Causal Analytics in IIoT – AI That Knows What Causes What, and When
actual cause(s) of the problems. Routine
correlational methods of analysis, such as
traditional RCA, had been applied but
provided the operators with only limited
assistance.
The analysis approach in phase 2 consists of
three main process steps:
Ingest data: Real-time feeds of
operational data are collected from
intelligent equipment (e.g.
submersible pumps, etc.) and from
the Honeywell Historian through
listener integration connectors that
stream the data to the analysis step;
Perform analysis: The streaming data
from the previous step is passed to
the rCA algorithms in the rCA function
connector where the analysis is
performed; and
Action agents provide reports and
actions on the results of the analysis
that identify real causes and not just
symptoms of production outages.
The project was conducted in 2 phases. In
the initial phase the FPSO operator wanted
to validate the algorithms through an initial
manual analysis before automating the
process in phase 2.
The rCA solution used sensor-driven and
machine data covering a defined period
where these events occurred as the basis for
the analysis. The rCA analysis discovered
cause-effect relationships that were not
previously known by the engineers and that
helped to identify and address the real root
causes of the problem. The results of the rCA
analysis in phase 1 provided new insights
into causal relationships that were
previously not considered in the human
analysis process. This is of significant value
and benefit to the operations and
engineering teams of the Oil & Gas customer
operating the FPSO and it provided the
support to automate the process in phase 2.
Data from sensors at locations across the
plant operations were mapped to locations
on a process flow diagram (PFD) and are
shown as red circles in Figure 3. This provides
a familiar visual reference for the engineers
of the physical process and the data from the
different sources.
The example data shown is that of the phase
1 analysis that provided the insights and
confidence in the output to support the
decision to automate the process for a larger
data set and additional equipment.
rCA on the FPSO
During phase 1 the analysis was done
manually to validate the model and establish
the workflow for the automated steps in
phase 2.
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June 2018