Reasoning about System-Level Failure Behavior from Large Sets of Function-Based Simulations Public Deposited

http://ir.library.oregonstate.edu/concern/articles/765376001

This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Cambridge University Press and can be found at:  http://journals.cambridge.org/action/displayJournal?jid=AIE

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  • 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 assist 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.
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  • Jensen, D. C., Bello, O., Hoyle, C., & Tumer, I. Y. (2014). Reasoning about system-level failure behavior from large sets of function-based simulations. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 28(04), 385-398. doi:10.1017/S0890060414000547
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  • description.provenance : Made available in DSpace on 2015-06-26T19:06:57Z (GMT). No. of bitstreams: 1 HoyleChristopherMechIndMfgEngnReasoningAboutSystemLevelFailure.pdf: 829729 bytes, checksum: bee121045b5817732101e35f06b89b0b (MD5) Previous issue date: 2014-11
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