000 02946nam a22005535i 4500
001 978-3-319-71871-2
003 DE-He213
005 20220801215122.0
007 cr nn 008mamaa
008 171230s2018 sz | s |||| 0|eng d
020 _a9783319718712
_9978-3-319-71871-2
024 7 _a10.1007/978-3-319-71871-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLemma, Tamiru Alemu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_942455
245 1 2 _aA Hybrid Approach for Power Plant Fault Diagnostics
_h[electronic resource] /
_cby Tamiru Alemu Lemma.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXII, 283 p. 161 illus., 138 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v743
505 0 _aIntroduction -- Literature Review -- Model Identification using Neuro-Fuzzy Approach -- Model Uncertainity, Fault Detection and Diagnostics -- Intelligent Fault Detection and Diagnostics -- Application Studies, Part-I: Model Identification and Validation -- Application Studies, Part-II: Fault Detection and Diagnostics -- Conclusion and Recommendation.
520 _aThis book provides a hybrid approach to fault detection and diagnostics. It presents a detailed analysis related to practical applications of the fault detection and diagnostics framework, and highlights recent findings on power plant nonlinear model identification and fault diagnostics. The effectiveness of the methods presented is tested using data acquired from actual cogeneration and cooling plants (CCPs). The models presented were developed by applying Neuro-Fuzzy (NF) methods. The book offers a valuable resource for researchers and practicing engineers alike.
650 0 _aComputational intelligence.
_97716
650 0 _aComputer vision.
_942456
650 0 _aElectric power production.
_927574
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aComputer Vision.
_942457
650 2 4 _aElectrical Power Engineering.
_931821
650 2 4 _aMechanical Power Engineering.
_932122
710 2 _aSpringerLink (Online service)
_942458
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319718699
776 0 8 _iPrinted edition:
_z9783319718705
776 0 8 _iPrinted edition:
_z9783319891125
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v743
_942459
856 4 0 _uhttps://doi.org/10.1007/978-3-319-71871-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77130
_d77130