Causal Analytics in IIoT – AI That Knows What Causes What, and When
Production-based Root Cause Analysis
has roots in the field of quality control
for industrial manufacturing.
Process-based Root Cause Analysis, a
follow-on to production-based RCA,
broadens the scope of RCA to include
business processes.
Failure-based Root Cause Analysis
originates in the practice of failure
analysis as employed in engineering
and maintenance.
Systems-based Root Cause Analysis
has emerged as an amalgam of the
preceding schools, incorporating
elements from other fields such as
change management, risk
management and systems analysis. assess root causes, but also find other causal
factors. These causal factors may not lead to
equipment or process failure but may still
impact equipment or process performance.
Root Cause Analysis became popular as an
approach to methodically identify and
correct the root causes of events instead of
addressing symptomatic results of these
events. The objective of root cause analysis
is to prevent problem recurrence. Some
popular root cause analysis techniques
include “Five Whys” and Cause and Effect
(Fishbone) diagrams. These techniques rely
on human interpretation of event
information and data and require
experienced practitioners to conduct the
analysis. It is often limited to a few critical
production assets as the manual process is
time-consuming and laborious. Wilson’s
distinction between root causes and other
causal factors provides some guidance on
the application of causal analytics in an IoT
context for this article. Traditional
techniques focused only on finding the root
causes through manual review. Modern
techniques such as rCA described in this
article, combined with IoT data and
advances in AI, enable engineers to not only There are three main reasons to find a
reliable, data-driven approach to finding
root causes and causal factors for equipment
failure and operational performance in
industrial environments:
Aging workforce and a large number
of experienced engineers retiring soon
Complexity of equipment, making it
harder to troubleshoot
Inaccuracy of Root Cause Analysis
Recent advances in cloud computing and AI
provide the necessary infrastructure to
analyze event data for IoT and other sources
at massive scale. This means analysts can
have a more expansive view of causal events
rather than a reductionist view where the
scope of an analysis is limited to what a
human can process.
M OTIVATION FOR D ATA -D RIVEN ,
R ELIABLE C AUSAL A NALYTICS
Retiring Workforce
With a retiring workforce in many industrial
sectors, the experience needed to conduct
meaningful RCAs is decreasing. As much of
the traditional approaches rely on
observational analysis, the number of
experienced engineers that can provide
reliable analysis is fast reducing.
According to a January 2017 assessment by
the US Department of Energy, 25% of US
employees in electric and natural gas utilities
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June 2018