An ontology for maintenance activities and its application to data quality

Tracking #: 3299-4513

Melinda Hodkiewicz
Caitlin Woods1
Matt Selway
Tyler Bikaun
Markus Stumptner

Responsible editor: 
Guest Editors SW for Industrial Engineering 2022

Submission type: 
Full Paper
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.
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Review #1
Anonymous submitted on 29/Nov/2022
Review Comment:

The authors have answered to my last small concerns. Thank you for the huge work. I think that you have a very nice paper here

Review #2
By Stefano Borgo submitted on 04/Dec/2022
Review Comment:

The paper has been further improved, I appreciate it.

Regarding "With respect to the reviewer we cannot find ‘typos’ in Table 5."
Here they are:
1. The abbreviation "D& E" has an extra space (D&E is used in the table)
2. The abbreviation "PT - power transmission" is never used in the table
3. Line 29: oil is classified LU which is not among the abbreviations
4. Line 34: pump is classified PI which is not among the abbreviations

I confirm that I am satisfied with the paper in this form.
Thank you for the good work done.

Review #3
Anonymous submitted on 01/Feb/2023
Review Comment:

This manuscript was submitted as 'full paper' and should be reviewed along the usual dimensions for research contributions which include (1) originality, (2) significance of the results, and (3) quality of writing. Please also assess the data file provided by the authors under “Long-term stable URL for resources”. In particular, assess (A) whether the data file is well organized and in particular contains a README file which makes it easy for you to assess the data, (B) whether the provided resources appear to be complete for replication of experiments, and if not, why, (C) whether the chosen repository, if it is not GitHub, Figshare or Zenodo, is appropriate for long-term repository discoverability, and (4) whether the provided data artifacts are complete. Please refer to the reviewer instructions and the FAQ for further information.