  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.
