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Categorization Errors for Data Entry in Maintenance Work-Orders

Published

Author(s)

Thurston B. Sexton, Melinda Hodkiewicz, Michael P. Brundage

Abstract

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 through- out the maintenance-management workflow.
Proceedings Title
Proceedings of the Annual Conference of the PHM Society
Conference Dates
September 21-26, 2019
Conference Location
Scottsdale, AZ
Conference Title
Annual Conference of the Prognostics and Health Management Society 2019

Keywords

human factors, task analysis, maintenance, data, manufacturing, human error, data entry
Created September 24, 2019, Updated October 2, 2019