Empowering Industry 4.0: Predictive AI Analytics for Component Self-Diagnostics
The relentless progress of Industry 4.0 is reshaping the manufacturing landscape, with AI-driven innovations leading the way. In the Deliverable D2.3 ‘Predictive AI analytics for component self-diagnostics’, the AI-PROFICIENT team focuses on the strides made within Work Package 2 (WP2) – Smart Components and Local AI at System Edge. In this blog post, we unveil the essence of this deliverable, which focuses on predictive AI analytics for component self-diagnostics.
In specific, the Deliverable D2.3 takes us into the heart of WP2’s endeavors, where cutting-edge technologies are harnessed to develop edge systems that diagnose the very assets they are integrated within or control. This deliverable is a testament to the project’s commitment to transforming asset management through state-of-the-art diagnostics.
In the context of Industry 4.0, diagnosis services emerge as a pivotal element. These services ‘can be used by higher level systems to optimize asset operation in coordination with other assets; or, by other edge systems that could modify their controls adapting them to the current condition of the controlled asset’.
Within this deliverable, Kerman Lopez de Calle (TEK), Alejandro Muro (TEK), and Alexandre Voisin (UL) aim to disseminate various diagnosis strategies employed for asset monitoring. In addition, the AI-PROFICIENT team presents different use cases were AI based diagnostic technologies have been validated, together with the corresponding technical dissemination of these advances.