Rethinking maintenance terminology for an Industry 4.0 future
Melinda Hodkiewicz, Sarah Lukens, Michael Brundage, Thurston Sexton
Sensors and mathematical models have been used since the 1990's to assess the health of systems and diagnose anomalous behavior. The advent of the Internet of Things (IoT) increases the range of assets on which data can be collected cost effectively. Cloud computing and the availability of data and models is democratizing the implementation of prognostic health (PHM) technologies. Together, these advancements and other Industry 4.0 developments are creating a paradigm shift in how maintenance is planned and executed. In this new future, maintenance will be initiated (using PHM) after a failure has begun, and thus corrective work is required since corrective work is defined as "work done to restore the function of an asset after failure or when failure is imminent." Many metrics for measuring the effectiveness of maintenance work management are grounded in a negative perspective of corrective work and hence may be misleading when a PHM strategy is deployed. In this paper, we use case studies to demonstrate the need to rethink maintenance terminology. The outcomes of this work include 1) definitions to be used for consistent evaluation of work management performance in an Industry 4.0 future and 2) recommendations to improve detection of work related to PHM activities.
International Journal of Prognostics and Health Management
, Lukens, S.
, Brundage, M.
and Sexton, T.
Rethinking maintenance terminology for an Industry 4.0 future, International Journal of Prognostics and Health Management, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930635
(Accessed September 21, 2021)