An ontology for maintenance activities and its application to data quality.

Caitlin Woods, Matt Selway, Tyler Bikaun, Markus Stumptner, Melinda Hodkiewicz

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Maintenance of assets is a multi-million dollar cost each year for asset intensive organisations in the defence, manufacturing, resource and infrastructure sectors. These costs are tracked though maintenance work order (MWO) records. MWO records contain structured data for dates, costs, and asset identification and unstructured text describing the work required, for example ‘replace leaking pump’. Our focus in this paper is on data quality for maintenance activity terms in MWO records (e.g. replace, repair, adjust and inspect).

We present two contributions in this paper. First, we propose a reference ontology for maintenance activity terms. We use natural language processing to identify seven core maintenance activity terms and their synonyms from 800,000 MWOs. We provide elucidations for these seven terms. Second, we demonstrate use of the reference ontology in an application-level ontology using an industrial use case. The end-to-end NLP-ontology pipeline identifies data quality issues with 55% of the MWO records for a centrifugal pump over 8 years. For the 33% of records where a verb was not provided in the unstructured text, the ontology can infer a relevant activity class.

The selection of the maintenance activity terms is informed by the ISO 14224 and ISO 15926-4 standards and conforms to ISO/IEC 21838-2 Basic Formal Ontology (BFO). The reference and application ontologies presented here provide an example for how industrial organisations can augment their maintenance work management processes with ontological workflows to improve data quality.
Original languageEnglish
Article number2
Pages (from-to)319-352
Number of pages34
JournalSemantic Web
Volume15
Issue number2
DOIs
Publication statusPublished - 30 Apr 2024

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