A causal perspective on reliability assessment. (March 2020)

• Record Type: Journal Article
• Authors: Hund, Lauren; Schroeder, Benjamin
• Is Part Of: Reliability engineering & system safety. Volume 195(2020)
• Publication Date: 2020-03
• Subjects: Causality -- Reliability -- Margin -- Risk assessment; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2019.106678
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Understanding reliability often requires causal, rather than predictive, inferences. Structural causal modeling is a framework for thinking causally about reliability. Sensitivity studies investigating data bias can improve decisions about reliability. 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.