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Title: A causal perspective on reliability assessment

Journal Article · · Reliability Engineering and System Safety
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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Causality in an engineered system pertains to how a system output changes due to a controlled change or intervention on the system or system environment. Engineered systems designs reflect a causal theory regarding how a system will work, and predicting the reliability of such systems typically requires knowledge of this underlying causal structure. The aim of this work is to introduce causal modeling tools that inform reliability predictions based on biased data sources. We present a novel application of the popular structural causal modeling (SCM) framework to reliability estimation in an engineering application, illustrating how this framework can inform whether reliability is estimable and how to estimate reliability given a set of data and assumptions about the subject matter and data generating mechanism. When data are insufficient for estimation, sensitivity studies based on problem-specific knowledge can inform how much reliability estimates can change due to biases in the data and what information should be collected next to provide the most additional information. We apply the approach to a pedagogical example related to a real, but proprietary, engineering application, considering how two types of biases in data can influence a reliability calculation.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1595019
Alternate ID(s):
OSTI ID: 1693908
Report Number(s):
SAND-2019-14320J; 682707
Journal Information:
Reliability Engineering and System Safety, Vol. 195, Issue C; ISSN 0951-8320
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 8 works
Citation information provided by
Web of Science

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Value of Information Analysis in Environmental Health Risk Management Decisions: Past, Present, and Future journal June 2004

Linked Research (2)

Figures / Tables (11)

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Related Subjects

42 ENGINEERING
Causality
Reliability
Margin
Risk assessment