Predicitive Maintenance in the Industrial IoT

Shutting down machines, maintaining them and replacing parts even before a failure occurs? That may have been unthinkable just a few years ago, but in Industry 4.0 this future scenario is already present.

Predictive maintenance in the IIoT (the Industrial Internet of Things) allows companies to combine sensor measurements, Big Data, and anticipatory analytics to detect weak points in production before they even become weak points.

This means fewer costly downtime or last-second repairs - find out how in this overview.

What is Predictive Maintenance?

Although the terms Predictive and Preventive Maintenance are often used interchangeably, there are key differences. Above all, the link with the Industrial Internet of Things shows how important predictive maintenance can still become in the coming years.

Preventive maintenance refers to a method of maintenance in which machines are serviced at regular intervals. This is often done independently of their actual use and is therefore often out of proportion to the wear and tear of a machine.

Predictive maintenance, on the other hand, is based on the sensor technology installed in a machine and only when these sensors report that maintenance is required is this carried out. This is doubly economical, as unnecessary maintenance costs are avoided, but the technology also warns of potential failures in good time.

However, the difference between predictive and condition-based maintenance is also essential, as both maintenance methods are based on the measurement performance of sensors. Condition-Based Maintenance in IIoT means that measurements are taken in real time and based on current data like temperature, pressure, etc. Maintenance decisions are made.

Condition-Based therefore reports an actual condition based on which a machine is maintained or not. Predictive maintenance not only uses current measured values, but also uses the history of measured values over any period of time. Based on past experience, predictions are made about the condition of the future. Machines therefore do not have to exceed certain threshold values before intervention takes place.

A major advantage to Condition-Based Maintenance is that it results in fewer breakdowns and maintenance bottlenecks. If necessary, machines running in parallel may have to be maintained at the same time due to similar operating conditions and times if only current measured values are used.

Predictive maintenance, on the other hand, allows maintenance teams to anticipate upcoming inspections and repairs and intervene in good time.

Predictive maintenance offers benefits for man and machine in the IIoT

The Industrial Internet of Things allows companies to work faster, more efficiently and, in many respects, more economically, and you predictive maintenance is just one part of it.

In concrete terms, predictive maintenance can help reduce machine downtime. Maintenance, especially unscheduled ones, can cause significant downtime in plants. In the worst case, the machine or production may even come to a standstill and require costly and time-consuming repairs. While maintenance-related downtime cannot be completely eliminated even with predictive maintenance, at least times can be reduced and better scheduled.

This, of course, also has a positive impact on the productivity of the plant and all stakeholders, as machines can run at high performance for longer and can be scheduled around maintenance times. The use of smart IIoT processes also makes maintenance easier to plan and costs easier to calculate.

On the human side, safety must not be ignored, of course, as a machine failure or accident always poses a tangible risk to the workers*. Even in the most benign case of a harmless break in operation, your company will have to bear considerable costs and make up for the lost work, which places increased burdens on workers*.

Predictive maintenance allows you to anticipate failures and damage even before critical thresholds are exceeded, remove workers* from the machines, shut them down and have them checked and, if necessary, repaired by service teams. This makes your day-to-day work easier to plan, more economical and safer.

Implementing IIoT and Predictive Maintenance in Practice

But of course Predictive Maintenance also needs the right technologies to be put into practice. One of these technologies comes from Germany - with toii®, thyssenkrupp offers a software platform that allows companies of all sizes to take advantage of the IIoT.

In doing so, toii® is a versatile platform for Industry 4.0, with the help of which more transparency in production can be created through the intelligent collection of resilient data directly at the neuralgic points of an operation.

In production, toii® is above all flexible and allows you to use it in a way that meets your requirements thanks to its modularity. The Think module allows the algorithmic application of data calculations to make predictive maintenance possible in practice, while other modules digitize the working status of analog machines, for example, or control machines automatically. In sum, this creates the perfect basis for integrating the IIoT into existing halls as well and enabling the predictive form of maintenance and analysis.

Looking to the future for an advantage in the present

If you want to gain a competitive advantage, then you need to explore all the possibilities in Industry 4.0 that will enable you to work more efficiently and economically. Meanwhile, it should be well known that the smart IIoT offers many opportunities here. But predictive maintenance is something special even for companies that already rely on sophisticated sensor technology and Big Data.

Using existing and growing data sets, sensor data to predict damage and failures can significantly simplify the use of even more complex systems. And that's where the biggest plus lies: making the unpredictable a bit more predictable and preventing failures before they happen.