Machines with moving parts give rise to vibrations and consequently noise. The setting up and
the status of each machine yield to a peculiar vibration signature. Therefore, a change in the
vibration signature, due to a change in the machine state, can be used to detect incipient
defects before they become critical. This is the goal of condition monitoring, in which the
informations obtained from a machine signature are used in order to detect faults at an early
stage. There are a large number of signal processing techniques that can be used in order to
extract interesting information from a measured vibration signal.
This study seeks to detect rotating machine defects using a range of techniques including
synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet
transform, Wigner-Ville distribution and spectral correlation density function. The detection
and the diagnostic capability of these techniques are discussed and compared on the basis of
experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth
spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity
to fault severity is assessed by the application of these signal processing techniques to gear
tooth faults of different sizes.
| Identifer | oai:union.ndltd.org:unibo.it/oai:amsdottorato.cib.unibo.it:952 |
| Date | 17 April 2008 |
| Creators | D'Elia, Gianluca <1980> |
| Contributors | Dalpiaz, Giorgio |
| Publisher | Alma Mater Studiorum - Università di Bologna |
| Source Sets | Università di Bologna |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, PeerReviewed |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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