Efficient Plant September 2021 - 23

department | on the floor
ments bother you, then you may not fully understand reliabilitycentered
maintenance.
Many companies don't have the patience to implement RCM or it
doesn't survive changes in management over the five-plus years needed
to properly transition and instill a sustaining process and mindset.
The six curves (patterns) that resulted from the original RCM study
by Nolan and Heap (published by the Office of Assistant Secretary of
Defense, 1978) changed the paradigm of what's important in applying
maintenance. It's been proven to work in military and the aerospace
industry where there are robust adhered-to procedures. In industry, the
RCM process is more tedious in application, often due to culture.
RCM has been around for more than 40 years and albeit a few variations
in implementation, the process still stands. I've not seen any new
theories that are better or newer. The big question is, do the six curves
explain everything that we need to know? Are we asking the correct
questions? We don't know what we don't know. The learning increase
that comes from condition monitoring and machine learning/artificial
intelligence (ML/AI) will reveal some additional knowledge to take us
to a still higher level of understanding of failure modes and RCM. As
industry collects not just more data, but complete and accurate data,
additional insights will be revealed.
Some of the potential benefits of increased use of ML/AI in reliability
and maintenance include:
Better insights on finding failure modes
Recognizing pending asset failures earlier than condition
monitoring alone
More focused root-cause analysis and elimination
Optimized planning and scheduling (coordination of people
and parts)
Increased confidence in reducing spare-parts inventory
Greater access to historical data and cross-referencing collective
knowledge (such as failure mode and effects analysis, maintenance-management
system, production data)
Better able to evaluate and make risk-based decisions
Enable prescriptive maintenance (system evaluates data and
generates work orders to minimize operation risk)
Create overall understanding of equipment failures, downtime,
repair time, and asset criticality
Ability (over time) to transition more people to do root-cause analysis
and elimination and other problem solving/long-term thinking
practices
Better coordinate (optimize) when maintenance should get done to
minimize operational disruptions.
SEPTEMBER 2021
Asset Degradation Process
The ongoing question is what is the optimal inspection time in the
asset-degradation spectrum?
ML/AI enables the analysis of big data to identify patterns that
provide insights for improved decision making.
When you look at the generic P-F asset-degradation curve illustrated
here, the struggle is always to find the optimal inspection time. The
typical starting point is using an inspection interval about half the P-F
interval, getting team-member and historical input, original-equipment-manufacturer
recommendation, and other information. Too
early/too often (point A) and you are wasting resources and reintroducing
infant mortality to the component or asset. Too late (point C)
and you risk a costly breakdown. Better data analysis with ML/AI can
help optimize the inspection interval (point B). In addition, proper
applications of ML/AI can detect potential issues earlier than predictive
technologies and condition-based monitoring alone. Your collective
knowledge from all data analysis and practical experience should be
used to design-in better machinery and equipment to eliminate or
minimize the consequence of failure.
Today, computing power and data storage are becoming more cost
effective. What's often challenging is getting good, meaningful, clean
data. By that I mean data that is complete, accurate, and in the correct
format. This is something that can be improved on now, so you have a
database with which you are willing to make decisions as you transition
to more digital-driven practices. ML/AI-driven RCM is the future and
will improve the reliability of industrial operations. EP
Based in Knoxville, Dr. Klaus M. Blache is director of the
Reliability & Maintainability Center at the Univ. of Tennessee,
and a research professor in the College of Engineering.
Contact him at kblache@utk.edu.
EFFICIENTPLANTMAG.COM | 23
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Efficient Plant September 2021

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