Driving a Software-Defined Machine

The concept of ‘software-defined machine’ is still a bit abstract for many in the industrial space.  Software for control or optimization of assets is not new, of course.  And opportunities to improve asset performance by monitoring machine health with software are increasingly part of the dialog.  But ‘software-defined’ remains elusive.  What if it were possible to have a machine that could do something tomorrow that it’s not capable of doing today?  What if a software update added new machine behaviors that an operator could opt to use?  That’s exactly what happened a few months ago when Tesla Version 7.0 software  for Model S was released.  Suddenly, the car had Autopilot.  By incorporating a range of new active safety and convenience features that work in conjunction with the automated driving capabilities already offered in Model S, Autopilot allows Model S to steer within a lane, change lanes with the simple tap of a turn signal, and manage speed by using active, traffic-aware cruise control. Digital control of motors, brakes, and steering helps avoid collisions from the front and sides, as well as preventing the car from wandering off the road. The car can also scan for a parking space, alert you when one is available, and parallel park on command. software This was possible because the car’s design incorporates software and an over-the-air mechanism for updating the software.  But the functionality was not an afterthought.  Tesla views developing and refining technologies to enable self-driving capability as a core part of its mission.  A year before the release of the Autopilot software, they started  equipping Model S with the needed hardware: a forward radar, a forward-looking camera, 12 long-range ultrasonic sensors positioned to sense 16 feet around the car in every direction at all speeds, and a high-precision digitally-controlled electric assist braking system.  As with industrial assets, the car’s behavior has critical and fundamental safety and process implications for the operator and others.  The Tesla Autopilot example isn’t a perfect analogy for software defined industrial machines, but there are important lessons nonetheless.  The future vision of the machine’s performance must be articulated in the machine design phase, so basic technologies can be incorporated.  Software control of mechanical subsystems should be extensive.  Appropriate machine environment and machine performance sensors must be anticipated and included, along with intelligence, communications, and security.   New operator interfaces may be required when new capabilities appear.  We’re not yet at the point where the future direction of an asset’s performance capabilities drive purchasing behaviors – but we’re on the road to it.  Got a better example of a software-defined industrial machine?  Let me know!  Reprinted with permission, original blog was posted here  About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.  For further information or to provide feedback on this article, please contact

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