CPMXai:Cognitive Predictive Maintenance and Quality Assurance using EXplainable AI and Machine Lea
| Reference number | |
| Coordinator | Mälardalens Universitet - Akademin för innovation design & teknik IDT |
| Funding from Vinnova | SEK 5 999 945 |
| Project duration | November 2021 - August 2025 |
| Status | Ongoing |
| Venture | The strategic innovation programme for Production2030 |
| Call | SIP Produktion2030, call 14 |
Purpose and goal
CPMXai will develop a digital twin for cognitive predictive maintenance through automatic data labelling, AI/ML and Explainable AI (XAI) to reduce unwanted situations and enhance maintenance in manufacturing and production processes. This will later be generalized and applied in other industries meeting their requirements and resulting in sustainable manufacturing and increasing the competitiveness of the Swedish manufacturing.
Expected effects and result
By applying XAI and digital twin technology, CPMXai will lessen the flaws in the processes and products and increase the reliability of the production system, bringing enhanced business competitiveness as well as economic and environmental sustainability. generalized scalable solution will be developed in a single framework to meet the specific needs of other non-partnering companies in a wider horizon of predictive maintenance enabling increasing competitiveness of Swedish manufacturing industry.
Planned approach and implementation
The project activities are divided into six work packages (WP)s. WP1: Project Management and dissemination. Leader: MDH WP2: Requirements, industrial case specifications and gap analysis: Leader: Hitachi, WP3 : Digital representation of real-world assets through multimodal datafication and digital twin Leader: SPM Instruments WP4 Learning and reasoning in predictive modelling for CPdM WP Leader: MDH WP5: Lifelong machine learning and XAI strategies Leader: MDH WP6 : Industrial demonstrators and performance analysis Leader: Seco Tools