The main objectives of this project are to develop a model-based prognostics method integrating the FMECA and PRM approaches for the smart prediction of equipment condition, a novel MDSS tool for smart industries maintenance strategy determination and resource management integrating ERP support, and the introduction of an MSP tool to share information between involved personnel. The proposers' approach is able to improve overall business effectiveness with respect to the following perspectives: • Increasing Availability and then Overall Equipment Effectiveness through increasing of MTBF, and reduction of MTTR and MDT. • Continuously monitoring the criticality of system components by performing/updating the FMECA analysis at first implementation or whenever a variation in the system design or composition occurs. • Building physical-based models of the components which have a higher criticality level or which status is difficult to monitor. • Determining an optimal strategy for the maintenance activities. • Creating a new schedule for the production activities that will optimize the overall system performance through a Smart Scheduling tool ensuring collaboration among the MDSS, the ERP and the RUL Estimation tool. • Providing, in addition to traditional data acquisition and management functions in a machine condition monitoring system, robust and customizable data analysis services by a cloud-based platform. • An Intra Factory Information Service will be developed to allow the company staff to be quickly informed of changes in the machine tool performances and to easily react to eventual production and maintenance activities rescheduling. The production and maintenance schedule of complete production lines and entire plants will run with real-time flexibility in order to perform at the required level of efficiency, optimize resources and plan repair interventions.

COGNIPLANT project will develop and demonstrate an innovative approach for the advanced digitization and intelligent management of the process industries. This approach will be based on a novel vision to data monitoring and analysis, that will make the most of the latest developments on advanced analytics and cognitive reasoning, coupled with a disruptive use of the Digital Twin concept to improve Production plants’ operation performance by up to 68% in real time control of the productive environment, 65% in quality control of the final products and 70 % in response time to uncontrolled incidents. The concept will be implemented by four end-users from four different SPIRE industries, one chemical industry in Austria, one aluminum refinery in Ireland, one concrete manufacturing industry in Italy and one metal industry in Spain. The COGNIPLANT solution will provide a hierarchical monitoring and supervisory control that will give a comprehensive vision of the plants’ production performance as well as the energy and resource consumption. Advanced data analytics will be applied to extract valuable information from the data collected about the processes and their effect on the production plant’s overall performance enabling to design and simulate operation plans in digital twin models based on the conclusions. As a result, optimal operation plans will be obtained that will improve the performance of those cognitive production plants. In addition, the project will demonstrate the positive impact derived from the implementation of COGNIPLANT solution that will allow industries reducing their CO2 emissions up to 20%. A training strategy will be designed to provide a comprehensive framework for the dissemination of the project outcomes and a clear understanding of the new solution for the employees of the SPIRE sectors.

Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15% of their total maintenance costs on predictive (vs reactive or preventative) maintenance. The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%. The platform includes 4 modules: 1) a data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components, 2) an artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health condition and supporting a large range of assets and dynamic operating conditions, 3) a secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities and 4) a human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks. The consortium includes 3 end-user factories, 3 machine-tool suppliers, 1 leading component supplier, 4 innovative SMEs, 3 research organizations and 3 academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.

Manufacturing industries continuously face the challenge of delivering high-quality products under high production rates while minimizing non value-adding activities. The recent COVID-19 pandemic is causing manufacturers to rethink and reassess their global supply chains and the flexibility of their production sites. Resilience means the ability to withstand difficult situations, while flexibility can be considered as the ability to accommodate changes without incurring significant extra costs. Production processes demanding high human skill such as forming processes, requires readjustment of the process parameters of all production steps as a new product evolves. The deficiencies can be attributed largely to the lack of efficient ways for trusted data sharing among the stakeholders without interoperability barriers. There is a need to be able to determine when such changes lead to deterministic-chaotic behavior with far reaching consequences. FLEX4RES provides an open platform to support production networks' reconfiguration for resilient manufacturing value chains. FLEX4RES will utilize platform-based manufacturing that builds on the state-of-the-art Gaia-X and IDS technologies for data-sharing in the horizontal supply chain and the Asset Administration Shell (AAS) that is to implement intra-factory reconfiguration practices. FLEX4RES considers the Digital Twin of the value-adding network a key enabling technology to achieve reconfiguration processes in highly flexible production systems and networks. The key element of technology linkage is represented by the Self-Descriptions with linked, standardized information models, especially in terms of resilience. The developed platform and specialized hardware aim to improve the existing industry-established lean management approaches related to Reconfiguration Management through the digitalization of the production, characterized as Industry4.0, by allowing for the information sharing between value chain stakeholders.

The more complex and critical that many systems becoming nowadays, the more they are developed by integrating different (sub-)components, even third-party hardware/software components. With the pressure of time to market, and the ever-evolving security threats, getting more complicated than ever with the breakthroughs in Generative Artificial Intelligence (GenAI), how to enable more secure development and integrations of critical systems to be assured for security? These huge challenges must be addressed to make “secure services, processes and products, as well as to robust digital infrastructures capable to resist and counter cyber-attacks and hybrid threats” as called for in the EC’s Strategic Plan 2021-2024. Addressing exactly these challenges, the overall objective of SECASSURED is to deliver innovative security engineering solutions with (AI-based) security services to achieve novel holistic assurance-driven security engineering, capable of (1) increasing software, hardware and supply chain security by identifying cybersecurity and regulatory risks while integrating (both commercial and open-source) components, even third party ones, (2) providing virtual, secure environments for the automated assessment of system components including AI components and their secure integration, and (3) continuous assurance-driven security engineering with a holistic toolbox of (AI-based) security services. It paves the way of secure continuous system integration of (third-party) components, including AI components, across the computing continuum, and implements EC's evolving security and privacy regulations, as well as the strategy of human-centric AI.