimal cut sets, classical and Bayesian analysis of reliability, component and system testing techniques, decomposition methods, etc. In spite of the growth in the sophistication of reliability models, however, little has been done to integrate optimization models within a reliability analysis framework. That is, often reliability models focus on characterization of system structure in terms of topology and failure/availability characteristics of components. A number of approaches have been proposed to help identify the components of a system that have the largest influence on overall system reliability. While this may help rank order the components, it does not necessarily help a system design team identify which components they should improve to optimize overall reliability (it may be cheaper and more effective to focus on improving two or three components of smaller importance than one component of larger importance). In this paper, we present an optimization model that identifies the components to be improved to maximize the increase in system MTBF, subject to a fixed budget constraint. A dual formulation of the model is to minimize cost, subject to achieving a certain level of system reliability.
- Research Organization:
- Sandia National Labs., Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 10103038
- Report Number(s):
- SAND-93-2420C; CONF-940312-22; ON: DE94001601; BR: GB0103012
- Resource Relation:
- Conference: 2. Probabilistic safety assessment and management conference (PSAM),San Diego, CA (United States),20-24 Mar 1994; Other Information: PBD: [1993]
- Country of Publication:
- United States
- Language:
- English
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