Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop

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

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

Published Oct 18, 2015
Brian A. Weiss Gregory Vogl Moneer Helu Guixiu Qiao Joan Pellegrino Mauricio Justiniano Anand Raghunathan

Abstract

The National Institute of Standards and Technology (NIST) hosted the Roadmapping Workshop – Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) in Fall 2014 to discuss the needs and priorities of stakeholders in the PHM4SMS technology area. The workshop brought together over 70 members of the PHM community. The attendees included representatives from small, medium, and large manufacturers; technology developers and integrators; academic researchers; government organizations; trade associations; and standards bodies. The attendees discussed the current and anticipated measurement science challenges to advance PHM methods and techniques for smart manufacturing systems; the associated research and development needed to implement condition monitoring, diagnostic, and prognostic technologies within manufacturing environments; and the priorities to meet the needs of PHM in manufacturing.

This paper will summarize the key findings of this workshop, and present some of the critical measurement science challenges and corresponding roadmaps, i.e., suggested courses of action, to advance PHM for manufacturing. Milestones and targeted capabilities will be presented for each roadmap across three areas: PHM Manufacturing Process Techniques; PHM Performance Assessment; and PHM Infrastructure – Hardware, Software, and Integration. An analysis of these roadmaps and crosscutting themes seen across the breakout sessions is also discussed.

How to Cite

A. Weiss, B. ., Vogl, G., Helu, M., Qiao, G., Pellegrino, J., Justiniano, M., & Raghunathan, A. (2015). Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2712
Abstract 121 | PDF Downloads 208

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

Keywords

prognostics, diagnostics, performance evaluation, Condition Based Monitoring, smart manufacturing, measurement science

References
Ahmad, R. & Kamaruddin, S. (2012). An overview of time- based and condition-based maintenance in industrial application. Computers & Industrial Engineering, vol. 63(1), pp. 135-149.
Barajas, L. G. & Srinivasa, N. (2008). Real-time diagnostics, prognostics health management for large-scale manufacturing maintenance systems. ASME International Manufacturing Science and Engineering Conference, MSEC2008 (pp. 85-94), Evanston, IL, United States. doi: 10.1115/MSEC_ICMP2008-72511
Bernaden, J. (2012). Indirect jobs: A direct way to talk about why we need smart manufacturing. Rockwell Automation.
Butcher, S. W. (2000). Assessment of condition-based maintenance in the Department of Defense. Logistics Management Institute, McLean, Virginia.
Byington, C. S., Roemer, M. J., Kacprzymki, G. J., & Galie, T. (2002). Prognostic enhancements to diagnostic systems for improved condition-based maintenance. 2002 IEEE Aerospace Conference (pp. 2815-2824), Big Sky, MT, United States. doi: 10.1109/AERO.2002.1036120
Coats, D., Hassan, M. A., Goodman, N., Blechertas, V., Shin, Y.-J., & Bayoumi, A. (2011). Design of advanced time-frequency mutual information measures for aerospace diagnostics and prognostics. 2011 IEEE Aerospace Conference, AERO 2011, Big Sky, MT, United States. doi: 10.1109/AERO.2011.5747575
Kothamasu, R., Huang, S. H., & VerDuin, W. H. (2006). System health monitoring and prognostics - a review of current paradigms and practices. The International Journal of Advanced Manufacturing Technology, vol. 28, pp. 1012-1024.
Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., & Liao, H. (2006). Intelligent prognostics tools and e-maintenance. Computers in industry, vol. 57(6), pp. 476-489.
Lee, J., Ghaffari, M., & Elmeligy, S. (2011). Self- maintenance and engineering immune systems: Towards smarter machines and manufacturing systems. Annual Reviews in Control, vol. 35(1), pp. 111-122. doi: 10.1016/j.arcontrol.2011.03.007
Manyika, J., Sinclair, J., Dobbs, R., Strube, G., Rassey, L., Mischke, J., Remes, J., Roxburgh, C., George, K., O'Halloran, D., & Ramaswamy, S. (2012). Manufacturing the future: The next era of global growth and innovation: McKinsey Global Institute.
Montgomery, N., Banjevic, D., & Jardine, A. K. S. (2012). Minor maintenance actions and their impact on diagnostic and prognostic CBM models. Journal of Intelligent Manufacturing, vol. 23(2), pp. 303-311. doi: 10.1007/s10845-009-0352-0
Muller, A., Crespo Marquez, A., & Iung, B. (2008). On the concept of e-maintenance: Review and current research. Reliability Engineering & System Safety, vol. 93(8), pp. 1165-1187.
National Institute of Standards and Technology (2015a).
Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems:http://www.nist.gov/el/isd/upload/Measurement- Science-Roadmapping-Workshop-Final-Report.pdf

National Institute of Standards and Technology (2015b). Roadmapping Workshop on Measurement Science for Prognostics and Health Management of Smart Manufacturing Systems Agenda: http://www.nist.gov/el/isd/phm4sms-workshop- agenda.cfm
(2012). Report to the President: Capturing Domestic Competitive Advantage in Advanced Manufacturing. Executive Office of the President - President's Council of Advisors on Science and Technology.
PCAST (2014). Report to the President: Accelerating U.S. Advanced Manufacturing. Executive Office of the President - President's Council of Advisors on Science and Technology.
Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: A review. The International Journal of Advanced Manufacturing Technology, vol. 50(1- 4), pp. 297-313.
Tian, Z., Lin, D., & Wu, B. (2012). Condition based maintenance optimization considering multiple objectives. Journal of Intelligent Manufacturing, vol. 23(2), pp. 333-340. doi: 10.1007/s10845-009- 0358-7
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014a).Standards related to prognostics and health management (PHM) for manufacturing. National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA, NISTIR 8012. doi: 10.6028/NIST.IR.8012
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014b). Standards for prognostics and health management (PHM) techniques within manufacturing operations. Annual Conference of the Prognostics and Health Management Society 2014, Fort Worth, Texas, USA.
Zhou, Y., Bo, J., & Wei, T. (2013). A review of current prognostics and health management system related standards. Chemical Engineering Transactions, vol. 33, pp. 277-282. doi: 10.3303/CET1333047
Section
Technical Research Papers