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-