On 15 June 2025, the AVATAR project convened its Advisory Board Meeting online, bringing together experts from academia, industry, and project stakeholders to discuss recent achievements and future directions. The meeting was structured into four key parts, each highlighting major developments across the project and inviting valuable feedback from the advisory board.

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Part I: Project Progress Overview

    The meeting opened with a summary of the AVATAR project’s recent progress. The team presented updates on technical milestones, work package coordination, and field validation efforts. Emphasis was placed on how interdisciplinary collaboration has supported the project’s advancement in areas such as system integration and prototype development.

    Board members welcomed the structured overview and commended the project’s steady progress. They emphasized the importance of continuing to align technical innovation with user needs and recommended strengthening internal coordination between software, hardware, and application teams as the project scales.

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Part II: AI-Driven Predictive Maintenance

    The second segment focused on the development of AI-based analytics for predictive maintenance, a core innovation within the AVATAR framework. The project team showcased machine learning models that analyze real-time sensor data to predict system faults and schedule maintenance proactively.

    These models, designed for lightweight deployment, have been integrated into the AVATAR system to enable early detection of anomalies and extend equipment life. The session also highlighted the link between predictive analytics and the digital twin environment, creating a feedback loop between real-world data and virtual simulation.

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Part III: Data Acquisition System Development

    The third part of the meeting addressed the data acquisition system, a foundational layer for the project’s AI capabilities. Recent enhancements were shared, including improved sensor calibration, robust edge computing devices, and reliable data synchronization mechanisms.

    The board acknowledged the importance of high-quality, real-time data for AI model performance and digital twin accuracy. Experts recommended ensuring the modularity of the acquisition system to allow for scalability and adaptability in diverse operational environments.

Part IV: Digital Twin Integration

    In the final segment, the team presented the latest work on digital twin integration. A prototype was demonstrated, showing how sensor data is used to continuously update a virtual model of the system, enabling interactive diagnostics and decision support.

    The advisory board expressed strong support for this direction, emphasizing the potential for digital twins to improve operational planning, maintenance strategies, and user engagement. They provided insights on improving usability, visualizing uncertainty, and tailoring interfaces to different user roles.