The Resource Compressor valve failure detection and prognostics, by Raghuram Puthali Ramesh, (electronic resource)

Compressor valve failure detection and prognostics, by Raghuram Puthali Ramesh, (electronic resource)

Label
Compressor valve failure detection and prognostics
Title
Compressor valve failure detection and prognostics
Statement of responsibility
by Raghuram Puthali Ramesh
Creator
Subject
Genre
Language
eng
Summary
"Reciprocating compressors are commonly used machinery for industrial applications. Unscheduled downtime and maintenance activity on the compressors causes considerable loss in throughput and efficiency of a plant. Of all the failures that cause unscheduled downtime in reciprocating compressors, valve related causes are predominant. Most of the failures associated with the valves are tracked to the failure of moving elements within the valve. Achieving higher reliability of critical reciprocating systems requires continuously monitoring the system and performing dynamic analysis of the sensory data for valve fault diagnosis. Continuous monitoring will improve the time and cost to repair through keeping a constant vigil for failure events. Though there has been a good amount of work done for condition monitoring of compressors, there has been very little work on detecting and predicting valve failures. The objective of this thesis is to research detection and prediction of valve failures by wavelet analysis, logistic regression and neural network analysis of pressure and temperature signals, which are the most common measurements on a reciprocating compressor system. Valve failures are seeded on a reciprocating compressor testbed that is instrumented with only temperature and pressure sensor order emulate the reciprocating compressor systems used in the industry. The parameters are measured on a continuous basis and baselines are established for normal (or acceptable) behavior and failure (or fault) condition. Deviation of the system from the normal condition and the time for the system to reach the fault mode is quantified with the help of the above mentioned tools."--Abstract, leaf iii
Related
Cataloging source
UMR
http://library.link/vocab/creatorDate
1982-
http://library.link/vocab/creatorName
Ramesh, Raghuram Puthali
Degree
M.S.
Dissertation year
2007.
Granting institution
University of Missouri--Rolla
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/subjectName
  • Valves
  • Valves
  • Failure Analysis System (Computer system)
  • Logistic regression analysis
  • Neural networks (Computer science)
Target audience
specialized
Label
Compressor valve failure detection and prognostics, by Raghuram Puthali Ramesh, (electronic resource)
Instantiates
Publication
Note
  • Vita
  • The entire thesis text is included in file
  • Title from title screen of thesis/dissertation PDF file (viewed May 16, 2012)
Bibliography note
Includes bibliographical references (pages 52-55)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
793756908
Dimensions
unknown
Extent
1 online resource (viii, 56 pages)
Form of item
electronic
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Other physical details
illustrations, digital, PDF file.
Specific material designation
remote
System control number
(OCoLC)793756908
System details
  • System requirements: Adobe Acrobat Reader; Internet browser
  • Mode of access: World Wide Web
Label
Compressor valve failure detection and prognostics, by Raghuram Puthali Ramesh, (electronic resource)
Publication
Note
  • Vita
  • The entire thesis text is included in file
  • Title from title screen of thesis/dissertation PDF file (viewed May 16, 2012)
Bibliography note
Includes bibliographical references (pages 52-55)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
793756908
Dimensions
unknown
Extent
1 online resource (viii, 56 pages)
Form of item
electronic
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Other physical details
illustrations, digital, PDF file.
Specific material designation
remote
System control number
(OCoLC)793756908
System details
  • System requirements: Adobe Acrobat Reader; Internet browser
  • Mode of access: World Wide Web

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