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

Yacov Haimes, Amy LaViers, Jeremy Marvel, Peter A. Beling, Michael Malinowski, and Brian Weiss
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NIST
AttachmentSizeTimestamp
phmc_15_038.pdf437.2 KBAugust 21, 2015 - 12:06pm

The literature and practice of risk analysis, and prognostics and health management (PHM) have developed in a largely independent fashion. The fields share a core common goal, however. Both aspire to minimizing future adverse consequences associated with prospective dysfunctions of the systems under consideration. This paper describes how a prominent risk analysis technique, known as Hierarchical Holographic Modeling (HHM), can be adapted to support the design of PHM systems in the context of manufacturing processes.

HHM is a comprehensive modeling methodology that can be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system or system-of-systems. EFCs are major sources of failure. Thus an important aspect of proactive risk management is bolstering resilience with respect to identified EFCs. Knowledge of EFCs and risk scenarios 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, and constraints. These elements cause couplings and interdependencies among subsystems in smart manufacturing systems. In risk analysis, HHM is often paired with the Risk Ranking and Filtering Method (RFRM). RFRM provides a framework for filtering risks on the basis of attributes such as observability and controllability.

A case study is presented in which HHM and RFRM are adapted for PHM use in the context of a robotic bagging, conveyance, and palletizing process at a food manufacturer.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
038
Submission Keywords: 
risk assessment
smart manufacturing
robotics
systems modeling
Submission Topic Areas: 
Modeling and simulation
Submitted by: 
  
 
 
 

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