Speaker
Description
The number of complex infrastructures in an industrial setting
is growing and is not immune to unexplained recurring events
such as breakdowns or failure that can have an economic and
environmental impact. To understand these phenomena, sensors
have been placed on the different infrastructures to track, monitor,
and control the dynamics of the systems. The causal study of these
data allows predictive and prescriptive maintenance to be carried
out. It helps to understand the appearance of a problem and find
counterfactual outcomes to better operate and defuse the event.
In this paper, we introduce a novel approach combining the
case-crossover design which is used to investigate acute triggers
of diseases in epidemiology, and the Apriori algorithm which is a
data mining technique allowing to find relevant rules in a dataset.
The resulting time series causal algorithm extracts interesting rules
in our application case which is a non-linear time series dataset.
In addition, a predictive rule-based algorithm demonstrates the
potential of the proposed method.
| Keywords | Causality, Time Series, Data Mining |
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