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Gantry health monitoring and fault detection based on process status sequence
Descriptive
TitleInfo
Title
Gantry health monitoring and fault detection based on process status sequence
Name
(type = personal)
NamePart
(type = family)
SONG
NamePart
(type = given)
RUI
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RUI SONG
Role
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author
Name
(type = personal)
NamePart
(type = family)
Jeong
NamePart
(type = given)
Myong-Kee
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Myong-Kee Jeong
Affiliation
Advisory Committee
Role
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(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Guo
NamePart
(type = given)
Weihong
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Weihong Guo
Affiliation
Advisory Committee
Role
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(authority = RULIB)
co-chair
Name
(type = personal)
NamePart
(type = family)
Hoang
NamePart
(type = given)
Pham
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Pham Hoang
Affiliation
Advisory Committee
Role
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internal member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
School of Graduate Studies
Role
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school
TypeOfResource
Text
Genre
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theses
OriginInfo
DateCreated
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2019
DateOther
(qualifier = exact);
(type = degree)
2019-01
CopyrightDate
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2019
Place
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(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Gantry refers to the system that moves the hoist by the machinery house along tracks on the floor level and transfers the material. As a critical asset, gantry has wide applications in many fields such as medical image area, infrastructure, and heavy industry. Mostly, gantry is reliable, however, the loss led by the gantry lockout is inestimable enormous. Moreover, there are limited previous gantry studies concentrate on the statistical quality control to detect the fault not to mention the research that focuses on the algorithms applied to the process status sequence to detect the fault. The categorical process status sequence is hard to obtain the features when dealing with fault identification. This thesis provides a novel method applying texture extraction in image processing to obtain the features of gantry process status sequence. Texture extraction techniques such as the histogram of oriented gradients (HOG) and local binary pattern are applied to the process status sequence. To demonstrate the effectiveness of image-based feature extraction, k-nearest neighbors, support vector machine, linear discriminant analysis, and quadratic discriminant analysis are applied to the time-series gantry process status sequences provided by a leading automobile manufacturer. Result demonstrates that the sequence after the transformation of both texture extraction techniques have improved the accuracy. The process status sequence after HOG transformation has the best performance. Besides, the HOG technique also dramatically reduces the dimension of the process status sequence. This result can help the on-site expert prognosis the fault as well as prepare the corresponding troubleshooting guide to save time and resources.
Subject
(authority = RUETD)
Topic
Industrial and Systems Engineering
Subject
(authority = ETD-LCSH)
Topic
Structural health monitoring
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_9483
PhysicalDescription
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electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (38 pages) : illustrations
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Rui Song
RelatedItem
(type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier
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rucore10001600001
Location
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(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/t3-t93d-g977
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
SONG
GivenName
RUI
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2019-01-03 21:35:14
AssociatedEntity
Name
RUI SONG
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
RULTechMD
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ContentModel
ETD
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windows xp
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1.7
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2019-01-04T13:26:09
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2019-01-04T13:28:05
ApplicationName
Microsoft: Print To PDF
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