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Title: A causal perspective on reliability assessment

Abstract

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.

Authors:
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1595019
Alternate Identifier(s):
OSTI ID: 1693908
Report Number(s):
SAND-2019-14320J
Journal ID: ISSN 0951-8320; 682707
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Reliability Engineering and System Safety
Additional Journal Information:
Journal Volume: 195; Journal Issue: C; Journal ID: ISSN 0951-8320
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Causality; Reliability; Margin; Risk assessment

Citation Formats

Hund, Lauren, and Schroeder, Benjamin. A causal perspective on reliability assessment. United States: N. p., 2019. Web. https://doi.org/10.1016/j.ress.2019.106678.
Hund, Lauren, & Schroeder, Benjamin. A causal perspective on reliability assessment. United States. https://doi.org/10.1016/j.ress.2019.106678
Hund, Lauren, and Schroeder, Benjamin. Wed . "A causal perspective on reliability assessment". United States. https://doi.org/10.1016/j.ress.2019.106678. https://www.osti.gov/servlets/purl/1595019.
@article{osti_1595019,
title = {A causal perspective on reliability assessment},
author = {Hund, Lauren and Schroeder, Benjamin},
abstractNote = {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.},
doi = {10.1016/j.ress.2019.106678},
journal = {Reliability Engineering and System Safety},
number = C,
volume = 195,
place = {United States},
year = {2019},
month = {10}
}

Journal Article:

Figures / Tables:

Figure 1 Figure 1: (a) Battery voltage as a function of age. (b) The estimated prediction distribution for voltage at age 25 based on the linear regression model. The estimated failure probability is the area under the curve to the left of the requirement, 26.8V.

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Works referenced in this record:

Foundational Issues in Risk Assessment and Risk Management: Perspectives
journal, October 2013


Causal inference and the data-fusion problem
journal, July 2016

  • Bareinboim, Elias; Pearl, Judea
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 27
  • DOI: 10.1073/pnas.1510507113

Testing the untestable: reliability in the 21st century
journal, March 2003

  • Bennett, T. R.; Booker, J. M.; Keller-McNulty, S.
  • IEEE Transactions on Reliability, Vol. 52, Issue 1
  • DOI: 10.1109/TR.2002.807239

Improving the analysis of dependable systems by mapping fault trees into Bayesian networks
journal, March 2001


Assessing causal claims about complex engineered systems with quantitative data: internal, external, and construct validity
journal, November 2017

  • Broniatowski, David A.; Tucker, Conrad
  • Systems Engineering, Vol. 20, Issue 6
  • DOI: 10.1002/sys.21414

Stan : A Probabilistic Programming Language
journal, January 2017

  • Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.
  • Journal of Statistical Software, Vol. 76, Issue 1
  • DOI: 10.18637/jss.v076.i01

Clarifying Types of Uncertainty: When Are Models Accurate, and Uncertainties Small?: Commentary
journal, October 2011


Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology
journal, January 2002


Information Value Theory
journal, January 1966


Distinguishing between model- and data-driven inferences for high reliability statistical predictions
journal, December 2018

  • Hund, Lauren; Schroeder, Benjamin; Rumsey, Kellin
  • Reliability Engineering & System Safety, Vol. 180
  • DOI: 10.1016/j.ress.2018.07.017

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches
journal, August 2011

  • Khakzad, Nima; Khan, Faisal; Amyotte, Paul
  • Reliability Engineering & System Safety, Vol. 96, Issue 8
  • DOI: 10.1016/j.ress.2011.03.012

Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal
journal, May 2006


Knowledge discovery from observational data for process control using causal Bayesian networks
journal, March 2007


Selection of model discrepancy priors in Bayesian calibration
journal, November 2014


Validation of reliability computational models using Bayes networks
journal, February 2005


Reliability: The Other Dimension of Quality
journal, January 2004


Ideas underlying the Quantification of Margins and Uncertainties
journal, September 2011

  • Pilch, Martin; Trucano, Timothy G.; Helton, Jon C.
  • Reliability Engineering & System Safety, Vol. 96, Issue 9
  • DOI: 10.1016/j.ress.2011.03.016

Data, Design, and Background Knowledge in Etiologic Inference
journal, January 2001


Test Resource Allocation in Hierarchical Systems Using Bayesian Networks
journal, March 2013

  • Sankararaman, Shankar; McLemore, Kyle; Mahadevan, Sankaran
  • AIAA Journal, Vol. 51, Issue 3
  • DOI: 10.2514/1.J051542

Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty
journal, September 2011

  • Urbina, Angel; Mahadevan, Sankaran; Paez, Thomas L.
  • Reliability Engineering & System Safety, Vol. 96, Issue 9
  • DOI: 10.1016/j.ress.2010.08.010

Bias Formulas for Sensitivity Analysis of Unmeasured Confounding for General Outcomes, Treatments, and Confounders
journal, January 2011


Quantification of margins and uncertainties: A probabilistic framework
journal, September 2011


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