Our objective is to provide added value bringing competitiveness to the industry, through machine learning-based failure diagnosis, prognosis and energy efficiency actionable insights.
Preventive maintenance
Reducing maintenance costs.
- Dynamic adjustment of maintenance activities
- User guide for maintenance tasks
Condition monitoring
Increasing the productivity and the availability: Machine health self assesment (machine fingerprint).
- Spindle bearing condition
- Tool clamping system
- Ball screw backlash condition
- Rotary axes backlash condition
- Servomotor temperatures / torques
Consumption efficiency
Controlling / Optimizing the utility consumption: Global and per part&model consumption values.
- Electric energy
- Coolant
- Cooling water
- Compressed air
- Aspiration air
Equipment performance
Controlling / Optimizing equipment performance / availability.
- Intrinsic Machine availability
- Production line availability
- Number of produced parts by model/day
- Machine status reports
How Aingura IIoT produces new knowledge?
- Acquiring massive data from multiple sensors and heterogeneous devices, ensuring data quality with advanced sensor fusion strategies.
- Filtering noise data and variable selection to reduce up to 90% of storage and communication infrastructure needs.
- Developing specially tailored machine-learning algorithms for knowledge discovery and perform Real-Time diagnosis and prognosis.
How Can We Help You ?
Get in touch with us.
