Increased reliability and reduced downtime
Nowadays, many machines are equipped with a variety of sensors, which continously gather data during operation. At the same reliability data from many components are provided by 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 preventively provide 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 weighted input factors, thus probability of failure can be calculated. The current machine parameters are therefore compared to specifications and reliability testing. Considering all these factors, the resulting model recognizes irregularities much faster than a human. You can react accordingly and pro-actively plan repairs.
- Demand-oriented planning of maintenance windows for your customers
- Reliable fleet management as a service provider
- Reduction of unplanned outages
- Avoidance of consequential damages
- Reduction of stress-induced energy consumption
- Increased system reliability