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- Swedish industry is increasingly integrating automation, robotics, and distributed sensor systems.Peter Hansson
- Industrial production lines face unplanned stoppages causing major financial losses, energy waste, and material scrap.
- Traditional monitoring relies on fixed thresholds and delayed feedback loops, leading to reactive rather than proactive maintenance.
- Edge-based AI and federated learning now enable real-time on-site signal processing without exposing sensitive data.
- Opportunity: decentralized, privacy-preserving AI that learns across sites and enables predictive maintenance.
- Lead Partner: ABB AB – industrial automation solutions
- Industrial Need Owner: Sandvik Manufacturing – production systems & machine data
- AI Partner: Chalmers University – edge AI & federated learning
- Security Partner: RISE Cybersecurity – secure data pipelines & on-prem learning
- SME Partner: Elsys AB – sensors & signal processing hardware
- Develop and validate an edge-based AI system for real-time predictive maintenance and adaptive process control.
- Deploy low-latency AI inference on industrial edge devices.
- Implement federated learning across sites without sharing sensitive data.
- Provide operator-facing decision support tools.
- Reduce downtime, scrap, and energy waste in production environments.
- Identify critical machine components and failure patterns.
- Define sensor configurations and industrial IT/OT data flows compatible with production environments.
- Establish secure pipelines for real-time signal capture.
- Develop neural models for vibration, acoustic, and control-signal pattern recognition.
- Apply quantization, pruning, and model reduction for edge execution.
- Deploy cross-site learning where only model parameters are shared between locations.
- Integrate differential privacy and secure aggregation mechanisms.
- Integrate predictive outputs into ABB's automation and HMI systems.
- Build operator dashboards for decision support.
- Evaluate downtime reduction, energy savings, scrap reduction, and operator workload impacts.
- Prepare replication toolkit and SME workshops for broader adoption.
- Reduced downtime and production losses.
- Lower scrap and energy consumption.
- Increased industrial resilience.
- Strengthened Swedish competence in privacy-preserving, real-time industrial AI.
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Abstract
In the abstract, please describe in brief the following: What is to be done: purpose and aims; How the research will be carried out: project organisation, time plan and scientific methods; What is important about the planned research. The abstract shall provide a summary of the purpose and implementation of the research. Please use wording to ensure persons with another subject specialisation can understand the information.
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rational β-lactam/aminoglycoside combinations, represents a major advance against extensively drug-resistant (XDR) Enterobacterales. However, rapidly emerging resistancePeter Hansson threatens their durability, underscoring the need to elucidate the mechanisms driving reduced susceptibility and their clinical consequences.Sandra Sander
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