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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
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. doi:10.1016/j.ress.2019.106678.
Hund, Lauren, & Schroeder, Benjamin. A causal perspective on reliability assessment. United States. doi:10.1016/j.ress.2019.106678.
Hund, Lauren, and Schroeder, Benjamin. Wed . "A causal perspective on reliability assessment". United States. doi:10.1016/j.ress.2019.106678.
@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:
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This content will become publicly available on October 23, 2020
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