Efficient Plant May 2020 - 18

feature | predictive maintenance

Reliability professionals at a potash mine in Saskatchewan, Canada, installed a
Rexnord Smart Condition Monitoring System to monitor critical health of their
Falk gear drive. The Rexnord Edge Device collects sensor data and computes
health condition. The green Andon light indicates a healthy gear drive.

asset connectivity preferences.
Data collection is the foundation of any
PdM solution and there is no shortage of
opinions surrounding what data and how
much is required to accurately assess an
asset's health or predict impending failure.

DATA ANALYSIS
In any case, modern PdM solutions
require continuous, consistent data collected directly from the monitored asset to
record KPIs (key performance indicators)
over time. But does sensor data need to be
live streamed so that KPIs are tracked every
second of every day, or can we poll sensors
once an hour or only a few times daily?
What about the type of data we collect? Is
vibration all we really need to assess the
performance of rotating equipment? Do we
also need temperature, oil condition, electrical current, motor speed, and so forth?
These questions will be debated by solution providers and reliability practitioners
for decades to come until successful track

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records are established for PdM accuracy
across equipment categories.
Once sensors are integrated and data is
flowing, the real magic of modern PdM
solutions can materialize. That magic is in
the software that interprets the raw sensor
data using machine-learning (ML) algorithms that identify trends and link patterns
to previously recorded outcomes. For
instance, certain vibration signatures may
be associated with known component failure points, e.g., bearing degradation versus
shaft misalignment, through historical
research. This is referred to as prescriptive
maintenance (RxM) and is where established solution providers have an advantage
over new PdM players who may only be
able to detect change generically. In the
same way, the remaining useful life of assets
can be estimated by correlating data trends
and patterns with historical outcomes.
Another strategy for interpreting
machine health data is to use published
ISO standards. Opponents of this method

contend that ISO standards cannot possibly be relevant to the spectrum of PdM
applications found throughout the industrial world. The reality lies in between. ISO
standards are a good indicator of overall
machine health but should be combined
with asset manufacturer expertise to drive
down specific repair recommendations.

IS DIRECT DATA ENOUGH?
So far, we have only discussed data collected
directly from the asset in question. There
are many other inputs that are relevant to
machine performance. Consider the effects
of environmental factors such as ambient
air temperature, humidity, and atmospheric
pressure. Additionally, the health and performance of auxiliary equipment associated
with the asset in question will have an effect.
For instance, a misalignment at one end
of a drive shaft could produce elevated
vibration at the motor on the other end. As
PdM solutions become more advanced, they
will begin to incorporate these indirect data

MAY 2020

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Efficient Plant May 2020

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