Causal Analytics in IIoT – AI That Knows What Causes What, and When
Figure 3: Locations of sensors (red circles) on a process diagram of plant operations
this information to map it back to the
physical process.
The FPSO plant engineers provided one
month’s data at one-minute intervals from
sensors at the above locations. The rCA AI
algorithm processed the data to identify a
limited number of cause and effect
relationships between the equipment or
devices in Figure 3 where there is a high
causation coefficient. This is derived from a
proprietary causal effects matrix. The causal
effects matrix tends to be sparse with a
much small number than in a correlation
matrix, typically about 10-15% for the same
data.
It is easy to spot events that have a high
causal coefficient with high confidence
levels and focus on the meaning and impact
of these insights.
The output from the causal effects matrix
that provided invaluable insight for the
engineers is a graph (Figure 4) that ranks
causal relationship based on causal
coefficient and the confidence level in the
causal relationship. It identifies those
relationships with high causality and high
confidence at a glance and engineers can use
IIC Journal of Innovation
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