Design_World_-_Internet_of_Things_Handbook_April_2020

DESIGN WORLD — EE NETWORK 27

PREVENTIVE VS PREDICTIVE
One of the biggest challenges to predictive
maintenance adoption has been the fact
that many industry sectors are still working
their way through the implementation of
preventive maintenance systems. Arguably
the forerunner of predictive, preventive
maintenance systems can range from quite
simplistic, such as a ‘traffi c light’ health system
for individual machines or plant elements,
to far more complex networks of sensors
feeding data back to centralized dashboards.
However, it generally relies on manufacturer
lifetime predictions, human operators or
direct sensor data to highlight potential
problems, rather than use complex algorithms
to predict maintenance schedules.
This means that the benefi ts of preventive
maintenance are becoming well-entrenched,
but the staged adoption has left many
industrial players waiting for the machine
learning and AI market to mature further,
easing adoption pains, and lowering costs.

FOOD FOR THOUGHT
The current situation has created a range of
opportunities, such as in the food industry.
One example is the Mitsubishi Electric Smart
Condition Monitoring (SCM) system that slots
neatly into the niche between “traffi c light”
preventive systems and full-fat predictive
IIoT. The system monitors the condition of
individual assets but layers these to provide
a holistic picture of overall plant health.
Local preventive systems still provide visual
‘health check’ indicators, but real-time data
are transferred over Ethernet to a PLC for in-
depth monitoring and cloud-based analysis.
A teach function ‘learns’ the normal operating
state of the machine, then vital information
such as bearing defect detection, imbalance,
misalignment, temperature measurement, lack
of lubricant, cavitation detection, phase failure
recognition and resonance frequency detection
can be viewed in a cloud dashboard.

IMPROVING TRANSPORT EFFICIENCY 
There are certainly clear indications that
predictive maintenance is still front of mind in
many sectors, such as the transport industry.
One example is trackside maintenance, a
signifi cant operating cost for rail fi rms that
also requires qualifi ed personnel to operate
around the clock in potentially dangerous

PREVENTIVE MAINTENANCE

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