FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2169661131> ?p ?o ?g. }

• W2169661131 endingPage "216" @default.
• W2169661131 startingPage "203" @default.
• W2169661131 abstract "In many textbook solutions, for systems failure diagnosis problems studied using reliability theory and artificial intelligence, the prior probabilities of different failure states can be estimated and used to guide the sequential search for failed components after the whole system fails. In practice, however, both the component failure probabilities and the structure function of the system being examined—i.e., the mapping between the states of its components and the state of the system—may not be known with certainty. At best:, the probabilities of different hypothesized system descriptions, each specifying the component failure probabilities and the system's structure function, may be known to a useful approximation, perhaps based on sample data and previous experience. Cost-effective diagnosis of the system's failure state is then a challenging problem. Although the probabilities of component failures are aleatory, uncertainties about these probabilities and about the system structure function are epistemic. This paper examines how to make best use of both epistemic prior probabilities for system descriptions and the information gleaned from costly inspections of component states after the system fails, to minimize the average cost of identifying the failure state. Two approaches are introduced for systems dominated by aleatory uncertainties, one motivated by information theory and the other based on the idea of trying to prove a hypothesis about the identity of the failure state as efficiently as possible. While the general problem of cost-effective failure diagnosis is computationally intractable (NP-hard), both heuristics provide useful approximations on small to moderate sized problems and optimal results for certain common types of reliability systems, including series, parallel, parallel-series, and k-out-of-n systems. A hybrid heuristic that adaptively chooses which heuristic to apply next after any sequence of observations (component test results) appears to give excellent results. Several computational experiments are summarized in support of these conclusions, and extensions to reliability systems with repair are briefly considered. Next, it is shown that diagnosis can proceed when aleatory and epistemic uncertainties are both present using the same techniques developed for aleatory probabilities alone. If only the epistemic probability distribution of system descriptions is known, then the same heuristics that are used to diagnose a system's failure state for systems with known descriptions can also be used to identify the system and diagnose its failure state when there is epistemic uncertainty about the identity of the system. This result suggests a unified approach to least-cost failure diagnosis in reliability systems with both aleatory probabilities of component failures and epistemic probabilities for system descriptions." @default.
• W2169661131 created "2016-06-24" @default.
• W2169661131 creator A5003499139 @default.
• W2169661131 creator A5005736242 @default.
• W2169661131 creator A5054171209 @default.
• W2169661131 date "1996-11-01" @default.
• W2169661131 modified "2023-09-26" @default.
• W2169661131 title "Least-cost failure diagnosis in uncertain reliability systems" @default.
• W2169661131 cites W1493140204 @default.
• W2169661131 cites W1514768918 @default.
• W2169661131 cites W1596324102 @default.
• W2169661131 cites W1968413363 @default.
• W2169661131 cites W2044141362 @default.
• W2169661131 cites W2050147177 @default.
• W2169661131 cites W2075349712 @default.
• W2169661131 cites W2094839178 @default.
• W2169661131 cites W2109554207 @default.
• W2169661131 cites W2110546951 @default.
• W2169661131 cites W2117889403 @default.
• W2169661131 cites W2125697860 @default.
• W2169661131 cites W2166843422 @default.
• W2169661131 cites W2947000318 @default.
• W2169661131 cites W93945635 @default.
• W2169661131 doi "https://doi.org/10.1016/s0951-8320(96)00076-2" @default.
• W2169661131 hasPublicationYear "1996" @default.
• W2169661131 type Work @default.
• W2169661131 sameAs 2169661131 @default.
• W2169661131 citedByCount "11" @default.
• W2169661131 countsByYear W21696611312013 @default.
• W2169661131 crossrefType "journal-article" @default.
• W2169661131 hasAuthorship W2169661131A5003499139 @default.
• W2169661131 hasAuthorship W2169661131A5005736242 @default.
• W2169661131 hasAuthorship W2169661131A5054171209 @default.
• W2169661131 hasConcept C11413529 @default.
• W2169661131 hasConcept C121332964 @default.
• W2169661131 hasConcept C126255220 @default.
• W2169661131 hasConcept C127413603 @default.
• W2169661131 hasConcept C127705205 @default.
• W2169661131 hasConcept C14036430 @default.
• W2169661131 hasConcept C154945302 @default.
• W2169661131 hasConcept C163164238 @default.
• W2169661131 hasConcept C163258240 @default.
• W2169661131 hasConcept C168167062 @default.
• W2169661131 hasConcept C200601418 @default.
• W2169661131 hasConcept C201729545 @default.
• W2169661131 hasConcept C33923547 @default.
• W2169661131 hasConcept C41008148 @default.
• W2169661131 hasConcept C43214815 @default.
• W2169661131 hasConcept C48103436 @default.
• W2169661131 hasConcept C62520636 @default.
• W2169661131 hasConcept C78458016 @default.
• W2169661131 hasConcept C86803240 @default.
• W2169661131 hasConcept C97355855 @default.
• W2169661131 hasConceptScore W2169661131C11413529 @default.
• W2169661131 hasConceptScore W2169661131C121332964 @default.
• W2169661131 hasConceptScore W2169661131C126255220 @default.
• W2169661131 hasConceptScore W2169661131C127413603 @default.
• W2169661131 hasConceptScore W2169661131C127705205 @default.
• W2169661131 hasConceptScore W2169661131C14036430 @default.
• W2169661131 hasConceptScore W2169661131C154945302 @default.
• W2169661131 hasConceptScore W2169661131C163164238 @default.
• W2169661131 hasConceptScore W2169661131C163258240 @default.
• W2169661131 hasConceptScore W2169661131C168167062 @default.
• W2169661131 hasConceptScore W2169661131C200601418 @default.
• W2169661131 hasConceptScore W2169661131C201729545 @default.
• W2169661131 hasConceptScore W2169661131C33923547 @default.
• W2169661131 hasConceptScore W2169661131C41008148 @default.
• W2169661131 hasConceptScore W2169661131C43214815 @default.
• W2169661131 hasConceptScore W2169661131C48103436 @default.
• W2169661131 hasConceptScore W2169661131C62520636 @default.
• W2169661131 hasConceptScore W2169661131C78458016 @default.
• W2169661131 hasConceptScore W2169661131C86803240 @default.
• W2169661131 hasConceptScore W2169661131C97355855 @default.
• W2169661131 hasIssue "2-3" @default.
• W2169661131 hasLocation W21696611311 @default.
• W2169661131 hasOpenAccess W2169661131 @default.
• W2169661131 hasPrimaryLocation W21696611311 @default.
• W2169661131 hasRelatedWork W1966433320 @default.
• W2169661131 hasRelatedWork W2014884629 @default.
• W2169661131 hasRelatedWork W2027301791 @default.
• W2169661131 hasRelatedWork W2081105808 @default.
• W2169661131 hasRelatedWork W2149335246 @default.
• W2169661131 hasRelatedWork W2388730101 @default.
• W2169661131 hasRelatedWork W2470700279 @default.
• W2169661131 hasRelatedWork W2899442959 @default.
• W2169661131 hasRelatedWork W3004271926 @default.
• W2169661131 hasRelatedWork W4247945096 @default.
• W2169661131 hasVolume "54" @default.
• W2169661131 isParatext "false" @default.
• W2169661131 isRetracted "false" @default.
• W2169661131 magId "2169661131" @default.
• W2169661131 workType "article" @default.