Categorization Errors for Data Entry in Maintenance Work-Orders
In manufacturing, there is a significant push toward the digitization
of processes and decision making, by increasing the
level of automation and networking via cyber-physical systems,
and machine learning methods that can parse useful
patterns from these complex architectures. As such, this push
toward being “smart” is largely driven by the availability of
data, for analysis, decision guidance, and the training of AI.
The maintenance team, however, one of the core subsystems
in any production line, remains a largely human endeavor.
Consequently, the historical data needed for research and development
of AI-assisted maintenance frameworks are often
full of misspellings, jargon, and abbreviations. While one
might enforce data-entry into pre-specified functional categories
(generally using some form of controlled vocabulary),
cognitive models, along with consistent reports from industry,
indicate that data entry remains a process fraught with significant
errors, especially when a mismatch occurs between designated
schemas and the technician’s needed semantic flexibility.
This paper offers a framework for understanding and
addressing these issues, with a methodological case study in
applying Human Reliability Analysis (HRA) to quantify and
understand human errors associated with entering maintenance
work-order (MWO) data into structured database (DB)
schema. We subsequently suggest potential mitigation strategies
for each to improve the quality of recorded data throughout
the maintenance-management workflow.
How to Cite
Maintenance, Human Factors, Data Collection
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.