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Research Papers

A Framework for Building Dimensionless Behavioral Models to Aid in Function-Based Failure Propagation Analysis

[+] Author and Article Information
Eric Coatanéa

Department of Engineering Design and Production,  Helsinki University of Technology (TKK), P.O. Box 4100, FIN-02015 HUT, Finlanderic.coatanea@tkk.fi

Sarayut Nonsiri, Tuomas Ritola

Department of Engineering Design and Production,  Helsinki University of Technology (TKK), P.O. Box 4100, FIN-02015 HUT, Finland

Irem Y. Tumer

Complex Engineered System Design Lab, Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 204 Rogers Hall, Corvallis, OR 97331irem.tumer@oregonstate.edu

David C. Jensen

Complex Engineered System Design Lab, Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 204 Rogers Hall, Corvallis, OR 97331

J. Mech. Des 133(12), 121001 (Dec 09, 2011) (13 pages) doi:10.1115/1.4005230 History: Received June 29, 2010; Revised September 23, 2011; Published December 09, 2011; Online December 09, 2011

This research builds on previous work on function-based failure analysis and dimensional analysis to develop a design stage failure identification framework. The proposed framework is intended to provide an alternative approach to model the behavior for use in function-based failure analysis proposed in the literature. This paper specifically proposes to develop more detailed behavioral models derived from information available at the configuration level. The new behavioral model uses design variables, which are associated with units and quantities (i.e., mass, length, time, etc…), and generates a graph of interactions for each component to define the quantitative behavior of components. The dimensionless behavioral modeling is applied briefly to the analysis of functional failures and fault propagation at a highly abstract system concept level before any potentially high-cost design commitments are made. The main contributions in this paper include: a method to automatically select the main variables of interest, an automatic causal ordering of the variables based on their units, an automatically generated graph associating the variables, a machinery based on dimensional analysis allowing a quantitative simulation of the graphs, and a methodology to combine subgraphs and create component behavioral models.

Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Interfunctions interactions with Π numbers

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Figure 2

Type of system variables for design problems

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Figure 3

Initial causal ordering graph representation of the pressure regulator

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Figure 4

Final causal ordering graph

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Figure 5

Configuration model of the function Regulate pressure of Liquid for the pressure regulator solution presented in Ref. [10]

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Figure 6

List of variables of the pressure regulator

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Figure 7

Graphical interpretation of the effect of the interaction between ρ and Pin

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Figure 8

Configuration model of the pressure regulator in form of a graph with some of the partial derivatives

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Figure 9

Composition using power variables as interface variables

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Figure 10

Composition of functions regulate and guide

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Figure 11

Configuration component of the function to guide

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Figure 12

Graphs for the function guide and the component pipe

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Figure 13

Coupling between two elementary configuration models

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Figure 14

Coupling Π numbers

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Figure 15

Simplified graph for the ensemble regulate-guide liquid and the configuration components valve and pipe of the pressure regulator (represented without the levels of interactions between the variables)

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Figure 16

Input pattern and its impact

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Figure 17

Modified architecture of the FFIP framework (in bold the modified phases) [9]

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