Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.484807
Title: Predicting malfunction in quasi steady state rotating machines
Author: Kitsos, Christos
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2007
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Abstract:
Dry vacuum pumps appeared in the mid-1980s in order to address problems caused by conventional fluid-sealed pumps. However, their working environment is often harsh, sometimes resulting in catastrophic faults. Continuously monitoring the state of the system and scheduling maintenance as appropriate is thus desirable.;Sliding mode techniques have been widely used in condition monitoring and fault detection schemes in recent years. Their main advantage is a fundamental robustness against certain kinds of parameter variations. They also enable faults and/or values of un-measurable system parameters to be reconstructed.;The principal aim of this thesis is to apply sliding mode techniques in order to reduce the occurrence of unplanned pump stoppages, by monitoring appropriate subsystems and parameters, for the onset of cooling system failure, bearing failure and exhaust blockage. This is achieved using the concept of the equivalent injection signal that is necessary to maintain a sliding motion. Experimental test results acquired from the dry vacuum pump test-bed illustrate the usefulness of the approach for condition monitoring. Further, the method is cost effective since it requires only low cost temperature transducers and an exhaust pressure sensor that is already part of the typical sensor package for some pump processes.;The thesis concludes with ideas and recommendations regarding possible future work, including the application of fault classification techniques and the development of processes for generating an efficient and implementable code, suitable for the vacuum pumps' embedded control systems.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.484807  DOI: Not available
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