<p> Vibration Analysis (VA) is a key technique used for maintenance and fault detection of vibrating machinery. The purpose of maintenance is to analyze how well the machinery is operating within its target parameters, while fault detection is done to diagnose and locate a fault that might be developing on the machinery.</p> <p> If we consider s(n) to be the true signal from a rotating system and e(n) to be the additive noise corrupting the signal, then the observed signal is x(n) = s(n) + e(n). If s(n) is composed of a main driving frequency sm(n) and summed fault frequencies sf(n), then fault detection is the study of sf(n). In fault detection, we eliminate e(n) as much as possible so that sf(n) can be isolated and studied.</p> <p> This thesis presents a technique based on cross-correlation, utilizing a network
of sensors, to eliminate e(n) from the measurements, preserving just the correlated
frequency content. This is extended to provide a means of localizing the source of
the frequency content, based on the relative strengths of the members of the complete
set of cross-correlations between all sensors. This technique has been shown to be able to extract a signal buried by noise, in situations where the traditional FFT fails.</p> <p> To enable this, a new VA system has been developed. This introduces new wireless vibration sensors as well as a data capture unit capable of providing real-time VA data to technicians. The system can simultaneously capture data from eight sensors, so the data can be used not only for traditional VA techniques, but also in conjunction with the cross-correlation technique described above. This system is now commercially available and in use by dozens of technicians around the world. </p> / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/19045 |
| Date | January 2010 |
| Creators | Parlar, Jay |
| Contributors | von Mohrenschildt, Martin, Software Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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