Rethinking Maintenance Terminology for an Industry 4.0 Future

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Mar 24, 2021
Melinda Hodkiewicz Sarah Lukens Michael P. Brundage Thurston Sexton

Abstract

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 wider availability of data and models are 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 work is planned and executed. In this new future, maintenance will be initiated once a potential failure has been detected (using PHM) and thus completed before a functional failure has occurred. 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 do not clearly capture work arising from condition monitoring and predictive modeling investments. 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.

Abstract 1544 | PDF Downloads 8916

##plugins.themes.bootstrap3.article.details##

Keywords

maintenance, prognostics, work management, maintenance work orders

References
Birkler, J., Graser, J. C., Arena, M. V., Cook, C. R., & Lee, G. (2001). Assessing competitive strategies for the joint strike fighter: Opportunities and options (Tech. Rep.). California, USA: Rand National Defense Research Inst.
Bo¨hm, T. (2013). How precise has fault detection to be? Answers from an economical point of view. In 26th International Congress on Condition Monitoring and Diagnostics Engineering Management (comadem 2013). Helsinki, Finland.
Brundage, M.P., Sexton, T., Hodkiewicz, M., Morris, K.C., Arinez, J., Ameri, F., Ni, J. & Xiao, G. (2019). Where do we start? Guidance for technology implementation in maintenance management for manufacturing. Journal of Manufacturing Science and Engineering, 141(9).
Coetzee, J. (1997). Maintenance. Pretoria, South Africa: Trafford Publishing.
Crocker, J. (1999). Effectiveness of maintenance. Journal of Quality in Maintenance Engineering, 5, 307–313.
EFNMS. (2007). Maintenance Key Performance Indicators (Standard No. En 15341). The European Federation of National Maintenance Societies.
Gulati, R., & Smith, R. (2009). Maintenance and reliability best practices (2nd ed.). Industrial Press Inc.
Hartly, K., Brauer, P., & Dunne, J. (2004). Offsets and the Joint Strike Fighter in the UK and the Netherlands. In Arms trade and economic development: Theory, policy and cases in arms trade offsets (pp. 118–125). London: Routledge.
Hodkiewicz, M., & Ho, M. T.-W. (2016). Cleaning historical maintenance work order data for reliability analysis. Journal of Quality in Maintenance Engineering, 22(2), 146–163.
IEC. (2016). Dependability management – Maintenance and maintenance support (Standard No. AS IEC 60300.3.14). Geneva, Switzerland: International Electrotechnical Commission.
ISO. (2016). Petroleum, petrochemical and natural gas industries – Collection and exchange of reliability and maintenance data for equipment (Standard No. ISO14224:2016). Geneva, Switzerland: International Organization for Standardization.
Kelly, A. (2006). Maintenance systems and documentation. Elsevier.
Kwon, D., Hodkiewicz, M. R., Fan, J., Shibutani, T., & Pecht,
M. G. (2016). IoT-based prognostics and systems health management for industrial applications. IEEE Access, 4, 3659–3670.
Lukens, S., & Markham, M. (2018). Data-driven application of PHM to asset strategies. In Proceedings of the annual conference of the prognostics and health management society (Vol. 10).
Lukens, S., Naik, M., Saetia, K., & Hu, X. (2019). Best practices framework for improving maintenance data quality to enable asset performance analytics. In Proceedings of the Annual Conference of the PHM Society. Scottsdale, AZ.
Ma´rquez, A. C. (2007). The maintenance management framework: models and methods for complex systems maintenance. London, UK: Springer.
Molina, R., Unsworth, K., Hodkiewicz, M., & Adriasola, E. (2013). Are managerial pressure, technological control and intrinsic motivation effective in improving data quality? Reliability Engineering & System Safety, 119, 26–34.
Naik, M., & Saetia, K. (2018). Improving data quality by using best practices and cognitive analytics. In SMRP conference proceedings. Orlando, FL.
NERC. (2020). Generating Availability Data System - Data Reporting Instructions (Standard). Atlanta, GA.
SAE. (2009). Evaluation Criteria for Reliability-Centered Maintenance (RCM) Processes (Standard No. JA1011- 2009). SAE International.
Schleichert, O. P., Bringmann, B., Kremer, H., Zablotskiy, S., & Ko¨pfer, D.(2017, July).Predictive maintenance:Taking pro-active measures based on advanced data analytics to predict and avoid machine failure [White Paper].Retrieved from https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte\%5FPredictive-Maintenance\%5FPositionPaper.pdf
SMRP. (2017). Society of Maintenance and Reliability Professionals (SMRP) Best Practices 5th. Edition (Vol. 5; Standard). Atlanta, GA: Society for Maintenance and Reliability Professionals.
Unsworth, K., Adriasola, E., Johnston-Billings, A., Dmitrieva, A., & Hodkiewicz, M. (2011). Goal hierarchy: Improving asset data quality by improving motivation. Reliability Engineering & System Safety, 96(11), 1474–1481.
Unsworth, K., Yeo, G., & Beck, J. (2014). Multiple goals. Journal of Organizational Behavior, 35(8), 1064–1078.
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, New Jersey: Wiley.
Section
Technical Papers