MACHINERY LUBRICATION- INDIA NOVEMBER-DECEMBER 2019 | Page 7

MLI
Machine- and Human-
Executed Responses
SYSTEM (PLC) CONTROLLER
Feedback &
Parameter
Monitoring Control
Algorithm Action
Response
Sensors &
Transducers
Shared Self-Referencing
Knowledge &
Edge Computing
Shared Machine Control
Actions/Condition
Responses
Condition
Monitoring Condition
Analysis Condition
Response
CONDITION CONTROLLER
Figure 4. The Intelligent Controller-Controller Interface (ICCI) System shares PLC
functions/sensing with machine condition functions/sensing.
MODES OF IIoT DATA-DRIVEN CONDITION RESPONSES
Data-to-Operator
Avoidance
Operating Limit Settings,
Duty Cycle,
Loads, Application,
Operator Handling
Human
Executed
Human-Executed
Remedy
Data-to-Machine
Avoidance
Root Cause
Avoidance
RESPONSE
MODE
Problem
Remediation
Filter Change. Component
Replacement, Fluid Change,
Alignment/Balance Correction
Changes to: Speed, Load,
Acceleration, Temperature, Lubricant
Supply, Auxiliary Filtration
Machine
Executed
Machine-Executed
Remedy
Additive Discharge, Leak Isolation,
Derate Load
Figure 5. This chart shows how the IIoT provides connectivity for both machine and
human executive condition control responses.
that is scanned or keyed into a handheld
device can be augmented by pairing it
with data generated from online condition
monitoring sensors. See Figure 3 for a
simple visual on augmented intelligence.
In real time, this data can dictate machine
control and movement to optimize and
sustain machine health and operating
conditions. These are like guidance
systems that respond to current conditions,
providing adaptive control in response to
instant changes. The state of the machine
is constantly monitored and recalibrated.
Real-time sensing can be shared between
the system controller (like a PLC) and
the condition monitoring controller. This
provides a functional interface enabled
by an IIoT platform for mutual benefit
related to machine performance and
reliability. Machines with autonomous
control features (current or potential) might
include hydraulic systems, compressors,
paper machines, turbines and many
sophisticated process machine trains. The
concept of coupling condition control with
system control is illustrated in Figure 4.
Of course, not everything must be done
in real time. Because of the complexity
of some machines and the technology
limitations of many condition and
operational control functions, both human
and machine responses are needed. The
IIoT and online sensors can supply the data,
while data analytics can translate the data
into prescriptive responses. However, the
manner and time element of the corrective
responses may vary.
This hybrid model probably makes the
most sense, as it is the easiest to deploy. But
this is a dynamic field that will continue
to evolve as technologies advance and
machines become smarter and more agile.
Examples of how humans and machines
can work together are shown in Figure 5.
The Internet of Tribology
Oil is like a flight data recorder. It is
exposed to the intimate innerworkings of
the machine, seeing both the good and bad.
It’s the common medium that records data
from these exposures which might reveal
health or aberrant conditions that can
induce future failure. Decades of research
in tribology and millions of oil analysis
samples have taught us that there’s gold in
our oil. The data that can prescribe needed
actions is this gold. It is detectable and
quantifiable.
The means of data acquisition should not
only be limited but also multimodal. It can
be extracted from samples and analyzed
in the laboratory, monitored in real time
with online sensors, interrogated using
portable data collectors, or examined by
skillful and investigative inspectors. Other
non-lubricant-related tests and inspections
at the machine help complete the picture
and establish greater confidence in what’s
happening now (or not happening).
The IIoT does not and cannot make all
other forms of condition monitoring
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