System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The fields of risk analysis and prognostics and health management (PHM) have developed in a largely independent fashion. However, both fields share a common core goal. They aspire to manage future adverse consequences associated with prospective dysfunctions of the systems under consideration due to internal or external forces. This paper describes how two prominent risk analysis theories and methodologies – Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) – can be adapted to support the design of PHM systems in the context of smart manufacturing processes. Specifically, the proposed methodologies will be used to identify targets – components, subsystems, or systems – that would most benefit from a PHM system in regards to achieving the following objectives: minimizing cost, minimizing production/maintenance time, maximizing system remaining usable life (RUL), maximizing product quality, and maximizing product output.HHM is a comprehensive modeling theory and methodology that is grounded on the premise that no system can be modeled effectively from a single perspective. It can also be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system. EFCs connote trends in external or internal sources of risk to a system that may adversely affect specific states of the system. An important aspect of proactive risk management includes bolstering the resilience of the system for specific EFCs by appropriately controlling the states. Risk scenarios for specific EFCs can be the basis for the design of prognostic and diagnostic systems that provide real-time predictions and recognition of scenario changes. The HHM methodology includes visual modeling techniques that can enhance stakeholders’ understanding of shared states, resources, objectives and constraints among the interdependent and interconnected subsystems of smart manufacturing systems. In risk analysis, HHM is often paired with Risk Filtering, Ranking, and Management (RFRM). The RFRM process provides the users, (e.g., technology developers, original equipment manufacturers (OEMs), technology integrators, manufacturers), with the most critical risks to the objectives, which can be used to identify the most critical components and subsystems that would most benefit from a PHM system.
A case study is presented in which HHM and RFRM are adapted for PHM in the context of an active manufacturing facility located in the United States. The methodologies help to identify the critical risks to the manufacturing process, and the major components and subsystems that would most benefit from a developed PHM system.
How to Cite
##plugins.themes.bootstrap3.article.details##
risk assessment, robotics, smart manufacturing, systems modeling
63 (1), pp. 135-149.
Al-Habaibeh, A., & Gindy, N. (2000). A new approach for systematic design of condition monitoring systems for milling processes. Journal of Materials Processing Technology, vol. 107 (1), pp. 243-251.
Alvandi, S., Bienert, G., Li, W., & Kara, S. (2015). Hierarchical modelling of complex material and energy flow in manufacturing systems. Procedia CIRP, vol. 29, pp. 92-97.
Andretta, M. (2014). Some considerations on the definition of risk based on concepts of systems theory and probability. Risk Analysis, vol. 34 (7), pp. 1184-1195.
Bai, G., Wang, P., & Hu, C. (2015). A self-cognizant dynamic system approach for prognostics and health management. Journal of Power Sources, vol. 278, pp. 163-174. doi:10.1016/j.jpowsour.2014.12.050.
Fernández, F. B., & Pérez, M. Á. S. (2015). Analysis and modeling of new and emerging occupational risks in the context of advanced manufacturing processes. Procedia Engineering, vol. 100, pp. 1150-1159.
Haimes, Y. Y. (3rd ed.). (2009). Risk modeling, assessment, and management. Hoboken, NJ: John Wiley & Sons, Inc.
Haimes, Y. Y. (2012). Systems‐based guiding principles for risk modeling, planning, assessment, management, and communication. Risk Analysis, vol. 32 (9), pp. 1451- 1467.
Haimes, Y. Y., & Horowitz, B. M. (2004). Adaptive two- player hierarchical holographic modeling game for counterterrorism intelligence analysis. Journal of Homeland Security and Emergency Management, vol. 1 (3).
Haimes, Y. Y., Kaplan, S., & Lambert, J. H. (2002). Risk filtering, ranking, and management framework using hierarchical holographic modeling. Risk Analysis, vol. 22 (2), pp. 383-397. doi:10.1111/0272-4332.00020.
He, N., Zhang, D. Z., & Li, Q. (2014). Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system. International Journal of Production Economics, vol. 149, pp. 117- 130.
