Too much maintenance?

When do you need to carry out maintenance on your system? Fixed intervals based on experience are often adhered to. However, this means you are wasting potential in terms of operating expenses and safety. The “dynamic process maintenance” of cross-ING provides a remedy here.

Preventive strategies are traditionally widespread in maintenance. In contrast to “repair-after-damage”, the aim here is to avoid unexpected damage and the associated costs and downtime.

“Preventive maintenance” is the classic, time-based approach. Maintenance takes place at fixed intervals. The advantage here is that it is easy to plan. On the other hand, the costs are high, as maintenance is carried out regardless of the state of wear.

“Condition-based maintenance” is therefore based on the condition of the machine and not on fixed time intervals. This condition can be guaranteed by regular inspections. This approach is very labor-intensive. The condition for this type of maintenance can also be permanently guaranteed by placed sensors, so-called condition monitoring.

However, if this data is also stored systematically, the potential of the treasure trove of data is not utilized in condition-based maintenance.

From condition to prognosis

This is where predictive maintenance comes into play. The data collected can be used not only to record the current condition, but also to make a forecast about the future. This means that maintenance can be planned for the future based on the wear and tear of the system. This opens up entirely new possibilities for maintenance.

Because condition monitoring and the collected data can be used to detect faults in systems, it can determine the “time to failure”. The size of your data set increases the informative value here.

If data is also available on malfunctions and downtimes, it can be used to make a more reliable forecast for the future. It is important to identify the correct parameters that document the condition and wear of the system.

Crucial: data and models

Two points are crucial for a correct prognosis: collecting the correct data and interpreting it correctly. cross-ING is here to help you with both of these challenges.

We offer a comprehensive analysis of your process engineering process and support you in collecting and storing the relevant data for the long term. This analysis also includes a

scientific analysis of the process so that a more in-depth understanding can be developed.

Building on this, we deploy predictive maintenance for you. We either develop a model based on our analysis of the process or train a model using machine learning with the data for AI-based identification of maintenance requirements.

Reduce maintenance expenses and avoid system failures: With cross-ING you can achieve these goals!

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