Bayesian Learning of Causal Relationships for System Reliability
This work addresses the problem of modeling failures in system reliability for engineers and analysts, but it is incremental as it adapts existing causal methods to a new domain.
The paper tackles the lack of causal theory in reliability by translating established causal methods into reliability theory using chain event graphs, and demonstrates this with an analysis of maintenance records from an electrical distribution company.
Causal theory is now widely developed with many applications to medicine and public health. However within the discipline of reliability, although causation is a key concept in this field, there has been much less theoretical attention. In this paper, we will demonstrate how some aspects of established causal methodology can be translated via trees, and more specifically chain event graphs, into domain of reliability theory to help the probability modeling of failures. We further show how various domain specific concepts of causality particular to reliability can be imported into more generic causal algebras and so demonstrate how these disciplines can inform each other. This paper is informed by a detailed analysis of maintenance records associated with a large electrical distribution company. Causal hypotheses embedded within these natural language texts are extracted and analyzed using the new graphical framework we introduced here.