These activities are framed in the circular economy model with purpose to retain the value of products and materials, which, together with health status monitoring using machine learning and optimization constitute key elements for the re-use and extension of the useful life of industrial equipment.
❖ Big data analytics, predictive analytics, and optimisation models using deep learning techniques, and digital twin models.
❖ Models for informed decision about whether to refurbish, remanufacture, upgrade, or repair machinery that is towards its end-of-life.
❖ Technologies and strategies to support a new paradigm for refurbishment and remanufacturing of industrial equipment in factories.
❖ New concepts and strategies for repair and equipment upgrade and factory layouts’ redesign.
❖ Optimal refurbishment and re-manufacturing of electromechanical machines and robotics systems.
❖ IoT sensors, novel prediction, and process optimisation techniques to offer machine lifetime extension.
❖ Innovative fog computing and augmented reality techniques combined with enhanced health monitoring and failure inspection and diagnosis.
❖ Approaches for the servicing and upgrading of legacy equipment
❖ Evaluation and demonstration of approaches in real industrial environments.
