BBI4.0

Development of signal processing modules bounded with data-driven Virtual Sensors and novelty/anomaly detection Machine Learning solutions, specifically addressed for the manufacturing industry.

BBI4.0 is a sub-project of the European Factory Platform (EFPF) which proposes to implement novel data-driven Predictive Maintenance (PM) solutions tailored to industrial applications. The proposed objectives and modules extend the EFPF platform functionalities and demonstrate their main capabilities by developing bespoke solutions for pilot use cases.

The specific objectives of the project are:

• Design and develop signal processing and pattern recognition modules and solutions customized to manufacturing sensor data.
• Design, develop and extend novelty/anomaly detection Machine Learning (ML) and Deep Learning (DL) based solutions for PM applications.
• Development of Virtual Sensors (VS) to contribute to a better understanding of the process as well as providing stability to the system.

Building blocks for Predictive Maintenance applications are key tools for improving manufacturing Key Performance Indicators (KPI) such as the Overall Equipment Effectiveness (OEE). Developing powerful, tailored, and user-friendly tools is crucial to make Industry 4.0 a reality.

BBI4.0 project is designed and executed by the Applied Artificial Intelligence (AAI) Unit of Eurecat, which is developing innovative solutions (algorithms, methods, platforms) based on a combination of artificial intelligence and knowledge management technologies, especially for the industrial, energy and sustainability sectors.

The EFPF is a federated smart factory ecosystem and a digital platform that interlinks different stakeholders of the digital manufacturing domain, enabling users to utilise innovative functionalities, experiment with disruptive approaches and develop custom solutions to maximise connectivity, interoperability and efficiency across the supply chains.