Maintenance work can be proactively coordinated, failures avoided, and workflows optimized. How? Simply combining the permanent monitoring of machine data, which is called condition monitoring, with our data science know- how.
In the age of digital transformation and the increased machine-to machine communication, industrial plants are usually standard equipped with sensors. Information about temperature or machine vibrations added to performance data, maintenance reports and external data sources create a knowledge base for predictive maintenance.
The use of advanced analytics not only enables us to identify patterns in past data, but also allows us to develop a forecast model. This helps you detecting defective components before any damage is caused on the machine.
The unpredictable breakdown of an industrial plant can lead to supply shortage. Predictive maintenance helps you avoiding those machine breakdowns and improves aligning the production planning with the actual maintenance work required
A machine breakdown causes expensive downtimes and the necessary maintenance produces personnel and material costs. Predictive maintenance enables you to coordinate according to requirements, avoid unnecessary maintenance work and reduce sales losses due to production downtimes.
Improving work processes
A continuous evaluation of machine data gives you a better understanding for your machines. Beyond the early detection of imminent errors, data science allows you to detect operating errors and faulty configurations which improves operating procedures and the output quality.