Condition monitoring of critical machinery has many economic benefits. The primary
objective is to detect faults, for example on rolling element bearings, at an early stage to
take corrective action prior to the catastrophic failure of a component. In this context, it is
important to be able to discriminate between stable and deteriorating fault conditions. A
number of conventional vibration analysis techniques exist by which certain faults in
rotating machinery may be identified. However, under circumstances involving multiple
fault conditions conventional condition monitoring techniques may fail, e.g. by indicating
deteriorating fault conditions for stable fault situations or vice versa. Condition monitoring
of rotating machinery that may have multiple, possibly simultaneous, fault conditions is
investigated in this thesis. Different combinations of interacting fault conditions are
studied both through experimental methods and simulated models. Novel signal
processing techniques (such as cepstral analysis and equidistant Fourier transforms) and
pattern recognition techniques (based on the nearest neighbour algorithm) are applied to
vibration problems of this nature. A set of signal processing and pattern recognition
techniques is developed for the detection of small incipient mechanical faults in the
presence of noise and dynamic load (imbalance). In the case investigated the dynamic
loading consisted of varying degrees of imbalance. It is demonstrated that the proposed
techniques may be applied successfully to the detection of multiple fault conditions. / Thesis (Ph.D. (Electronical Engineering))--North-West University, Potchefstroom Campus, 2004.
| Identifer | oai:union.ndltd.org:NWUBOLOKA1/oai:dspace.nwu.ac.za:10394/68 |
| Date | January 2003 |
| Creators | Van der Merwe, Nicolaas Theodor |
| Publisher | North-West University |
| Source Sets | North-West University |
| Detected Language | English |
| Type | Thesis |
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