The Industrial Internet of Things Empower Predictive Maintenance

The potential of the Industrial Internet of Things lies in the power of humans and machines working together in a connected environment. Basically, the IIoT applies the principles of IoT in an industrial setting. The main target is to encompass machine learning and Big Data by exploiting machine to machine communication and a connected sensor infrastructure.

How does manufacturing benefit from the rise of the IIoT? It is still the case that most factories have limited computer and sensor connectivity. There is an untapped potential for growth through higher revenues, optimization of the workplace and more unconventional innovation. That’s why the IIoT is not just a buzzword – it is the future.

Hence, in many factories there is still a limited integration between internal systems, for example plant data sources, and external partners. There is also limited intelligence control at the device or plant level. Software is also usually outdated and difficult to upgrade. Businesses need to rethink their business models.

The IIoT infrastructure is a solution that brings a common data model and control architecture. Thus it enables a flow of insights from and to the factory floor, also beneficial for external partners.

One area where the power of the IIoT really shines is predictive maintenance. In its core, predictive maintenance is a real-time monitoring of machine conditions via sensors, connected to a data pool. Connected machines can tell you that they are going to break down before actually breaking down.

In reality, there are various ways to perform maintenance:


  • Corrective Maintenance – Repair it after it breaks. It involves high costs and workers need to work extra hours since machine malfunctions are random events.
  • Planned Maintenance – Repair it before it breaks. This is a scheduled maintenance which is not only an expensive one due to a frequent change of parts but also requires higher labour costs.
  • Predictive Maintenance – Eliminate the root cause, avoid critical failures before they happen. It extends the life of the machine and increases the efficiencies of the workers.

Predictive Maintenance proves to be one of the best examples of the IIoT application. A research done by IoT analytics shows that the market for predictive maintenance applications is to grow from $2.2B in 2017 to $10.9B by 2022, a 39% annual growth rate.

Here is an example of predictive maintenance involving farm equipment:

The tractor case

Travis Senter of Senter Farms has around of 20,000 acres of row crops – cotton, corn, wheat – in the Mississippi River delta. It operates 23 tractors, 3 combines, 2 cotton-pickers and 4 sprayers linked to an agricultural IoT system. The above mentioned machines need to be fully available during the busy season from March to April.

“We need this technology to be able to track and see where things are. And if there’s something going on, we need to make sure we can fix it in a hurry because you can’t afford downtime,” said Senter. “You’ve got your back against the wall every day with weather, with timing, with planting.”

The data received from the connected machines is analyzed to draw patterns about reliability. The IIoT investment paid out quickly. There was a vibration detected which turned out to be a sign of impending failure on 10 of the 13 tractors. The repair was done in a cost-effective way, instead of replacing the entire drive and causing fatal downtime.

It is clear that downtime is of utter importance, causing critical losses in cases like the above one. The IIoT provide endless possibilities to streamline manufacturing processes and improve predictive maintenance.

How to improve predictive maintenance with a smartwatch – the IIoT in manufacturing

Predictive maintenance aims at eliminating root causes of incidents and tries to avoid critical failures before they occur. To boost the efficiency of maintenance staff even further, smartwatches can be used to increase predictive maintenance workflows:

  • Overcome paper documentation – Paper is a static medium and still widely used for maintenance processes. Digital standard repair procedures can increase process adherence
  • Shorten process running times – the overall completion time of a repair task can be shortened with digital repair instructions.
  • Improcess process flexibility – Long waiting times between tasks can be minimized. Finishing a task and waiting for a colleague to perform the machine changeover is not only annoying but also costs time. By using a smartwatch, tasks can be interchanged between workers.

In manufacturing, it is often the case that equipment becomes unavailable due to production changes that must be performed while equipment is stopped. The machine changeover time becomes a hurdle especially when dealing with high-complex machines. Long changeover time means waste of time and money. The IIoT in manufacturing become a solution for this common problem.

Reaching 81% reduction of machine repair time can be done in the following way with WORKERBASE:


  • No waiting times between tasks – Tasks are executed in parallel.
  • Shorter process running times – Each workflow step is analyzed and optimized.
  • Paperless Maintenance Procedure – All instructions are sent on a wearable device and fully digitalized.

Source of the tractor example: NetworkWorld