Our primary goal was to show we could leverage cloud-based machine learn-
ing to train analytical models that could then be run locally at the edge (simu-
lating a factory floor and control center).
The showcased models we built were designed to look for three things:
• Fire
• Smoke
• Legacy analog gauge monitoring
Since having real fire and smoke on the expo hall floor would have been frowned
upon, we simulated them using a very real looking LED lamp and a misting
humidifier. What actually was real, however, was our ability to monitor an ana-
log gauge, and this seemingly mundane capability resonated broadly with expo
attendees.
Many of those folks have legacy industrial systems in place, some of them
decades old, that are simply too risky to change or too inaccessible to feasibly
retrofit and instrument with modern sensors. An in very practical terms, it is
simply too difficult for a human to watch and monitor these gauges. However,
video analytics can do so continuously, without distractions, or needing to
sleep, or take bathroom and coffee breaks.
Training the video analytics to monitor a gauge and watch for anomalies, such
as “red line” events, proved to be a remarkably useful capability. Furthermore,
it was possible for us to “derive” actual numerical data (temperature, RPM,
pressure, etc.), by converting the video image of the gauge needle into an ana-
log/digital equivalent. How fast is that motor running? Is it 15% past critical?
Has the pressure risen or dropped to unsafe levels? These questions and more
can all be answered without modern sensors. Pretty useful stuff indeed!
Building the Demo
Let us take a look at the architecture and solutions components involved in our
demo.
The key to making use of machine learning for the video analytics in this demo,
without a team of data scientists, was utilizing an approach called transfer
learning, which starts with a pre-trained model and uses it to extract image
features to train a new graph 1 . The pre-trained model for this solution is called
14 | THE DOPPLER | SUMMER 2018