Project Information :
Project Overview
PREDICTUS is an industrial research and innovation project focused on the evolution of the KNOW4I platform from Technology Readiness Level (TRL) 6 to TRL 8. The project aims to enhance predictive maintenance capabilities in industrial environments through the integration of advanced Artificial Intelligence (AI), Machine Learning (ML), real-time data acquisition systems, and interoperable industrial communication frameworks.
The project is centred around the deployment and validation of the KNOW4I platform within real operational environments. Through the integration of industrial sensors, PLCs, ERP systems, and FIWARE-based interoperability services, PREDICTUS aims to deliver a scalable and production-ready predictive maintenance platform capable of supporting smart manufacturing environments.
The project combines expertise in industrial systems integration, AI-driven predictive analytics, data interoperability, and operational validation to ensure that KNOW4I can be effectively adopted in real industrial contexts.
Our Role & Contribution
At INNOVA, we contribute to PREDICTUS by ensuring that the evolution of the KNOW4I platform is guided by clear operational requirements, real industrial needs, and a structured validation pathway towards TRL 8.
Within the project, INNOVA leads activities dedicated to updating the technical and functional requirements of the KNOW4I platform and to defining the industrial case study that will support its validation in a real operational environment. This includes analysing the current TRL 6 prototype, identifying the additional requirements needed to reach TRL 8, and translating them into clear specifications for the next development phases.
A central part of our work is the definition of the case study in which KNOW4I will be tested. In this context, INNOVA supports the identification of relevant production processes, critical equipment, data sources and operational constraints, helping to establish the baseline for testing and validating the upgraded platform.
Our contribution includes:
- Coordinating the definition of the industrial case study for the validation of KNOW4I at TRL 8;
- Supporting the review of the platform architecture, ensuring that the technical evolution remains aligned with the updated requirements and industrial use case;
- Contributing to the identification of relevant data inputs, predictive indicators and performance measures for the machine learning models;
- Supporting data management, data quality analysis and the configuration of data collection processes from industrial systems, sensors, PLCs and ERP environments;
- Assisting testing and validation activities, including performance assessment and verification of the platform against the operational requirements defined during the project;
Through its experience in innovation management, technology transfer, software-based innovation and research project coordination, INNOVA helps ensure that the development of KNOW4I is consistent with real industrial needs and ready for future deployment in smart manufacturing environments.
The Challenge
Industrial manufacturing environments face several challenges related to predictive maintenance and smart system integration, including:
- Fragmented industrial data sources distributed across PLCs, sensors, ERP systems, and legacy infrastructures
- Limited interoperability between industrial equipment and digital platforms
- Difficulties in acquiring and managing real-time operational data
- Challenges in accurately predicting equipment failures and Remaining Useful Life (RUL)
- The need for scalable and secure AI-driven maintenance solutions suitable for production environments
- Constraints related to industrial cybersecurity, data governance, and operational continuity
PREDICTUS addresses these challenges by developing an interoperable predictive maintenance platform capable of integrating heterogeneous industrial data sources and applying advanced machine learning techniques to improve operational efficiency and reduce downtime.
Pictures taken from the research paper by: Antonio Cimino
Technologies & Innovation Areas
PREDICTUS leverages a combination of advanced industrial and digital technologies, including:
- Artificial Intelligence & Machine Learning
- Predictive Maintenance
- Industrial IoT (IIoT)
- Real-Time Data Acquisition
- Industrial Interoperability Frameworks
- FIWARE Ecosystem Integration
- Smart Manufacturing & Industry 4.0
- Industrial Data Analytics
- Edge-to-Cloud Industrial Communication
Impact & Results
Release of the KNOW4I platform at TRL 8 readiness for industrial deployment.
PREDICTUS contributes to:
- Advancing the industrial readiness of the KNOW4I platform from TRL 6 to TRL 8
- Improving predictive maintenance accuracy and operational reliability
- Reducing industrial downtime and maintenance costs
- Enhancing interoperability between industrial systems and digital platforms
- Enabling real-time industrial data processing and analytics
- Supporting the digital transformation of manufacturing environments
- Strengthening the adoption of Industry 4.0 technologies in industrial production systems
In all, PREDICTUS aims to deliver a scalable, interoperable, and AI-driven predictive maintenance platform capable of supporting next-generation smart manufacturing environments.
