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System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing

Published

Author(s)

M Malinowski, Peter A. Beling, Amy LaViers, Jeremy Marvel, Brian A. Weiss

Abstract

The development 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. 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.
Proceedings Title
Annual Conference Of The Prognostics And Health Management Society 2015
Conference Dates
October 19-24, 2015
Conference Location
San Diego, CA, US

Keywords

prognostics and health management (PHM), smart manufacturing, diagnostics, prognostics, condition-monitoring, maintenance, industrial robotics

Citation

Malinowski, M. , Beling, P. , LaViers, A. , Marvel, J. and Weiss, B. (2015), System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing, Annual Conference Of The Prognostics And Health Management Society 2015, San Diego, CA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=919168 (Accessed February 28, 2024)
Created October 23, 2015, Updated April 4, 2022