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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 Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE; USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1595019
Alternate ID(s):
OSTI ID: 1639612
OSTI ID: 1693908
Report Number(s):
SAND--2019-14320J; 682707
Journal Information:
Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Journal Issue: C Vol. 195; ISSN 0951-8320
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (23)

Assessing causal claims about complex engineered systems with quantitative data: internal, external, and construct validity journal November 2017
Improving the analysis of dependable systems by mapping fault trees into Bayesian networks journal March 2001
Selection of model discrepancy priors in Bayesian calibration journal November 2014
Validation of reliability computational models using Bayes networks journal February 2005
Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal journal May 2006
Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty journal September 2011
Quantification of margins and uncertainties: A probabilistic framework journal September 2011
Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches journal August 2011
Ideas underlying the Quantification of Margins and Uncertainties journal September 2011
Distinguishing between model- and data-driven inferences for high reliability statistical predictions journal December 2018
Causal inference and the data-fusion problem journal July 2016
Knowledge discovery from observational data for process control using causal Bayesian networks journal March 2007
Reliability: The Other Dimension of Quality journal January 2004
Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology journal January 2002
Data, Design, and Background Knowledge in Etiologic Inference journal January 2001
Bias Formulas for Sensitivity Analysis of Unmeasured Confounding for General Outcomes, Treatments, and Confounders journal January 2011
Testing the untestable: reliability in the 21st century journal March 2003
Information Value Theory journal January 1966
Value of Information Analysis in Environmental Health Risk Management Decisions: Past, Present, and Future journal June 2004
Clarifying Types of Uncertainty: When Are Models Accurate, and Uncertainties Small?: Commentary journal October 2011
Foundational Issues in Risk Assessment and Risk Management: Perspectives journal October 2013
Stan : A Probabilistic Programming Language journal January 2017
Test Resource Allocation in Hierarchical Systems Using Bayesian Networks journal March 2013

Figures / Tables (11)

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Table 1(p. 10)table Table 1
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Figure A.9(p. 15)figure Figure A.9
Figure A.10(p. 16)figure Figure A.10

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Related Subjects

42 ENGINEERING
Causality
Margin
Reliability
Risk assessment