國立虎尾科技大學 |

A Hybrid Approach for Power Plant Fault Diagnostics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A Hybrid Approach for Power Plant Fault Diagnostics/ by Tamiru Alemu Lemma.
作者:	Lemma, Tamiru Alemu.
面頁冊數:	XII, 283 p. 161 illus., 138 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-71871-2
ISBN:	9783319718712

A Hybrid Approach for Power Plant Fault Diagnostics
Lemma, Tamiru Alemu.

A Hybrid Approach for Power Plant Fault Diagnostics[electronic resource] /by Tamiru Alemu Lemma. - 1st ed. 2018. - XII, 283 p. 161 illus., 138 illus. in color.online resource. - Studies in Computational Intelligence,7431860-949X ;. - Studies in Computational Intelligence,564.

Introduction -- 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.

This 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.

ISBN: 9783319718712

Standard No.: 10.1007/978-3-319-71871-2doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

A Hybrid Approach for Power Plant Fault Diagnostics
LDR:02331nam a22004095i 4500 001 990678
003 DE-He213
005 20200703221245.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319718712 $9 978-3-319-71871-2
024 7 $a 10.1007/978-3-319-71871-2 $2 doi
035 $a 978-3-319-71871-2
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Lemma, Tamiru Alemu. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1282370
245 1 2 $a A Hybrid Approach for Power Plant Fault Diagnostics $h [electronic resource] / $c by Tamiru Alemu Lemma.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XII, 283 p. 161 illus., 138 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $v 743
505 0 $a Introduction -- 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 $a This 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 $a Computational intelligence. $3 568984
650 0 $a Optical data processing. $3 639187
650 0 $a Power electronics. $3 561011
650 0 $a Energy systems. $3 1253529
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Power Electronics, Electrical Machines and Networks. $3 768690
650 2 4 $a Energy Systems. $3 785876
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319718699
776 0 8 $i Printed edition: $z 9783319718705
776 0 8 $i Printed edition: $z 9783319891125
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-319-71871-2
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)