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FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES

Failure of rotating machineries is an inevitable incident in process industries, leading to catastrophic outcomes. There is therefore the need to continuously monitor the condition of machines and identify impending faults long before disastrous breakdowns occur. Diagnosis of faults is a principl...

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Main Author: SARWAR, UMAIR
Format: Thesis
Language:English
Published: 2016
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://utpedia.utp.edu.my/id/eprint/21480/1/2015-MECHANICAL-FAULT%20DIAGNOSIS%20MODEL%20FOR%20ROTATING%20MACHINERY%20BASED%20ON%20MULTIPLE%20CONDITION%20MONITORING%20DATA%20SOURCES-UMAIR%20SARWAR.pdf
http://utpedia.utp.edu.my/id/eprint/21480/
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spelling oai:utpedia.utp.edu.my:214802024-07-25T00:32:05Z http://utpedia.utp.edu.my/id/eprint/21480/ FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES SARWAR, UMAIR TJ Mechanical engineering and machinery Failure of rotating machineries is an inevitable incident in process industries, leading to catastrophic outcomes. There is therefore the need to continuously monitor the condition of machines and identify impending faults long before disastrous breakdowns occur. Diagnosis of faults is a principle part of condition-based maintenance (CBM) and intends to detect the faults before it occur. In recent years fault diagnosis of rotating machinery has been a concern of great interest because of the increasing demand and the requirements for reliable operations. 2016-06 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/id/eprint/21480/1/2015-MECHANICAL-FAULT%20DIAGNOSIS%20MODEL%20FOR%20ROTATING%20MACHINERY%20BASED%20ON%20MULTIPLE%20CONDITION%20MONITORING%20DATA%20SOURCES-UMAIR%20SARWAR.pdf SARWAR, UMAIR (2016) FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES. Masters thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
SARWAR, UMAIR
FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES
description Failure of rotating machineries is an inevitable incident in process industries, leading to catastrophic outcomes. There is therefore the need to continuously monitor the condition of machines and identify impending faults long before disastrous breakdowns occur. Diagnosis of faults is a principle part of condition-based maintenance (CBM) and intends to detect the faults before it occur. In recent years fault diagnosis of rotating machinery has been a concern of great interest because of the increasing demand and the requirements for reliable operations.
format Thesis
author SARWAR, UMAIR
author_facet SARWAR, UMAIR
author_sort SARWAR, UMAIR
title FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES
title_short FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES
title_full FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES
title_fullStr FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES
title_full_unstemmed FAULT DIAGNOSIS MODEL FOR ROTATING MACHINERY BASED ON MULTIPLE CONDITION MONITORING DATA SOURCES
title_sort fault diagnosis model for rotating machinery based on multiple condition monitoring data sources
publishDate 2016
url http://utpedia.utp.edu.my/id/eprint/21480/1/2015-MECHANICAL-FAULT%20DIAGNOSIS%20MODEL%20FOR%20ROTATING%20MACHINERY%20BASED%20ON%20MULTIPLE%20CONDITION%20MONITORING%20DATA%20SOURCES-UMAIR%20SARWAR.pdf
http://utpedia.utp.edu.my/id/eprint/21480/
_version_ 1805891035791884288
score 13.214268

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