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Low cost condition monitoring under time-varying operating conditions
Dissertation (MEng)--University of Pretoria, 2013.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2014
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| _version_ | 1867613712232218624 |
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| access_status_str | Open Access |
| author2 | De Villiers, Johan Pieter |
| author_browse | De Villiers, Johan Pieter |
| author_facet | De Villiers, Johan Pieter |
| collection | Thesis |
| dc_rights_str_mv | © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng)--University of Pretoria, 2013. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/33371 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:30.132Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/33371 Low cost condition monitoring under time-varying operating conditions De Villiers, Johan Pieter theoheyns@gmail.com Heyns, Theo Condition monitoring Time-varying operating conditions Discrepancy analysis Waveform reconstruction UCTD Dissertation (MEng)--University of Pretoria, 2013. Advances in machine condition monitoring technologies are driven by the rise in complexity of modern machines and the increased demand for product reliability. Condition monitoring research tends to focus on the development of signal processing algorithms that are sensitive to machine faults, robust under time-varying operating conditions, and informative regarding the nature and extent of machine faults. A significant challenge remains for monitoring the condition of machines that are subject to time-varying operating conditions. The here presented work is concerned with the development of cost effective condition monitoring algorithms. It is investigated how empirical models (including probability density distributions and regression functions) may be used to extract diagnostic information from machine response signals that have been generated under fluctuating operating conditions. The proposed methodology is investigated on a number of case studies, including gearboxes, alternator end windings, and haul roads. It is shown how empirical models for machine condition monitoring may generally be implemented according to one of two basic approaches. The two approaches are referred to as discrepancy analysis and waveform reconstruction. Discrepancy analysis is concerned with the comparison of a novel signal to a reference model. The reference model is sufficiently expressive to represent vibration response as measured on a healthy machine over a range of operating conditions. The novel signal is compared to the reference model in such a manner that a discrepancy signal transform is obtained. A discrepancy signal is sensitive to faults, robust to time-varying operating conditions, and inherently simple. As such it may further beWaveform reconstruction implements a regression function to model machine response as a function of different state space variables. The regression function may subsequently be exploited to extract diagnostic information. The machine response may for instance be reconstructed at a specified steady state operating condition. This renders the signal wide-sense stationary so that Fourier analysis may be applied. analysed in order to extract periodicities and magnitudes as diagnostic markers. gm2014 Electrical, Electronic and Computer Engineering unrestricted 2014-02-11T05:15:06Z 2014-02-11T05:15:06Z 2013-09-04 2013 Dissertation Heyns, T 2013, Low cost condition monitoring under time-varying operating conditions, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/33371> E13/9/1047/gm http://hdl.handle.net/2263/33371 en © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Condition monitoring Time-varying operating conditions Discrepancy analysis Waveform reconstruction UCTD Low cost condition monitoring under time-varying operating conditions |
| title | Low cost condition monitoring under time-varying operating conditions |
| title_full | Low cost condition monitoring under time-varying operating conditions |
| title_fullStr | Low cost condition monitoring under time-varying operating conditions |
| title_full_unstemmed | Low cost condition monitoring under time-varying operating conditions |
| title_short | Low cost condition monitoring under time-varying operating conditions |
| title_sort | low cost condition monitoring under time varying operating conditions |
| topic | Condition monitoring Time-varying operating conditions Discrepancy analysis Waveform reconstruction UCTD |
| url | http://hdl.handle.net/2263/33371 |