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Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions
Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613507643506688 |
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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 | © 2019 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 (Mechanical Engineering))--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78513 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:15.129Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| 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:2263/78513 Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions Heyns, P.S. (Philippus Stephanus) ryanbalshaw1@gmail.com Wilke, Daniel Nicolas Schmidt, Stephan Balshaw, Ryan Cameron Latent Variable Models Unsupervised Learning Latent Analysis Temporal Preservation Time-Varying Operating Conditions 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 Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020. Vibration-based condition monitoring is a key and crucial element for asset longevity and to avoid unexpected financial compromise. Currently, data-driven methodologies often require significant investments into data acquisition and a large amount of operational data for both healthy and unhealthy cases. The acquisition of unhealthy fault data is often financially infeasible and the result is that most methods detailed in literature are not suitable for critical industrial applications. In this work, unsupervised latent variable models negate the requirement for asset fault data. These models operate by learning the representation of healthy data and utilise health indicators to track deviance from this representation. A variety of latent variable models are compared, namely: Principal Component Analysis, Variational Auto-Encoders and Generative Adversarial Network-based methods. This research investigated the relationship between time-series data and latent variable model design under the sensible notion of data interpretation, the influence of model complexity on result performance on different datasets and shows that the latent manifold, when untangled and traversed in a sensible manner, is indicative of damage. Three latent health indicators are proposed in this work and utilised in conjunction with a proposed temporal preservation approach. The performance is compared over the different models. It was found that these latent health indicators can augment standard health indicators and benefit model performance. This allows one to compare the performance of different latent variable models, an approach that has not been realised in previous work as the interpretation of the latent manifold and the manifold response to anomalous instances had not been explored. If all aspects of a latent variable model are systematically investigated and compared, different models can be analysed on a consistent platform. In the model analysis step, a latent variable model is used to evaluate the available data such that the health indicators used to infer the health state of an asset, are available for analysis and comparison. The datasets investigated in this work consist of stationary and time-varying operating conditions. The objective was to determine whether deep learning is comparable or on par with state-of-the-art signal processing techniques. The results showed that damage is detectable in both the input space and the latent space and can be trended to identify clear condition deviance points. This highlights that both spaces are indicative of damage when analysed in a sensible manner. A key take away from this work is that for data that contains impulsive components that manifest naturally and not due to the presence of a fault, the anomaly detection procedure may be limited by inherent assumptions made in model formulations concerning Gaussianity. This work illustrates how the latent manifold is useful for the detection of anomalous instances, how one must consider a variety of latent-variable model types and how subtle changes to data processing can benefit model performance analysis substantially. For vibration-based condition monitoring, latent variable models offer significant improvements in fault diagnostics and reduce the requirement for expert knowledge. This can ultimately improve asset longevity and the investment required from businesses in asset maintenance. Eskom Power Plant Engineering Institute (EPPEI) UP Postgraduate Bursary mi2025 Mechanical and Aeronautical Engineering MEng (Mechanical Engineering) Unrestricted SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production 2021-02-12T09:52:26Z 2021-02-12T09:52:26Z 2021-04 2020 Dissertation * http://hdl.handle.net/2263/78513 en © 2019 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 | Latent Variable Models Unsupervised Learning Latent Analysis Temporal Preservation Time-Varying Operating Conditions 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 Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions |
| title | Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions |
| title_full | Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions |
| title_fullStr | Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions |
| title_full_unstemmed | Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions |
| title_short | Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions |
| title_sort | latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time varying operating conditions |
| topic | Latent Variable Models Unsupervised Learning Latent Analysis Temporal Preservation Time-Varying Operating Conditions 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 |
| url | http://hdl.handle.net/2263/78513 |