Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage
Dissertation (MEng)--University of Pretoria, 2017.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Published: |
University of Pretoria
2018
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613493526528000 |
|---|---|
| access_status_str | Open Access |
| author2 | Heyns, P.S. (Philippus Stephanus) |
| author_browse | Heyns, P.S. (Philippus Stephanus) |
| author_facet | Heyns, P.S. (Philippus Stephanus) |
| collection | Thesis |
| dc_rights_str_mv | © 2018 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, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/64043 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:01.683Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| 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:2263/64043 A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage Heyns, P.S. (Philippus Stephanus) dutor06@gmail.com Diamond, D.H. (David) Du Toit, Ronald Blade Tip Timing Condition Based Monitoring Predictive Maintenance UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Engineering, built environment and information technology theses SDG-13 SDG-13: Climate action Dissertation (MEng)--University of Pretoria, 2017. Blade Tip Timing (BTT) has been in existence for many decades as an attractive vibration based condition monitoring technique for turbomachine blades. The technique is non-intrusive and online monitoring is possible. For these reasons, BTT may be regarded as a feasible technique to track the condition of turbomachine blades, thus preventing unexpected and catastrophic failures. The processing of BTT data to find the associated vibration characteristics is however non-trivial. In addition, these vibration characteristics are difficult to validate, therefore resulting in great uncertainty of the reliability of BTT techniques. This article therefore proposes a hybrid approach comprising a stochastic Finite Element Model (FEM) based modal analysis and Bayesian Linear Regression (BLR) based BTT technique. The use of this stochastic hybrid approach is demonstrated for the identification and classification of turbomachine blade damage. For the purposes of this demonstration, discrete damage is incrementally introduced to a simplified test blade of an experimental rotor setup. The damage identification and classification processes are further used to determine whether a damage threshold has been reached, therefore providing sufficient evidence to schedule a turbomachine outage. It is shown that the proposed stochastic hybrid approach may offer many short- and long-term benefits for practical implementation. Eskom Power Plant Engineering Institute (EPPEI) mi2025 Mechanical and Aeronautical Engineering MEng (Mechanical Engineering) Unrestricted SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production SDG-13: Climate action 2018-02-22T10:12:27Z 2018-02-22T10:12:27Z 2018-05-03 2017-11 Dissertation Du Toit, R 2017, A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage, MEng (Mechanical Engineering) Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/64043> http://hdl.handle.net/2263/64043 © 2018 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 | Blade Tip Timing Condition Based Monitoring Predictive Maintenance UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Engineering, built environment and information technology theses SDG-13 SDG-13: Climate action A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage |
| title | A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage |
| title_full | A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage |
| title_fullStr | A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage |
| title_full_unstemmed | A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage |
| title_short | A Stochastic Hybrid Blade Tip Timing Approach for the Identification and Classification of Turbomachine Blade Damage |
| title_sort | stochastic hybrid blade tip timing approach for the identification and classification of turbomachine blade damage |
| topic | Blade Tip Timing Condition Based Monitoring Predictive Maintenance UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Engineering, built environment and information technology theses SDG-13 SDG-13: Climate action |
| url | http://hdl.handle.net/2263/64043 |