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Reasoning about system-level failure behavior from large sets of function-based simulations

Published online by Cambridge University Press:  30 September 2014

David C. Jensen ,
Oladapo Bello ,
Christopher Hoyle  and
Irem Y. Tumer
David C. Jensen*
Affiliation:
Department of Mechanical Engineering, University of Arkansas, Fayetteville, Arkansas, USA
Oladapo Bello
Affiliation:
Department of Mechanical Engineering, University of Arkansas, Fayetteville, Arkansas, USA
Christopher Hoyle
Affiliation:
School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Irem Y. Tumer
Affiliation:
School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
*
Reprint requests to: David C. Jensen, Department of Mechanical Engineering, University of Arkansas, 204 Mechanical Engineering Building, Fayetteville, AR 72701, USA. E-mail: dcjensen@uark.edu
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Abstract

This paper presents the use of data clustering methods applied to the analysis results of a design-stage, functional failure reasoning tool. A system simulation using qualitative descriptions of component behaviors and a functional reasoning tool are used to identify the functional impact of a large set of potential single and multiple fault scenarios. The impact of each scenario is collected as the set of categorical function “health” states for each component-level function in the system. This data represents the space of potential system states. The clustering and statistical tools presented in this paper are used to identify patterns in this system state space. These patterns reflect the underlying emergent failure behavior of the system. Specifically, two data analysis tools are presented and compared. First, a modified k-means clustering algorithm is used with a distance metric of functional effect similarity. Second, a statistical approach known as latent class analysis is used to find an underlying probability model of potential system failure states. These tools are used to reason about how the system responds to complex fault scenarios and assists in identifying potential design changes for fault mitigation. As computational power increases, the ability to reason with large sets of data becomes as critical as the analysis methods used to collect that data. The goal of this work is to provide complex system designers with a means of using early design simulation data to identify and mitigate potential emergent failure behavior.

Keywords

Big DataClusteringComplex System DesignEmergent BehaviorFault Reasoning
Type
Special Issue Articles
Information
AI EDAM , Volume 28 , Issue 4: Design of Complex Engineered Systems , November 2014 , pp. 385 - 398
Copyright
Copyright © Cambridge University Press 2014 

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