How to Solve Problems Before they Happen with Industry 4.0

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Industry 4.0 is upon us: A manufacturing regime characterized by smart production systems powered by data and artificial intelligence, and putting to operate for example transformational technologies as robotics, augmented reality, and, perhaps most importantly, the Internet of Things (IoT).

Similar to its antecedents, this fourth industrial revolution — driven by info-tech, as the first three were driven by steam, electricity, and the first generations of computing, respectively — could lead to a radically faster and more efficient way to make things. It will also leave our planet cleaner, and our economies more sustainable.

And if Industry 4.0 will take years to fully mature, a number of its startling innovations are already online. Take, as one example, the crucial matter of equipment and infrastructure maintenance.

For generations, that maintenance has been a reactive activity, as it wasn’t akin to divining the future. Managers might scramble to catch up with developments that had already happened; or armed with data (in amounts and varieties that would strike us as quaint today), they may posit every time a piece of gear was likely to require repair or replacement.

But while age is a factor in equipment health, an industrial facility’s environmental conditions and the way it sets machinery to use matter just as much, if not more. Studies have discovered that 82 percent of equipment failures follow no discernible age-related pattern.

That typically means that when factories replace parts only according to a schedule, they tend to drop money. 1 study discovered that 30 percent of preventative maintenance activities took place over-frequently.

And then there’s the unplanned downtime that occurs when equipment problems grind a plant to a halt. That downtime costs industrial manufacturers an estimated $50 billion a year.

The Value of the New Maintenance Regime

The Industrial IoT, happily, has given facilities the power to anticipate maintenance issues before they occur. Networks of sensors integrated into industrial equipment pick up a multitude of data: the temperature at which a turbine is operating, the rotations per unit of time of a flywheel, the depth of a bearing as it wears away during use.

That information feeds to the cloud, where it is aggregated and analyzed, helping managers and engineers who access the platform via simple interfaces (maybe their smartphones) make decisions about how to proceed.

If not, as is increasingly the case, the information doesn’t ascend into the cloud, but undergoes analysis at the”border” — in distributed nodes close to the stage of interaction with the surroundings. By cutting out the need to push huge amounts of data cloudward, advantage computing makes for a faster, lighter, more supple system.

Edge-reliant or not, a predictive maintenance procedure might, upon picking up on a problem, automatically summon repair technicians who arrive onsite with the advantage of knowing in advance what the issue is and what materials and resources they’ll need to fix it.

In the future, these technicians are increasingly likely to be robotic, making maintenance even more efficient. But now, an IoT-based predictive maintenance system can increase uptime by 10 to 20 percent and reduce maintenance costs by 5 to 10 percent. 

Perhaps just as importantly, such a system can also reduce by 20 to 50 percent the time required to plan maintenance, thereby streamlining enterprise bureaucracy.

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