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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

Information

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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References

REFERENCES

Coatanéa, E., Nonsiri, S., Ritola, T., Tumer, I.Y., & Jensen, D. (2011). A framework for building dimensionless behavioral models to aid in function-based failure propagation analysis. Journal of Mechanical Design 133(12), 121001.Google Scholar
Dulac, N., & Leveson, N. (2005). Incorporating safety into early system architecture trade studies. Proc. Int. Conf. System Safety Society, San Diego, CA, August 22–26.Google Scholar
Estivill-Castro, V. (2002). Why so many clustering algorithms: a position paper. ACM SIGKDD Explorations Newsletter 4(1), 65–75.Google Scholar
Grantham-Lough, K., Stone, R.B., & Tumer, I.Y. (2009). The risk in early design method. Journal of Engineering Design 20(2), 144–173.Google Scholar
Han, J., Kamber, M., & Pei, J. (2006). Data Mining: Concepts and Techniques. San Francisco, CA: Morgan Kaufmann.Google Scholar
Huang, Z., & Jin, Y. (2008). Conceptual stress and conceptual strength for functional design-for-reliability. Proc. ASME Design Engineering Technical Conf.; International Design Theory and Methodology Confs.CrossRefGoogle Scholar
Jensen, D., Hoyle, C., & Tumer, I.Y. (2012). Clustering function-based failure analysis results to evaluate and reduce system-level risks. Proc. ASME 2012 Int. Design Engineering Technical Conf./Computers and Information in Engineering Conf.Google Scholar
Jensen, D., Tumer, I.Y., & Kurtoglu, T. (2008). Modeling the propagation of failures in software-driven hardware systems to enable risk-informed design. Proc. ASME Int. Mechanical Engineering Congr. and Exposition.Google Scholar
Jensen, D., Tumer, I.Y., & Kurtoglu, T. (2009 a). Design of an electrical power system using a functional failure and flow state logic reasoning methodology. Proc. Prognostics Health Management Society Conf.Google Scholar
Jensen, D., Tumer, I.Y., & Kurtoglu, T. (2009 b). Flow state logic (FSL) for analysis of failure propagation in early design. Proc. ASME Design Engineering Technical Conf.: Int. Design Theory and Methodology Confs.Google Scholar
Krus, D., & Grantham Lough, K. (2007). Applying function-based failure propagation in conceptual design. Proc. ASME Design Engineering Technical Conf.: Int. Design Theory and Methodology Confs.Google Scholar
Kurtoglu, T., Johnson, S., Barszcz, E., Johnson, J., & Robinson, P. (2008). Integrating system health management into early design of aerospace systems using functional fault analysis. Proc. Int. Conf. Prognostics and Heath Management, PHM'08.CrossRefGoogle Scholar
Kurtoglu, T., & Tumer, I.Y. (2008). A graph-based fault identification and propagation framework for functional design of complex systems. Journal of Mechanical Design 130(5), 1–8.Google Scholar
Kurtoglu, T., Tumer, I.Y., & Jensen, D. (2010). A functional failure reasoning methodology for evaluation of conceptual system architectures. Research in Engineering Design 21(4), 209.Google Scholar
Lazarsfeld, P.F., & Koch, S. (1959). Latent structure analysis. In Psychology: A Study of a Science (Koch, S., Ed.), Vol. 3. New York: McGraw–Hill.Google Scholar
Leveson, N. (2011). Engineering a Safer World. Cambridge, MA: MIT Press.Google Scholar
Linzer, D.A., & Lewis, J. (2011 a). poLCA: an R package for polytomous variable latent class analysis. Journal of Statistical Software 42(10), 1–29.Google Scholar
Linzer, D.A., & Lewis, J. (Eds.). (2011 b). poLCA: polytomous variable latent class analysis. R package version 1.3.1. Accessed at http://www.inside-r.org/packages/polca/versions/1-3-1Google Scholar
Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–137.Google Scholar
MacKay, D.J. (2003). Information Theory: Inference and Learning Algorithms. Cambridge: Cambridge University Press.Google Scholar
Mardia, K.V., Kent, J.T., & Bibby, J.M. (1980). Multivariate Analysis. New York: Academic Press.Google Scholar
Papakonstantinou, N., Jensen, D., Sierla, S., & Tumer, I.Y. (2011). Capturing interactions and emergent failure behavior in complex engineered systems and multiple scales. Proc. ASME Design Engineering Technical Conf.: Computers in Engineering Conf.Google Scholar
Pearl, J. (2000). Causality: Models, Reasoning and Inference. Cambridge: Cambridge University Press.Google Scholar
Poll, S. (2007). Advanced diagnostics and prognostics testbed. Proc. 18th Int. Workshop on Principles of Diagnosis.Google Scholar
R Development Core Team. (2011). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Sierla, S., Tumer, I.Y., Papakonstantinou, N., Koskinen, K., & Jensen, D. (2012). Early integration of safety to the mechatronic system design process by the functional failure identification and propagation framework. Mechatronics 22(2), 137–151.Google Scholar
Stone, R.B., Tumer, I.Y., & VanWie, M. (2005). The function failure design method. Journal of Mechanical Design 14, 25–33.Google Scholar
Tumer, I.Y., & Smidts, C. (2010). Integrated design and analysis of software-driven hardware systems. IEEE Transactions on Computers 60(8), 1072–1084.Google Scholar
Vermunt, J., & Magidson, J. (2004). Latent class analysis. In The Sage Encyclopedia of Social Science Research Methods (Lewis-Beck, M.S., Bryman, A.E., & Liao, T.F., Eds.), Vol. 1. Thousand Oaks, CA: Sage.Google Scholar