Matches in SemOpenAlex for { <https://semopenalex.org/work/W2490890388> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W2490890388 abstract "Energy production units are large complex installations comprised of several smaller units, subsystems, and mechanical components, whose monitoring and control to secure safe operation are high demanding tasks. In particular, human operators are required to monitor a high volume of incoming data and must make critical decisions in very short time. Although they are explicitly trained in such situations, there are cases that may not be able to identify a gradually developing crucial faulty state. To that end, automated systems can be used for monitoring operational quantities and detecting potential faults in time. The field of machine learning offers a variety of tools that may be used as the ground for developing automated monitoring and control systems for energy systems. In the current chapter, we present an approach that adopts a single Gaussian process learning machine in monitoring high complex energy systems. The Gaussian process is a data-driven model assigned to monitor a set of operational parameters. The values of the operational parameters at a specific instance comprise the system’s operational vector at that time instance. The operational vector consists the input to the individual Gaussian process machine whose task is to classify the operation of the system either as normal (or steady state) or match it to a faulty state. The presented approach is benchmarked on a set of experimentally data taken from the Fix-II test facility that is a representation of a Boiling Water Reactor. Obtained results exhibit the potential of Gaussian processes in monitoring highly complex systems such as nuclear reactors, by identifying with high accuracy the faults in system operation." @default.
- W2490890388 created "2016-08-23" @default.
- W2490890388 creator A5051620719 @default.
- W2490890388 creator A5074118906 @default.
- W2490890388 creator A5076719857 @default.
- W2490890388 date "2016-01-01" @default.
- W2490890388 modified "2023-09-27" @default.
- W2490890388 title "Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors" @default.
- W2490890388 cites W1531115374 @default.
- W2490890388 cites W1654016072 @default.
- W2490890388 cites W177209644 @default.
- W2490890388 cites W1970921864 @default.
- W2490890388 cites W1987093071 @default.
- W2490890388 cites W2005305357 @default.
- W2490890388 cites W2035958365 @default.
- W2490890388 cites W2044309218 @default.
- W2490890388 cites W2045186954 @default.
- W2490890388 cites W2068193536 @default.
- W2490890388 cites W2094658738 @default.
- W2490890388 cites W2120390927 @default.
- W2490890388 cites W2129736434 @default.
- W2490890388 cites W2131628677 @default.
- W2490890388 cites W2134788745 @default.
- W2490890388 cites W2156530876 @default.
- W2490890388 cites W2162761343 @default.
- W2490890388 cites W2169739344 @default.
- W2490890388 cites W2291965947 @default.
- W2490890388 cites W242851230 @default.
- W2490890388 cites W4211049957 @default.
- W2490890388 cites W4240177667 @default.
- W2490890388 cites W4252103803 @default.
- W2490890388 doi "https://doi.org/10.1007/978-3-662-49179-9_8" @default.
- W2490890388 hasPublicationYear "2016" @default.
- W2490890388 type Work @default.
- W2490890388 sameAs 2490890388 @default.
- W2490890388 citedByCount "0" @default.
- W2490890388 crossrefType "book-chapter" @default.
- W2490890388 hasAuthorship W2490890388A5051620719 @default.
- W2490890388 hasAuthorship W2490890388A5074118906 @default.
- W2490890388 hasAuthorship W2490890388A5076719857 @default.
- W2490890388 hasConcept C111919701 @default.
- W2490890388 hasConcept C114614502 @default.
- W2490890388 hasConcept C121332964 @default.
- W2490890388 hasConcept C12267149 @default.
- W2490890388 hasConcept C127413603 @default.
- W2490890388 hasConcept C133731056 @default.
- W2490890388 hasConcept C152745839 @default.
- W2490890388 hasConcept C154945302 @default.
- W2490890388 hasConcept C163716315 @default.
- W2490890388 hasConcept C172707124 @default.
- W2490890388 hasConcept C33923547 @default.
- W2490890388 hasConcept C41008148 @default.
- W2490890388 hasConcept C61326573 @default.
- W2490890388 hasConcept C62520636 @default.
- W2490890388 hasConcept C74193536 @default.
- W2490890388 hasConcept C98045186 @default.
- W2490890388 hasConceptScore W2490890388C111919701 @default.
- W2490890388 hasConceptScore W2490890388C114614502 @default.
- W2490890388 hasConceptScore W2490890388C121332964 @default.
- W2490890388 hasConceptScore W2490890388C12267149 @default.
- W2490890388 hasConceptScore W2490890388C127413603 @default.
- W2490890388 hasConceptScore W2490890388C133731056 @default.
- W2490890388 hasConceptScore W2490890388C152745839 @default.
- W2490890388 hasConceptScore W2490890388C154945302 @default.
- W2490890388 hasConceptScore W2490890388C163716315 @default.
- W2490890388 hasConceptScore W2490890388C172707124 @default.
- W2490890388 hasConceptScore W2490890388C33923547 @default.
- W2490890388 hasConceptScore W2490890388C41008148 @default.
- W2490890388 hasConceptScore W2490890388C61326573 @default.
- W2490890388 hasConceptScore W2490890388C62520636 @default.
- W2490890388 hasConceptScore W2490890388C74193536 @default.
- W2490890388 hasConceptScore W2490890388C98045186 @default.
- W2490890388 hasLocation W24908903881 @default.
- W2490890388 hasOpenAccess W2490890388 @default.
- W2490890388 hasPrimaryLocation W24908903881 @default.
- W2490890388 hasRelatedWork W1743342729 @default.
- W2490890388 hasRelatedWork W1967625228 @default.
- W2490890388 hasRelatedWork W1983909998 @default.
- W2490890388 hasRelatedWork W2044458101 @default.
- W2490890388 hasRelatedWork W2115398131 @default.
- W2490890388 hasRelatedWork W2139435351 @default.
- W2490890388 hasRelatedWork W2298029947 @default.
- W2490890388 hasRelatedWork W2594201622 @default.
- W2490890388 hasRelatedWork W2800387005 @default.
- W2490890388 hasRelatedWork W2951897430 @default.
- W2490890388 hasRelatedWork W2986670754 @default.
- W2490890388 hasRelatedWork W2997679862 @default.
- W2490890388 hasRelatedWork W3024435332 @default.
- W2490890388 hasRelatedWork W3114205451 @default.
- W2490890388 hasRelatedWork W3185407626 @default.
- W2490890388 hasRelatedWork W55777355 @default.
- W2490890388 hasRelatedWork W58214161 @default.
- W2490890388 hasRelatedWork W634661854 @default.
- W2490890388 hasRelatedWork W922983276 @default.
- W2490890388 hasRelatedWork W2184484365 @default.
- W2490890388 isParatext "false" @default.
- W2490890388 isRetracted "false" @default.
- W2490890388 magId "2490890388" @default.
- W2490890388 workType "book-chapter" @default.