NLP for Enterprise Asset Management: An Emerging Paradigm

• Published in: 2023 27th International Conference Information Visualisation (IV) pp. 238 - 243
• Main Authors: Santos, Pedro, Datia, Nuno, Pato, Matilde, Sobral, Jose, Gomes, Nuno, Leitao, Noel, Ferreira, Manuel R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.07.2023
• Subjects: Annotations; Asset Management; Automation; Force; Human in the loop; Industry 4.0; Interactive interface; KB curation; Maintenance engineering; Manuals; Natural language processing; NLP
• ISSN: 2375-0138
• DOI: 10.1109/IV60283.2023.00049

In the field of asset management, a Work Order refers to a document that outlines the necessary steps to carry out a maintenance operation on a specific physical asset. The text on this Work orders providing details about the problem and the actions required are open-ended, not normalized, and Technician' dependant, presenting challenges for automating asset management Work Order processing. To address the issue of automating the analysis of Work Orders, Natural Language Processing techniques are employed to process the content of these documents. The aim is to identify and extract relevant information related to actions and components within the sentences. This paper presents the Reliability Centred Maintenance for Assets solution, which utilizes a semi-automatic, human-in-the-loop approach to determine a standardised and condensed set of actions and components. The results indicate a significant increase in the number of annotations, reaching a ratio of 1:14. By implementing this solution, the manual workload associated with analysing Work Orders can be reduced, thereby improving decision support and analytical processing of the data contained within these documents.