Causal Analytics in IIoT – AI That Knows What Causes What , and When
model for the engineers who can interpret the results .
This diagram shows , for example the causal relationship and confidence level , in brackets , of the condenser coil pressure and the flash vessel pressure ( nodes I and J ) that correspond to the chart in Figure 4 .
The plant engineers were most interested in examining the main relationships , those with the strongest measure casual coefficients and higher levels of confidence .
Findings and Observations from the Phase 1 Analysis
The chart in Figure 4 provided the most insight to the FPSO operators . The initial objective to validate the rCA algorithm was accomplished with physical evidence to support the outputs of the rCA analysis . The following three examples describe some of that validation process .
The high casual coefficient of the condenser coil pressure and the flash vessel pressure
Figure 6 : The top cause and effect relationships shown as a directed graph
Figure 6 provides a causal coefficient view in a traditional graph that removes the contextual bias that an engineer may have . This view enabled the engineers to see causal relationships without the physical process relationships . It triangulated some of their findings and observations from the chart and process flow diagram views . made sense from an engineering perspective as it is part of the design . It was expected to have a high causal relationship and it did . This proved that the rCA algorithm performed as expected .
The causal relationship between the condenser coil pressure and the dewpoint ( middle of the chart ) was not obvious prior
IIC Journal of Innovation - 17 -