Downtimes or disturbances of high bay warehouses, machines and conveyances can stop your entire production and Supply Chain. In order to keep downtimes at a minimum, the topic maintenance is crucial. Even better, of course, would be to eliminate disturbances at all: the solution is Predictive Maintenance.

By means of Machine Learning and digital twins, causes of wear are identified early and ideal maintenance measures and - times are calculated. You benefit from continuously operating plants and reduce your maintenance, inspections and service costs at the same time. We support you in developing tailor-made IT solutions which perfectly suit your processes.

 

What is Predictive Maintenance?

Predictive Maintenance defines a maintenance strategy which aims to completely avoid production downtimes and unscheduled waiting times while guaranteeing operational reliability.

Maintenance strategies

at one glance

Damage-dependent, corrective or reactive maintenance

This strategy applies maintenance measures only in case of occurred failures, which can easily lead to a complete plant downtime.

Preventive maintenance

This strategy specifies fixed and frequent maintenance intervals. Downtimes are reduced, at the same time, maintenance intervals are often shorter than necessary.

Condition- related maintenance

As the name suggests, maintenance services are performed in relation to the condition of plants or machines. To do this a precise, sensor-based monitoring is required.

Predictive Maintenance

For the predictive maintenance, a maintenance model is trained or calculated based on historical condition data. This model can predict future developments from newly added, current data. Errors or disturbances can be avoided prior to their occurrence.

Maintenance strategies at one glance

Damage-dependent, corrective or reactive maintenance

This strategy applies maintenance measures only in case of occurred failures, which can easily lead to a complete plant downtime.

Preventive maintenance

This strategy specifies fixed and frequent maintenance intervals. Downtimes are reduced, at the same time, maintenance intervals are often shorter than necessary.

Condition- related maintenance

As the name suggests, maintenance services are performed in relation to the condition of plants or machines. To do this a precise, sensor-based monitoring is required.

Predictive Maintenance

For the predictive maintenance, a maintenance model is trained or calculated based on historical condition data. This model can predict future developments from newly added, current data. Errors or disturbances can be avoided prior to their occurrence.

Advantage of Predictive Maintenance

Traditionally plant maintenance moves between two opposing states. Enterprises either save costs for operating resources and working time by applying maintenance measures as rarely as possible. In this case, they risk unexpected waiting times and massive disturbances.

Or they minimize this risk, but have to apply maintenance measures in much shorter intervals in order to make sure that they are carried out on time. As a result, they have to invest more in resources and time.

Predictive Maintenance has the goal to make the best of both alternatives a reality: to minimize maintenance measures while guaranteeing reliably operating machines and plants.

How to profit from Predictive Maintenance

  • Minimized downtimes or malfunctions
  • Cost reduction for spare parts and operating resources to a minimum
  • Fewer performance of maintenance measures, minimum time consumption
  • Servicing times can be scheduled optimally without having an impact on the ongoing business
  • Improved machine - and plant performance
  • Reliable and prompt overview of machine conditions by visualizations and alarm functions
  • Optimized maintenance through the consideration of additional factors such as shift schedules, electric loads or scheduled waiting times in your planning

How does Predictive Maintenance work?

Basis of all Predictive Maintenance projects is the precise information about machine or plant conditions. This is why machine - and process data is collected, based on which, predictive models are then developed. Since an exact image of the according machine or plant has to be generated from data, data is collected as long as possible and from as many sources as possible.

Data is then evaluated and calculation models are defined by means of Data Science. These data models are able to predict probable interferences. With Machine Learning an algorithm is trained to recognize patterns in data sets and to determine correlations.

When does Predictive Maintenance get profitable?

A successful predictive maintenance generates added value from your data through a sustainably optimized maintenance planning.

The challenge of data integration

A precise disturbance prediction requires the underlying data set to be as complete as possible. Which means, data has to be collected across all relevant factors.

The solution: sensorics

Plants that are not ready jet have to be equipped with sensors accordingly and interfaces for the collection of data have to be implemented.

The challenge of process expertise

Due to the diversity and individuality of processes, the interpretation of data should not be underestimated. The more expertise of processes is available, the better relevant correlations between plant data and production processes are identified.

The solution : expert know-how

Close cooperation with the according special department and domain experts, is crucial. Their detailed knowledge and expertise concerning machines and production processes is vital and enables a better recognition of connections.

The challenge of significance

The best quality of predictions is reached when different disturbances can be analyzed. However, a plant or machine with rare disturbances or downtimes does not offer a lot of room for the prediction of exceptional occurrences.

The solution: experience

The expertise of special departments is particularly important here. Their resources of experience is crucial for predictions. At best, rather thinly available facts can be enhanced by their expert knowledge.


In order to get the best out of machine- and process data, it is often useful to approach Predictive Maintenance as part of a larger digitalization project. In the course of automatization efforts and process monitoring, more and more plants are equipped with sensors and digital twins are created across entire processes. All of this forms a solid basis to also use collected data for the creation of predictive models. In the context of Industry 4.0 and the Industrial Internet of Things (IIoT) enterprises can now profit holistically and gain relevant added value.

 

IT-solutions for Predictive Maintenance

With Predictive Maintenance you benefit from more effective maintenance measures and reduced costs. We are on your side to find a suitable solution for your processes. Thanks to our long-term experience in the fields of production processes we have the necessary process expertise to develop suitable solutions for the Predictive Maintenance of your systems.

You receive solutions of vast value for your special department due to clear visualizations and alarm functions. This is how you can efficiently optimize your maintenance planning, reduce maintenance and servicing costs while profiting from reliably operating systems.

We will take care of the entire process from data integration on the shop floor to data analysis and productive deployment. We also provide advice on suitable system architectures. We corporate closely with the specialty department over the course of the entire project in order to ensure that your Predictive Maintenance solution optimally suits your processes. This way, we ensure that you generate real added value from your data and improve your processes.

We will take care of your journey to digitalization.

Contact us