Causal Analytics in IIoT – AI That Knows What Causes What, and When
to the analysis and it turned out that it had
an impact on operations. It led to further
investigations by the engineering team. and the benefits of the automation process
will described in a future article by the
authors.
The third example relates to the causal
relationship at the bottom of the chart. It has
a high causal coefficient, but a low
confidence level for ambient temperature to
measure dewpoint. In a physical, isolated
system the 2 should not be related from an
engineering perspective. One of the
engineers found the
cause after
investigation. The dewpoint measurement
sensor is exposed to full sun when the vessel
sits in a certain orientation, which affected
the dewpoint measurement and led it to
track the ambient temperature as a measure
of exposure to sunlight. This in turn affected
the efficiency of operations as the rate of lift-
gas being dehydrated was affected. The use case illustrated that an additional
dimension of understanding can be added to
the analysis and troubleshooting of
Industrial IoT problems. The results are
reported in the above formats and able to be
extended to additional desired forms of
output.
C ONCLUSION
Most IoT platform vendors use traditional
statistical methods based on correlation and
regression for their analytics functionality.
These are proven to work but also have
limitations that restrict their applicability to
quickly and accurately identify causative
factors that did, or will, cause some effect.
This means the decision making may be sub-
optimal or even inaccurate.
The three examples provided enough
evidence to proceed with phase 2 to
automate the process for scheduled time
intervals and to include additional data
points. One of the main benefits of the
automated process is that the FPSO
engineers can perform the root cause
analysis without the assistance of a data
scientist to manually perform the analysis.
This is an important requirement for the
FPSO operator to deploy rCA at scale. The
nature of causal analytics requires a
complete re-analysis if any of the causes
found in previous analyses were addressed
or changed. There are often unintended
consequences of making changes that only
show up in a new analysis. These analyses
need to be performed in a consistent
manner which is facilitated by the
automation of the process. Phase 2 is
underway at the time of writing this article
rCA adds a different and complementary
dimension of IoT data analysis and decision
making. rCA is a breakthrough algorithmic
approach that brings new methods for the
application of cause and effect analytics to
real-world industrial problems. By adding a
new and powerful dimension of analytics, it
enables users to refine their decision-
making, reduce risks, improve safety and
reliability, and reduce costs and equipment
downtime.
The culmination of this “Reliable Causal
Analytics” approach is the ability for the
solution to enable operators to more
completely understand:
What caused what (and when)
What will cause what (and when)
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June 2018