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Factories are getting smarter: industrial robots are now commonplace, and IoT tools are tracking the flow of materials, components, and finished products. But there’s one area where many plants still have room to grow: the use of IoT to power predictive maintenance, enabling technicians to service networked machines before they fail.

Imagine you’re running a large facility and your cooling tower starts using more water than usual. Using conventional tools, plant managers might notice that a dial is registering increased water consumption, but they’d then have to walk to the factory to locate the problem and to figure out how to resolve it. In the meantime, water might be leaking into other equipment, causing short circuits or other costly damage.

Now imagine that you get an alert on your smartphone as soon as your cooling tower starts drawing down excessive water. You pull up the app, quickly confirm the usage spike, and instantly get a diagnostic rundown flagging an open valve in a particular area as the most likely cause. The app then walks you through a troubleshooting process customized to your facility’s infrastructure, enabling you to solve the problem before it turns into a crisis.

That’s the power of predictive maintenance. When you leverage the power of industrial IoT (IIoT) and sitewide connectivity, it becomes possible to maximize efficiency, minimize downtime, and keep your facility firing on all cylinders.

Clearly, predictive maintenance is a big upgrade from standard routine or preventive maintenance. Instead of simply following an inspection schedule, predictive maintenance enables complex organizations to get ahead of the curve and fix problems before they start.

This type of maintenance is made possible by low-latency connections, automation, and the same IoT equipment that operators already use to streamline production. By leveraging machine learning and predictive analytics to spot patterns in both current and historical data, it becomes possible to proactively identify warning signs and take timely action.

Through statistical and AI analysis, the torrents of data generated by today’s connected industrial infrastructure can spot errors far earlier and far more reliably than human work crews. A fleet driver might fail to spot a check-engine light, for instance. However, with IoT technologies, a vehicle’s onboard diagnostics system would instantly report the error to the fleet manager, along with clear guidance on whether the vehicle needs to be immediately recalled or if a checkup can wait until the end of the shift.

So how can your business adopt this powerful new maintenance paradigm? Here are 4 things to keep in mind as you build out predictive maintenance capabilities:

If you’re still using manual inspections to track the health of your equipment and machinery, it’s time for a rethink. The Industry 4.0 revolution is about augmenting human perception and intuition with connected sensors and smart tools that make it possible to identify potential problems faster and more reliably.

Some problems will always require in-person diagnostic work, but most of your initial data collection should happen automatically and remotely. That way you can steer your field teams to where they’re most useful while keeping a close watch on the entirety of your plant or facility around the clock.

Industry 4.0 is powered by data, and it’s easy for supervisors or plant managers to get overwhelmed. Don’t try to interpret data yourself — find an analytics platform that can process and learn from the data that’s being generated and connect the dots between incoming performance metrics and required maintenance.

Again, human judgment will still be required. But with the right analytics solution, you can stop worrying about the raw data and spend your time more productively by focusing on the valuable information surfaced by your data-management solution. 

The whole point of industrial IoT is maximum connectivity, with every process and piece of equipment able to communicate seamlessly across the entire value chain. The goal is to ensure horizontal integration across all your tools — both software and hardware — to deliver a single unified network of data that can be leveraged by your predictive maintenance solution.

Getting to that point can be a challenge because it requires products from different vendors to work together seamlessly and intelligently. It’s important to bear this in mind during the procurement process and to make sure you’re building out infrastructure that will support rather than constrain your transition to Industry 4.0. 

One key consideration for IoT applications is how you’ll connect your fleet of devices to one another. Hardwired or WiFi connections can work in some settings, but for many others cellular connectivity is essential. If you’re drilling for oil in a challenging environment, for instance, you’ll need SIM-based connectivity if you want your predictive maintenance system to monitor the health of your extraction machine’s drill-head and prevent breakages. 

The goal is to ensure that IoT solutions stay connected no matter how remote or extreme the conditions they operate in. After all, the machinery that is the hardest for field teams to access is what most urgently needs predictive maintenance capabilities.

Ask any plant manager when the best time is to conduct a repair, and they’ll tell you: right before the machinery breaks down. Fix things too soon, and you’re wasting resources; fix things too late, and you risk knock-on impacts and significant plant-wide downtime.

The difficulty with this advice, of course, is figuring out when that critical moment is — and that’s something that humans aren’t typically able to do with any degree of certainty. By the time a machine starts clanking or juddering to such an extent that a human inspector can spot the problem, the damage is often already done.

Fortunately, in the era of widespread IIoT, there’s a better way. If you keep your machinery connected and learn to leverage the incoming data efficiently, then you can open the door to true predictive maintenance — reducing your maintenance costs and keeping your plant operating at full capacity.

Image Credit: Vitalii Vodolazskyi / Shutterstock.com

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