Maintworld Magazine 4/2020

INDUSTRIAL INTERNET
https://sense.com
The Sense cloud database uses this current pattern to identify the device as a washing
machine, and presents the result back to the user for confirmation. Below is the
waveform pattern for a typical furnace start up. We can see how the pattern has a
unique signature that can be used to identify the asset.
In addition,
once the
pattern for
a normal
furnace is
established,
a pattern for
a furnace
beginning to
experience
a failure
mode could
be identified.
The example
below shows a
failed ignition
sequence.
between IT and Maintenance, the roles
will get closer. We already have networks,
interconnected equipment, PLCs, CNC,
and robots that communicate to each
other. The addition of smart sensors and
dedicated wireless and wired networks
to communicate data will bring us closer
to the IT department, either in the need
to partner, or overlapping roles. I have
often seen the parallel between our maintenance
organizations’ workflow and the
IT departments’ workflow. The processes,
not the technology, are very similar. Both
have routine maintenance of the assets,
equipment upgrades, and emergency work
requests. I think we will continue to see
increased partnership with the IT department.
The next impact, and this is very positive,
is that we will see earlier detection
of failure through the use of pattern
recognition. In my recent SMRP presentation,
I used two home-based digital
automation devices to help the attendees
understand how pattern recognition can
be used to identify early failure. Both
Sense Energy (electrical current) and
Phyn (water pressure) use wave forms to
detect equipment in the home and can be
used to identify failures early in the P to
F curve. The Sense device installs in your
house electrical panel and uses two current
transformers, clamped around your
main incoming feed wires, to measure
electrical current at very sensitive levels.
What has been revealing for me, in my
journey to understand pattern recognition
in industry, is understanding how Sense
captures a wave form, compares that
waveform to a cloud database of equipment
(stoves, washers, microwaves, refrigerators…)
and accurately identifies the
equipment. It then presents me with an
educated guess and asks for confirmation
of the information.
So, it is easy to see how this could move
our detection of failures “up the P to F
curve” using pattern recognition. If technology
such as Sense and Phyn can detect
equipment using current, temperature,
and pressure, then we can use similar
technology to detect normal and abnormal
patterns in our industrial applications. We
could detect faults, such as motor overcurrent,
excessive fluid leakage, or overtemperature.
In conclusion, while I believe there are
other impacts of the transformation happening
around us, the skills, IT support,
and earlier failure detection are impacts
we are likely to see relatively soon.
4/2020 maintworld 33