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Degradation estimation of high energy steam piping using hybrid recurrent neural networks
Dissertation (MEng)--University of Pretoria, 2018.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2018
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| _version_ | 1867613623767007232 |
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| 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, 2018. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/66253 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:05.758Z |
| 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:2263/66253 Degradation estimation of high energy steam piping using hybrid recurrent neural networks Heyns, P.S. (Philippus Stephanus) u28407840@tuks.co.za Hindley, Michael Philip Van Niekerk, Johannes Lodewikus UCTD High energy steam pipework Creep degradation estimation Power plant Machine learning Pipe elevation survey Engineering, built environment and information technology theses SDG-07 SDG-07: Affordable and clean energy 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 Dissertation (MEng)--University of Pretoria, 2018. This dissertation is a study on estimating degradation of high energy steam pipework using modern machine learning techniques. High energy piping systems are very complex to simulate due to the many variables that could in uence the useful life of a component. In this research a hybrid recurrent neural network is created that consists of a combined recurrent neural network and a feed forward neural network. The machine learning model is trained on historical data that has been captured over a six-year time period and is applied to a test dataset to see if any usable patterns exist within the training data. In this research the following variables of the piping system components are used as input to the machine learning model: the operating temperature and pressure time sequence, the distance to the closest anchor point, the distances to neighbouring supports as well as their elevation survey readings and the last known creep damage of the component. The model is created in Python using the Tensor ow library. Two types of recurrent neural networks (RNN) are tested, gated recurrent unit (GRU) and long short term memory (LSTM). The standard gradient descent (GD) algorithm, as well as adaptive gradient descent (ADAGRAD) and adaptive movement estimation (ADAM) are tested. The model was able to predict the classi cation of a component with an accuracy of up to 91% on the training dataset and 56% on the test data set, which is considered to be high given the complexity of the problem. The model is successful in recognising patterns within the data and o ers an automated way to parse large data sets that consist of a temporal and static data mixture. This o ers an approach to make an objective decision on similar complex data driven problems and its application is not constrained to this single problem. The methods applied in this research is expected to perform even better on problems where the frequency of data collection is higher than what is used in this research. mi2025 Mechanical and Aeronautical Engineering MEng Unrestricted SDG-07: Affordable and clean energy SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production 2018-08-17T09:42:50Z 2018-08-17T09:42:50Z 2005/03/18 2018 Dissertation Van Niekerk, JL 2018, Degradation estimation of high energy steam piping using hybrid recurrent neural networks, MEng Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/66253> A2018 http://hdl.handle.net/2263/66253 en © 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 | UCTD High energy steam pipework Creep degradation estimation Power plant Machine learning Pipe elevation survey Engineering, built environment and information technology theses SDG-07 SDG-07: Affordable and clean energy 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 Degradation estimation of high energy steam piping using hybrid recurrent neural networks |
| title | Degradation estimation of high energy steam piping using hybrid recurrent neural networks |
| title_full | Degradation estimation of high energy steam piping using hybrid recurrent neural networks |
| title_fullStr | Degradation estimation of high energy steam piping using hybrid recurrent neural networks |
| title_full_unstemmed | Degradation estimation of high energy steam piping using hybrid recurrent neural networks |
| title_short | Degradation estimation of high energy steam piping using hybrid recurrent neural networks |
| title_sort | degradation estimation of high energy steam piping using hybrid recurrent neural networks |
| topic | UCTD High energy steam pipework Creep degradation estimation Power plant Machine learning Pipe elevation survey Engineering, built environment and information technology theses SDG-07 SDG-07: Affordable and clean energy 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 |
| url | http://hdl.handle.net/2263/66253 |