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Computational aspects of on-line machine monitoring.

Milne, Alan James

Authors

Alan James Milne



Contributors

N.D. Deans
Supervisor

W.T. Thomson
Supervisor

R. Leonard
Supervisor

Abstract

The detection of the fault phenomena found on three phase induction motors, as widely used in industry, from an electrical engineering viewpoint has been investigated. Various transducers are used, on a real motor, to establish connections between known fault and measurable changes in the signals from these transducers. It is shown that these signals may be processed in a very powerful, yet conceptually simple way, to allow various fault conditions to be diagnosed. A fault condition may be due to one or more individual faults. In this thesis, the development of a general purpose, user friendly minicomputer based system to carry out fault detection is reported. The establishment of such a system is necessary to provide a framework within which to develop signal processing regimes which are suited to the task of condition monitoring. Having established the desired links between the controlled faults and measurable changes in the signals from the various transducers used, and the signal processing needed to detect these changes, a dedicated microprocessor implementation has been devised. The results of this research are directly applicable to industry, and would allow considerable savings on maintenance costs. This is due to the continuous indication of the working state of the machine, so that maintenance may be planned in advance, thus saving on costly downtimes. It will also save time and costs on regular maintenance strategies, as it allows for a motor to be left in service, with complete confidence in its performance, without the need to withdraw it from service and examine it at regular, but possibly otherwise unnecessary intervals.

Citation

MILNE, A.J. 1984. Computational aspects of on-line machine monitoring. Robert Gordon's Institute of Technology, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1993208

Thesis Type Thesis
Deposit Date Sep 25, 2024
Publicly Available Date Sep 25, 2024
DOI https://doi.org/10.48526/rgu-wt-1993208
Public URL https://rgu-repository.worktribe.com/output/1993208
Award Date Aug 31, 1984

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