Maintenance intervals and fleet management
More and more, machines are equipped with a variety of sensor data arising during operation. At the same time, reliability data from many components are available by the component manufacturers. We use this information to detect irregularities during normal operation and can thus preventively recommend maintenance windows. So you as a service providers are able to manage your fleet more reliable and preventively provide the customer with appropriate spare parts or replacement equipment.
How does this work?
All sensor data is fed into a self-learning system and based on weighted input factors. This enables you to calculate failure probability. Current machine parameters are therefore compared to specifications and reliability testing. Considering all these factors, the resulting model can recognize irregularities much faster than a human.
- Demand-oriented planning of maintenance windows for your customers
- Improved reliability for your fleet management as a service provider
- Reduction of unplanned outages
- Avoidance of consequential damages
- Reduction of stress-induced energy consumption
- Increased system reliability