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DETECTING AND CHARACTERIZING PATTERNS OF FAILURE IN COMPLEX ENGINEERED SYSTEMS: AN ONTOLOGY DEVELOPMENT AND CLUSTERING APPROACH

Published online by Cambridge University Press:  19 June 2023

Hannah Scharline Walsh ,
Andy Dong ,
Irem Tumer  and
Guillaume Brat
Hannah Scharline Walsh*
Affiliation:
NASA Ames Research Center
Andy Dong
Affiliation:
Oregon State University
Irem Tumer
Affiliation:
Oregon State University
Guillaume Brat
Affiliation:
NASA Ames Research Center
*
Walsh, Hannah Scharline, NASA Ames Research Center, United States of America, [email protected]

Abstract

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While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA's publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Keywords

ComplexityRisk managementOntologies
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1425 - 1434
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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