Hou-bo, H., & Jian-min, Z. (2011). Cost-benefit model for PHM. Procedia Environmental Sciences, vol. 10, pp. 759-764.
Barajas, L. G., & Srinivasa, N. (2008). Real-time diagnostics, prognostics and health management for large-scale manufacturing maintenance systems. ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing (pp. 85-94). American Society of
Mechanical Engineers.
Batzel, T. D., & Swanson, D. C. (2009). Prognostic health management of aircraft power generators. In IEEE Transactions on Aerospace and Electronic Systems, vol. 45 (2), pp. 473-482.
Biehl, S., Staufenbiel, S., Recknagel, S., Denkena, B., & Bertram, O. (2012). Thin film sensors for condition monitoring in ball screw drives. 1st Joint International Symposium on System-Integrated Intelligence: New Challenges for Product and Production Engineering.
Hofmeister, J. P., Wagoner, R. S., & Goodman, D. L.(2013). Prognostic health management (PHM) of electrical systems using condition-based data for anomaly and prognostic reasoning. Italian Association of Chemical Engineering, vol. 33, pp. 991-996. doi:10.3303/CET1333166.
Holland, S. W., Barajas, L. G., Salman, M., & Zhang, Y.(2010). PHM for automotive manufacturing & vehicle applications. Prognostics & Health Management Conference Fielded Systems Session, October 14,Portland, Oregon.
Chittester, C. G., & Haimes, Y. Y. (2004). Risks of terrorism to information technology and to critical interdependent infrastructures. Journal of Homeland Security and Emergency Management, vol. 1 (4).
Cocheteux, P., Voisin, A., Levrat, E., Iung, B. (2009). Prognostic design: requirements and tools. 11th International Conference on The Modern Information Technology in the Innovation Processes of the Industrial Enterprises, MITIP 2009. Bergame, Italy.
Crowther, K. G., Dicdican, R. Y., Leung, M. F. , Lian, C., & Williams, G. M. (2004). Assessing and managing risk of terrorism to Virginia’s interdependent transportation systems. Center for Risk Management of Engineering Systems, Charlottesville, Virginia.
Dombroski, M., Haimes, Y. Y., Lambert, J. H., Schlussel, K., & Sulcoski, M. (2002). Risk-based methodology for support of operations other than war. Military Operations Research, vol. 7 (1), pp. 19-38.
Feldman, K., Jazouli, T., & Sandborn, P. (2009). A methodology for determining the return on investment associated with prognostics and health
Jung K., Morris K. C., Lyons K. W., Leong S., & Cho H. (2015). Mapping strategic goals and operational performance metrics for smart manufacturing systems. Procedia Computer Science, vol. 44, pp. 184-193. doi:10.1016/j.procs.2015.03.051.
Kaplan, S., Haimes, Y. Y., & Garrick, B. J. (2001). Fitting hierarchical holographic modeling into the theory of scenario structuring and a resulting refinement to the quantitative definition of risk. Risk Analysis, vol. 21 (5), pp. 807-807.
Lambert, J. H., Haimes, Y. Y., Li, D., Schooff, R. M., & Tulsiani, V. (2001). Identification, ranking, and management of risks in a major system acquisition. Reliability Engineering & System Safety, vol. 72 (3), pp. 315-325.
Lee, C. K. M., Lv, Y., & Hong, Z. (2013). Risk modelling and assessment for distributed manufacturing system. International Journal of Production Research, vol. 51 (9), pp. 2652-2666.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems – Reviews, methodology and applications. Mechanical Systems and Signal Processing, vol. 42 (1-2), pp. 314-334. doi:10.1016/j.ymssp.2013.06.004.
National Institute of Standards and Technology (NIST). (2015). Measurement science roadmap for prognostics and health management for smart manufacturing systems. Roadmapping Workshop on Measurement Science for Prognostics and Health Management of Smart Manufacturing Systems. November 19-20, 2014, Gaithersburg, MD.
Vykydal, D., Plura, J., Halfarová, P., & Klaput, P. (2015). Advanced approaches to failure mode and effect analysis (FMEA) applications. Metalurgija, vol. 54 (4), pp. 675-678.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.