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Fusion of vibration based features f...

Venugopal, Suresh.

Fusion of vibration based features for gear condition classification.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Fusion of vibration based features for gear condition classification./
作者:	Venugopal, Suresh.
面頁冊數:	1 online resource (78 pages)
附註:	Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6379.
Contained By:	Dissertation Abstracts International69-10B.
標題:	Mechanical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780549874713

Fusion of vibration based features for gear condition classification.
Venugopal, Suresh.

Fusion of vibration based features for gear condition classification. - 1 online resource (78 pages)

Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6379.

Thesis (Ph.D.)--The University of Alabama, 2008.

Includes bibliographical references

Proactive maintenance of drive train components precludes unexpected plant shutdowns. There are different methods to monitor machine conditions. This study focuses on vibration based monitoring.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780549874713Subjects--Topical Terms:

557493
Mechanical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Fusion of vibration based features for gear condition classification.
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020 $a 9780549874713
035 $a (MiAaPQ)AAI3334659
035 $a AAI3334659
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Venugopal, Suresh. $3 1189536
245 1 0 $a Fusion of vibration based features for gear condition classification.
264 0 $c 2008
300 $a 1 online resource (78 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6379.
500 $a Adviser: Sally McInerny.
502 $a Thesis (Ph.D.)--The University of Alabama, 2008.
504 $a Includes bibliographical references
520 $a Proactive maintenance of drive train components precludes unexpected plant shutdowns. There are different methods to monitor machine conditions. This study focuses on vibration based monitoring.
520 $a There are a variety of gear condition indicators available. When applied to a vibration time sequence, different indicators behave differently. Also, because there are so many of them, it is difficult to know which one to choose. This work focuses on how to select the condition indicators that provide maximum discriminatory information and also how to reduce false alarm rates by combining different indicators. Forty-six newly developed gear condition indicators (CIs) were added to the already existing framework of 12 CIs, filling the logical gaps existing among them. CIs were calculated on seeded fault test datasets of an input pinion gear. Thirty-six sets of data were recorded in the test until the failure of the gear. This yielded 58 feature vectors, each with a length of 36. Using engineering judgment, the 36 datasets were divided into 4 classes, starting from the initial gear in good condition to the gear near failure.
520 $a Discriminant analysis was applied to the 58 feature vectors to determine a reduce 6 or 7 best features. This set included SLF2, SLF, NB43, FM4, M8A, NBFM43, and NA4. Finally, a simple Baye's classifier was used to combine these features and classify gear condition. The classifier was tested using the training dataset.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mechanical engineering. $3 557493
655 7 $a Electronic books. $2 local $3 554714
690 $a 0548
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Alabama. $3 1189537
773 0 $t Dissertation Abstracts International $g 69-10B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3334659 $z click for full text (PQDT)

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