Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911792559554560 |
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| author | Pronk, Morgen |
| author_facet | Pronk, Morgen |
| contents | In the evolving field of maintenance and reliability engineering, the organization of equipment into hierarchical structures presents both a challenge and a necessity, directly impacting the operational integrity of industrial facilities. This paper introduces an innovative approach employing machine learning, specifically Long Short-Term Memory (LSTM) models, to automate and enhance the creation and management of these hierarchies. By adapting techniques commonly used in natural language processing, the study explores the potential of LSTM models to interpret and predict relationships within equipment tags, offering a novel perspective on understanding facility design. This methodology involved character-wise tokenization of asset tags from approximately 29,000 entries across 50 upstream oil and gas facilities, followed by modeling these sequences using an LSTM-based recurrent neural network. The model's architecture capitalizes on LSTM's ability to learn long-term dependencies, facilitating the prediction of hierarchical relationships and contextual understanding of equipment tags. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities Pronk, Morgen Computational Engineering, Finance, and Science In the evolving field of maintenance and reliability engineering, the organization of equipment into hierarchical structures presents both a challenge and a necessity, directly impacting the operational integrity of industrial facilities. This paper introduces an innovative approach employing machine learning, specifically Long Short-Term Memory (LSTM) models, to automate and enhance the creation and management of these hierarchies. By adapting techniques commonly used in natural language processing, the study explores the potential of LSTM models to interpret and predict relationships within equipment tags, offering a novel perspective on understanding facility design. This methodology involved character-wise tokenization of asset tags from approximately 29,000 entries across 50 upstream oil and gas facilities, followed by modeling these sequences using an LSTM-based recurrent neural network. The model's architecture capitalizes on LSTM's ability to learn long-term dependencies, facilitating the prediction of hierarchical relationships and contextual understanding of equipment tags. |
| title | Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2403.06103 |