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A Deep Belief Network Based Approach...

ProQuest Information and Learning Co.

A Deep Belief Network Based Approach for Bearing Fault Diagnosis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A Deep Belief Network Based Approach for Bearing Fault Diagnosis./
作者:	Akkad, Khaled Mohammad A.
面頁冊數:	1 online resource (49 pages)
附註:	Source: Masters Abstracts International, Volume: 56-03.
標題:	Industrial engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369597301

A Deep Belief Network Based Approach for Bearing Fault Diagnosis.
Akkad, Khaled Mohammad A.

A Deep Belief Network Based Approach for Bearing Fault Diagnosis. - 1 online resource (49 pages)

Source: Masters Abstracts International, Volume: 56-03.

Thesis (M.S.)--University of Illinois at Chicago, 2016.

Includes bibliographical references

Effective fault diagnosis techniques are crucial for normal and safe machinery operation. The development of data acquisition techniques allows for massive volumes of data to be collected and used for fault diagnostics and prognostics. The main challenge that faces existing methods is the dependency on extracting features manually. A modified deep belief network (MDBN) is proposed for the purposes of bearing fault classification. The proposed method can address the challenge between machinery big data and intelligent diagnosis by extracting the features automatically, with only applying a simple signal processing technique. Two more goals of the proposed method are to increase the speed of learning and to prevent overfitting. To increase the learning speed, momentum is added to the original deep belief network (DBN). To prevent the model from overfitting the training data, weight decay and sparsity of the hidden units are both brought into the proposed MDBN. The proposed MDBN based fault diagnosis proved to be more effective when compared to DBN.

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

Mode of access: World Wide Web

ISBN: 9781369597301Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

A Deep Belief Network Based Approach for Bearing Fault Diagnosis.
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100 1 $a Akkad, Khaled Mohammad A. $3 1188637
245 1 2 $a A Deep Belief Network Based Approach for Bearing Fault Diagnosis.
264 0 $c 2016
300 $a 1 online resource (49 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: Masters Abstracts International, Volume: 56-03.
500 $a Adviser: David He.
502 $a Thesis (M.S.)--University of Illinois at Chicago, 2016.
504 $a Includes bibliographical references
520 $a Effective fault diagnosis techniques are crucial for normal and safe machinery operation. The development of data acquisition techniques allows for massive volumes of data to be collected and used for fault diagnostics and prognostics. The main challenge that faces existing methods is the dependency on extracting features manually. A modified deep belief network (MDBN) is proposed for the purposes of bearing fault classification. The proposed method can address the challenge between machinery big data and intelligent diagnosis by extracting the features automatically, with only applying a simple signal processing technique. Two more goals of the proposed method are to increase the speed of learning and to prevent overfitting. To increase the learning speed, momentum is added to the original deep belief network (DBN). To prevent the model from overfitting the training data, weight decay and sparsity of the hidden units are both brought into the proposed MDBN. The proposed MDBN based fault diagnosis proved to be more effective when compared to DBN.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Industrial engineering. $3 679492
650 4 $a Management. $3 558618
655 7 $a Electronic books. $2 local $3 554714
690 $a 0546
690 $a 0454
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Illinois at Chicago. $b Mechanical and Industrial Engineering. $3 1180413
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10404497 $z click for full text (PQDT)

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