In early May, Israel’s Iron Dome missile defense system intercepted 35% of the 690 rockets and mortar shells launched by Hamas from the Gaza Strip. The system was able to prioritize threats in real-time so effectively that only
three civilians lost their lives.
The Iron Dome uses a “system of systems” approach,
which Col. Natan Barak of the Israel Defense Force helped
develop. In 2003 he retired to commercialize the approach
to managing real-time threats for industry use.
Manufacturers may not face such literal life-and-death decisions as Israel does on a constant basis, but they similarly
rely on thousands of sensors and devices to make tough
choices in an instant. We emailed Barak to fnd exactly how
this technology helps shield the industry from the daily barrage of problems.
NED: Can you describe how you helped develop the
algorithms now used for Iron Dome in the early 2000s and
what the inspiration was?
Natan Barak: During my military service, I led the development of a generic platform that offered visibility across the
full operations of battle ships, submarines, combat planes, helicopters, naval commando, and other supporting systems. This
platform integrated and shared data into a holistic, real-time view
for decision makers, offering maximal fexibility and logic that
adjusts itself to the conditions in real time. The Iron Dome Command and Control system has similar, yet much more complex
tasks —it needs to take into account various and ever-changing
threats, multiple data sources in real time, the location of our
forces and planes, weather, and other conditions of the terrain,
all in real time to deliver immediate decisions and results.
Beyond that, the ability of the platform to handle distributed
and adaptive image building algorithms, along with real-time
interaction plans, are what makes this robust, successful solution of Iron Dome, which we developed together with Rafael.
NED: How did you turn that into mPrest?
NB: The idea behind the mPrest platform is built on the
same foundation as the Iron Dome command and control
system. We wanted to create a platform that could seamlessly
integrate with existing systems, gather extensive, relevant
data from these systems, and use AI-driven analytics to make
decisions in real time. We call it a “System of Systems,” or
orchestration and optimization platform. We provide end-to-end optimization, instead of only a limited, local solution.
Most, if not all, enterprises have multiple siloed systems that
do not communicate with each other. mPrest empowers its
customers to leverage and optimize by connecting all systems
and resources. We believe that if you are not integrated, you
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The tech behind Israel’s Missile
Defense System is ready to defend
smart factories and cities
are not optimized. Our ability to identify patterns and anomalies enables us to offer enterprises optimization similar to
what we created in the defense domain.
In our projects with Netafm, for example, the capability of
our “System of Systems” is demonstrated perfectly. Netafm
has integrated sensors on their irrigation pipes and now has the
ability to analyze the data gathered from the pipes, transforming
them into a big-data company rather than an irrigation company.
Our platform gathers data from multiple sources, including
soil quality, irrigation status, nutrients, weather forecast, etc.,
and makes ongoing decisions and recommendations for farmers
based on pre-defned rules and defnitions, such as when to
water the feld and when it is time to harvest.
NED: So what is being tracked and intercepted in the factory?
NB: There are processes that are not yet fully optimized
in any enterprise and production environment, but new IIo T
connectivity and monitoring capabilities are being leveraged
to offer increased effciency, reduce energy costs, and enable
predictive maintenance to avoid malfunction and breakage.
Essentially, we receive all inputs, make a decision of whether
or not an object represents a danger, select the best interception scenario, and then manage its execution. In most cases,
there are multiple enemy rockets launched. Combining all
relevant systems, analyzing the data, and making a decision
in real time becomes a big data analytics algorithm.
Another way of describing our system is as a real time, distributed
asset management and analytics platform. Once described in these
terms, the connection to industrial applications, and specifcally to
energy management applications, becomes quite evident.
Over recent years, due to decentralization and de-carbon-
ization, there is an exponential growth in distributed clean
energy resources (photo voltaic cells, batteries, electric vehi-
cles, etc.). Current systems are not designed to cope with the
amount of resources, with energy fow being multi-directional,
or with the negative effect on system quality, in parallel to
the good they bring. In other words, energy utilities require a
new product, which is capable of aggregating data from all
the various sources, forecasting needs (supply and demand)
and managing these distributed assets in real time.
NED: Do you have any specifc examples of the system
preventing catastrophic failure, and conversely, in day-to-day
operation, how much power is being saved (or not wasted)?
NB: mPrest’s Asset Health Management and specifcally
our Transformer Health Management are operational systems,
monitoring power transformers 24/7. As such, the very fact that
power transformers are being managed via our application is
reducing critical failure rate signifcantly for companies like
New York Power Authority (NYPA). Our project with NYPA also
won EPRI’s Technology Transfer Award. Of course, many other
transmission utilities have power transformer failures. Implementing mPrest’s product would bring such critical failures to a
halt and save tens of millions of dollars per year per such utility.
A critical failure of a power transformer entails many cost
considerations, including a new power transformer, which
typically takes between 12 and 24 months to replace. During
that period, there is a need to source energy from alternate
sources. As this is an unplanned energy procurement, costs
are signifcantly higher than normal energy procurement.
This can drive a typical total cost of a power transformer to
be anywhere between $10 million to $15 million.
In addition to the above, such a critical failure can result
in deaths, service level interruptions, and negative publicity,
which utilities would want to avoid.
Israel’s Iron Dome air-defense system launches rockets to intercept an
incoming attack. The algorithm behind it can help optimize the IIo T.