Slow speed rotating machines are the mainstay of several industrial
applications worldwide. They can be found in paper and steel mills,
rotating biological contractors, wind turbines etc. Operational experience of
such machinery has not only revealed the early design problems but has also
presented opportunities for further significant improvements in the technology
and economics of the machines. Slow speed rotating machinery maintenance,
mostly related to bearings, shafts and gearbox problems, represents the cause
of extended outages. Rotating machinery components such as gearboxes,
shafts and bearings degrade slowly with operating time. Such a slow
degradation process can be identified if a robust on-line monitoring and
predictive maintenance technology is used to detect impending problems and
allow repairs to be scheduled. To keep machines functioning at optimal levels,
failure detection of such vital components is important as any mechanical
degradation or wear, if is not impeded in time, will often progress to more
serious damage affecting the operational performance of the machine. This
requires far more costly repairs than simply replacing a part.
Over the last few years there have been many developments in the use of
Acoustic Emission (AE) technology and its analysis for monitoring the condition
of rotating machinery whilst in operation, particularly on slow speed rotating
machinery. Unlike conventional technologies such as thermography, oil
analysis, strain measurements and vibration, AE has been introduced due to its
increased sensitivity in detecting the earliest stages of loss of mechanical
integrity.
This programme of research involves laboratory tests for monitoring slow speed
rotating machinery components (shafts and bearings) using AE technology. To
implement this objective, two test rigs have been designed to assess the
capability of AE as an effective tool for detection of incipient defects within low
speed machine components (e.g. shafts and bearings). The focus of the
experimental work will be on the initiation and growth of natural defects. Further,
this research work investigates the source characterizations of AE signals
associated with such bearings whilst in operation. It is also hoped that at the
end of this research program, a reliable on-line monitoring scheme used for
slow speed rotating machinery components can be developed.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/4568 |
| Date | 06 1900 |
| Creators | Elforjani, Mohamed Ali |
| Contributors | Mba, David |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University 2010. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner |
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