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

M L Malinowski1, P A Beling1, Y Y Haimes1

  • 1University of Virginia, Charlottesville, Virginia, 22904, USA.

Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference
|July 1, 2017
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Summary

This study adapts risk analysis methods, Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM), to design Prognostics and Health Management (PHM) systems for smart manufacturing. These methods identify critical components benefiting from PHM to optimize manufacturing objectives.

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Area of Science:

  • Industrial Engineering
  • Risk Management
  • Smart Manufacturing Systems

Background:

  • Risk analysis and Prognostics and Health Management (PHM) have historically developed independently.
  • Both fields aim to mitigate future adverse consequences from system dysfunctions.
  • Smart manufacturing introduces complex systems requiring integrated risk and health management strategies.

Purpose of the Study:

  • To adapt Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) for designing PHM systems in smart manufacturing.
  • To identify system components that would most benefit from PHM to achieve key manufacturing objectives.
  • To enhance proactive risk management and system resilience through integrated modeling.

Main Methods:

  • Application of Hierarchical Holographic Modeling (HHM) for system modeling and scenario structuring.
  • Utilization of Risk Filtering, Ranking, and Management (RFRM) to prioritize critical risks and components.
  • Integration of HHM and RFRM to identify PHM targets for optimizing cost, time, remaining usable life (RUL), quality, and output.

Main Results:

  • HHM and RFRM effectively identify emergent forced changes (EFCs) and associated risk scenarios.
  • The methodologies pinpoint critical components and subsystems that would gain the most from PHM implementation.
  • A case study demonstrates the practical application in an active manufacturing facility, validating the approach.

Conclusions:

  • HHM and RFRM provide a robust framework for designing effective PHM systems in smart manufacturing.
  • The integrated approach enhances understanding of system interdependencies and facilitates targeted PHM investments.
  • This research bridges risk analysis and PHM, offering a pathway to improved manufacturing performance and reliability.