Platform capabilities in addressing I4.0 and I5.0 requirements
| Requirements | Plaform proposed in this paper | HMC challenge |
|---|---|---|
| Prescriptive analytics for decision-making support | Predictive maintenance analytics provide recommendations and decisions, with Live Chat enabling real-time operator interaction within the “Collaborative Decision Support” module | 1 |
| Statistical analysis | Advanced statistical analysis through Big Data and ML within the “Data Analytics and Management” module | 5 |
| HMC approach | Intuitive and adaptive user interfaces foster human-AI interaction, with Live Chat supporting continuous communication | 7 |
| Low training costs and evaluation time | 10-s LSTM training and rapid evaluation with agile model updates via Retraining Trigger based on Drift Detection | 3 |
| OEE beyond MTTR, MTBF, or availability | OEE, MTTR, and MTBF visualised in real-time within the “KPI visualisation” module | 5 |
| Focus on all equipment, not just critical components | Data from all equipment across the entire production line is analysed | 2 |
| Real-time data collection | Ensured through edge computing principles and IoT devices (PLCs and sensors) | 5 |
| Low time interval for prediction | Predictions before failures are made within a 6 to 3-min window | 2 |
| Probability assessment of the failure occurrence | Failure probability assessed via ML models and communicated through Augmented Alerts and XAI Insights | 3 |
| New technologies | Integration of AI and human-centric design for PdM 5.0, within automated processes, fostering I5.0 adoption in a continuous learning perspective | 1 |
| AI-driven solutions for dynamic work environments | Self-learning AI, with Drift Detection, Retraining Trigger and Human-in-the-loop refinement, adapts to changing environments | 2 |
| Efficient adaptation algorithms for complex operational situations | Real-time PdM strategy adaptation with XAI Insights, ensuring transparency and model improvement | 3 |
| Practical use cases of HMC approaches | Applied to three assembly lines with real benefits and a roadmap for development for replicability and scalability | 4 |
| Data volume, management, real-time processing and edge computing solution | Cloiud Data Center, edge computing and real-time AI analytics manage large industrial data volumes | 5 |
| Security, privacy, and data integrity issues | Ensured through oversight by the Secure and Ethical Data Officer and internal protocols | 6 |
| Emphasis on human-centred design and intuitive interfaces | Focus on the Human Expertise and Collaboration Hub, enhancing the user experience through adaptive interfaces, usability feedback loops and prototypal optimisation | 7 |
| Portability of HMC system or technology | Accessible on multiple devices (smartphones, tablets) via a web-based approach | 8 |
| Guidelines to enhance workforce skill in new technologies and AI-driven systems | Educational resources and tailored training provided by the User Support and Training Coordinator foster continuous workforce skill development | 9 |
| Bridging the gap between academic research and practical applications | Integrates academic research into a practical, validated PdM 5.0 solution for industrial application | 10 |
| Requirements | Plaform proposed in this paper | HMC challenge |
|---|---|---|
| Prescriptive analytics for decision-making support | Predictive maintenance analytics provide recommendations and decisions, with Live Chat enabling real-time operator interaction within the “Collaborative Decision Support” module | 1 |
| Statistical analysis | Advanced statistical analysis through Big Data and ML within the “Data Analytics and Management” module | 5 |
| HMC approach | Intuitive and adaptive user interfaces foster human-AI interaction, with Live Chat supporting continuous communication | 7 |
| Low training costs and evaluation time | 10-s LSTM training and rapid evaluation with agile model updates via Retraining Trigger based on Drift Detection | 3 |
| OEE beyond MTTR, MTBF, or availability | OEE, MTTR, and MTBF visualised in real-time within the “KPI visualisation” module | 5 |
| Focus on all equipment, not just critical components | Data from all equipment across the entire production line is analysed | 2 |
| Real-time data collection | Ensured through edge computing principles and IoT devices (PLCs and sensors) | 5 |
| Low time interval for prediction | Predictions before failures are made within a 6 to 3-min window | 2 |
| Probability assessment of the failure occurrence | Failure probability assessed via ML models and communicated through Augmented Alerts and XAI Insights | 3 |
| New technologies | Integration of AI and human-centric design for PdM 5.0, within automated processes, fostering I5.0 adoption in a continuous learning perspective | 1 |
| AI-driven solutions for dynamic work environments | Self-learning AI, with Drift Detection, Retraining Trigger and Human-in-the-loop refinement, adapts to changing environments | 2 |
| Efficient adaptation algorithms for complex operational situations | Real-time PdM strategy adaptation with XAI Insights, ensuring transparency and model improvement | 3 |
| Practical use cases of HMC approaches | Applied to three assembly lines with real benefits and a roadmap for development for replicability and scalability | 4 |
| Data volume, management, real-time processing and edge computing solution | Cloiud Data Center, edge computing and real-time AI analytics manage large industrial data volumes | 5 |
| Security, privacy, and data integrity issues | Ensured through oversight by the Secure and Ethical Data Officer and internal protocols | 6 |
| Emphasis on human-centred design and intuitive interfaces | Focus on the Human Expertise and Collaboration Hub, enhancing the user experience through adaptive interfaces, usability feedback loops and prototypal optimisation | 7 |
| Portability of HMC system or technology | Accessible on multiple devices (smartphones, tablets) via a web-based approach | 8 |
| Guidelines to enhance workforce skill in new technologies and AI-driven systems | Educational resources and tailored training provided by the User Support and Training Coordinator foster continuous workforce skill development | 9 |
| Bridging the gap between academic research and practical applications | Integrates academic research into a practical, validated PdM 5.0 solution for industrial application | 10 |
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