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.
Research
Research and development activities (R&D) have always been a strong point of Etxetar. Therefore the company did many investments in this direction during the years. Thanks to its research and development activity, the Company can constantly adjust its products to the customers need and maintain an advanced technological position in its reference productive sector.
Since January 2019 and for 48 months, Etxetar is part of The Zero Defects Manufacturing Platform (ZDMP), activity launched in with an investment of 19M€ provided by the 30 sponsoring companies and the European Commission. Its mission is to develop and establish a digital platform and related Apps for achieving excellence in manufacturing through zero defect processes and products.
ZDMP combines state of the art zero defect technological approaches based on commercial grade or open-source software, with built-in software for any gaps, and with an open development approach and App store. It focuses on both Process and Product quality modules for pre, during, supervisory, and post-production quality issues to ensure manufacturers are enabled for a Zero Defects environment
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825631
ZDMP Newsletter_2021_2
IoTwins is a European project that aims to build a reference architecture for the development of efficient and distributed digital twins for specific manufacturing and facility management domains.
12 dedicated large-scale testbeds will collect large amounts of data to generate and refine the associated digital twins, including optimized models of resources, systems, and processes involved. IoTwins digital twins will be used to improve the efficiency of production processes and of facility management, as well as to demonstrate the replicability of the achieved results in similar scenarios and to determine new application areas and business models.
All the IoTwins testbeds share the same methodology: models that exploit big data and domain expert knowledge to accurately represent a complex system, such as an industrial plant, or a process, or a facility, with the ambition of predicting its temporal evolution and dynamics. The underlying technologies ground on the concept of distributed IoT-/edge- /cloud-enabled hybrid twins.
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