Increased reliability and reduced downtime
More and more machines are equipped with a variety of sensor data arising during operation. At the same 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 the maintenance window. So you as a service providers are able to manage your fleet more reliable and provide preventively the customer with appropriate spare parts or replacement equipment.
How this this work?
All sensor data is fed into a self-learning system and based on the weighted input factors, a failure probability can be calculated. The current machine parameters are therefore compared to specifications and reliability testing. The resulting model can much faster recognize irregularities than a human considering all factors. You can react accordingly and plan repairs pro-actively.
- Demand-oriented planning of maintenance windows for your customers
- Your reliability building block 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