A Reference Stack for PHM Architectures



Charles Crabb


This paper suggests a reference model for PHM processes that aids the customer of PHM in developing a business case for adopting PHM in his or her supply chain. Various PHM systems have been envisioned and developed in order to produce a prognosis of system or component behavior by collecting physical data from some section of a system, analyzing it and reporting the results to the entity that benefits from it, notably the supply chain that manages the components and receives the resulting cost benefit from PHM. All these systems have varying configurations that involve the collection of different types of data in different ways, the analysis of varying types of physical behavior and have different types of customers (different supply chain configurations). The customer needs to include the cost and complexity of the PHM system in his or her business model but has no formal standard to determine bounds on the complexity of the PHM system. Just as there are reference stacks for service-oriented architectures, this paper proposes a functional stack for PHM that can become a reference architecture for developing or purchasing a PHM system for an organization. The stack of PHM services ranges from the data acquisition layer through analysis functions to supply chain decision support services.

How to Cite

Crabb, C. . (2014). A Reference Stack for PHM Architectures. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2442
Abstract 66 | PDF Downloads 133



distributed PHM, Asset health management, PHM, Business case, Cost Benefit Analysis, life cycle cost, architecture

Banks, J. & Merenich, J.(2007) cost-benefits analysis for asset health management technology. Reliability and Maintainability Symposium, 2007. RAMS '07. Annual Conference

Banks, J.C., Reichard, K.M., Hines, J.A. & Brought, M.S. (2008). Platform degrader analysis for the design and development of Vehicle Health Management Systems. International Conference on Prognostics and Health Management, 2008.

Begin, M. P., (2012). Systems engineering processes for the acquisition of prognostic and health management systems. Masters thesis. Naval Postgraduate School, Monterrey, CA., http://www.nps.edu/

Bernstein, A., Hauske, S. & Hermann, M. (2014). Decision support systems II, http://www.elml.uzh.ch/preview/fois/DSSII/en/html/ind ex.html Univerisity of Zurich

Beyer, B., Hess, A. & Fila, L. (2001). Writing a convincing cost benefit analysis to substantiate autonomic logistics. Aerospace Conference, 2001, Big Sky, MT

Butcher, S. (2000). Assessment of condition-based maintenance in the department of defense. Logistics Management Institute

Byington, C. S., Roemer, M. J. & Galie, T. (2002). Prognostic enhancements to diagnostic systems for improved condition-based maintenance. Aerospace Conference Proceedings, 2002. Big Sky, MT

CDF User's Guide (2012). CDF User's Guide Version 3.4, February 28, 2012. Space Physics Data Facility NASA / Goddard Space Flight Center

Chappell, D. (2004). Enterprise Service Bus. O'Reilly Media, Inc. ©2004

CJCSI6212.01F (2012). Net Ready Key Performance Parameter (NR KPP). United States Department of Defense Chairman of the Joint Chiefs of Staff

DISN (2014). DISN Connection Process Guide, Cross Domain Solutions, http://www.disa.mil/Services/Network- Services/Enterprise-Connections/Connection-Process- Guide/Service-Appendices/CDS. Defense Information Services Agency

DoDI 8510.01 (2007). Information Assurance Certification and Accreditation Process (DIACAP). ASD(NII)/DoD CIO

Emmanouilidis, C., Fumagalli, L., Jantunen, E., Pistofidis, P., Macchi, M. & Garetti, M. (2010). Condition monitoring based on incremental learning and domain ontology for condition-based maintenance. Proceedings of APMS 2010 International Conference on Advances in Production Management Systems, Cernobbio, Como, Italy, 11-13.10.2010

Feldman, K., Jazouli, T. & Sandborn, P. (2009). A methodology for determining the return on investment associated with prognostics and health management. IEEE Trans. on Reliability, Vol. 58, (No. 2), pp. 305- 316.

Hall, C. L., Leary, S., Lapierre, L., Hess, A. & Bladen, K. (2001). F/A-18E/F F414 advanced inflight engine condition monitoring system (IECMS). Aerospace Conference, 2001, Big Sky, MT

Hess, A. (2002). Prognostics and Health Management: The Cornerstone of Autonomic Logistics, Joint Strike Fighter Program Office PHM Development
ISO 13374-3:2012 (2012). Condition monitoring and diagnostics of machines -- Data processing, communication and presentation. International Standards Organization

Keller, K., Wiegand, D., Swearingen, K., Reisig, C., Black, S., Gillis, A. & Vandernoot, M. (2001). An architecture to implement integrated vehicle health management systems. AUTOTESTCON Proceedings, 2001. IEEE Systems Readiness Technology Conference

Kent, R. M., & Murphy D A. (2000). Health monitoring system technology assessments - cost benefits analysis. NASA / CR-2000-209848 National Aeronautics
and Space Administration Langley Research Center McCollom, N. N. & Brown, E. R. (2011). PHM on the F- 35 fighter. IEEE Conference on Prognostics and
Health Management (PHM), 2011

MIMOSA (2009). Common Relational Information Schema

(CRIS) Version 3.2.2 Specification, Production Release, December 31. Machinery Information Management Open Systems Alliance

NIST 800-18 (2006). NIST Special Publication 800-18 Revision 1 Guide for Developing Security Plans for Federal Information Systems. Computer Security Division Information Technology Laboratory National Institute of Standards and Technology

OASIS (2004). Web Services Security: SOAP Message Security 1.1 (WS-Security 2004). OASIS Open 2002- 2006

OSD(ATL) (2010). Information on Conducting Business Case Analyses For Condition Based Maintenance Plus (CBM+) Initiative. Report of the Office of the Secretary of Defense
CBM+ Action Group 2010 Summer Study

Roemer, M. J., Byington, C. S., Kacprzynski, G. J., & Vachtsevanos, G. (2006). An overview of selected prognostic technologies with reference to an integrated phm architecture. Proceedings of GT2006 ASME Turbo Expo 2006: Power for Land, Sea, and Air May 8-11, 2006, Barcelona, Spain

Saha,B, Goebel,K., Poll, S. & Christophersen, J. (2007). A bayesian framework for remaining useful life estimation. Association for the Advancement of Artificial Intelligence

Saha, B., Saha, S. and Goebe, K. (2009). A distributed prognostic health management architecture. Proceedings of the Society for Society for Machinery Failure Prevention Technology

Sandborn, P. A, Wilkinson, C. (2007). A maintenance planning and business case development model for the application of prognostics and health management (phm) to electronic system. Microelectronics Reliability, Vol. 47, (No. 12), 1889-1901

Sankararaman, S. & Goebel, K. (2013). Why is the remaining useful life prediction uncertain? Annual Conference of the Prognostics and Health Management Society 2013

Tang, L., Kacprzynski, G., Goebel, K., & Vachtsevanos, G. (2009). Methodologies for uncertainty management in prognostics. Aerospace Conference, 2009, Big Sky, MT

Tsoutis, A. (2003). Simulation of the I3 to D Repair Process and Sparing of the F414-GE-400 Jet Aircraft Engine. Masters thesis. Naval Postgraduate School, Monterrey, CA.

US Army PEWG (2011). Army Product Data & Engineering Working Group (PEWG) Report: Standards Used to Acquire Product Data Summary Report. US Army Materiel Command

W3C (2014). http://www.w3.org/standards/ World Wide Web Consortium

W3C Semantic Web (2014). http://www.w3.org/standards/semanticweb/ World Wide Web Consortium
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