Causal Analytics in IIoT – AI That Knows What Causes What, and When
causality of business and operational
events such as equipment failure or
operational issues
I NTRODUCTION
Finding the “because” behind certain
business or operations events has always
been a key part of any engineering,
maintenance or operations manager’s job in
industrial businesses. “The First Stage
compressor failed because…” or “the supply
tank ran dry because…” are common
phrases in maintenance and operations
departments in industrial businesses.
Finding the “because” traditionally relies on
experienced engineers that can interpret
event, contextual and temporal data to
deduce the likelihood of specific factors
causing others in either a negative or
positive way.
How Reliable Causal Analytics provides
data-driven decision support for
traditional Root Cause Analysis
approaches
The approach to embed this causal
analytics methodology in IoT Process
Management software to be able to
perform this in a repeatable and
automated manner.
The rCA approach is the result of many years
of research and application of causal
analytics in real-world scenarios. Through
this, Au Sable developed rCA algorithms that
enables cause and effect relationships to be
identified from sensor-driven data and made
known to the analyst (e.g. wear on part #105
has causally impacted the performance of
device #65 with a causal coefficient of 0.86),
as well as correlation relationships in the
data.
Knowing the real root causes of events is
critical to resolving problems rather than
continuously dealing with the symptoms. It
resulted in popular, formalized approaches
such as “Root Cause Analysis,” or RCA as it is
generally known. The challenge is that there
are often multiple causal factors for these
events, and finding the one “root cause”
may not always be possible. Understanding
other causal factors that may influence the
outcome of industrial processes and the
behavior of equipment need to be
considered.
This means:
the risk of making false decisions about
what were, or will be predictively, the
causal drivers of an effect is reduced,
and
the potential for costly or disastrous
mistakes is thereby reduced.
Au Sable, in collaboration with XMPro,
developed
an
algorithmic,
artificial
intelligence-based, approach for “Reliable
Causal Analytics” (rCA) in industrial IoT
applications. This article demonstrates:
This article provides background on
traditional Root Cause Analysis and the
evolution
of
Causal
Analytics.
It
demonstrates how to automate the
analytics to scale with an IoT Process
Management platform and how it is applied
in an industrial application. It provides a
It is possible to perform Reliable Causal
Analytics using industrial IoT data and
Artificial Intelligence (AI) to determine
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June 2018