Public deliverables 

D1.2 Legal and ethical requirements for human-machine interaction (PDF)

This report contains an overview of the integration of ethics into the first six months of the AI-PROFICIENT project (taking into account also an update from information retrieved after site visits done in November and December 2021). The overview includes a literature review of the current and past work upon Industrial AI ethics, a survey of related guides and principles, and observations regarding the state of the discipline and its particular character relative to other fields of AI ethics.

D1.3 Pilot-specific demonstration scenarios (PDF)

This report incorporates a specification of the demonstrator to be constructed per use case. This specification has been reviewed and includes a structured and unified approach to the specification of all use cases, with a common information regarding Gap Analysis, Stakeholders, Data Sources, Ethical issues, and a High Level design including Use Case and Sequence Diagrams, as well as a flowchart showing the expected contributions from different partners and the link to appropriate task activities within development work packages WP2-WP4.

D7.1 Roadmap for dissemination and communication first release (PDF)

This report sets out the dissemination and communication strategy as well as the plan to raise awareness, share knowledge, attract potential stakeholders in the context of the AI-PROFICIENT project, through various means, including the AI-PROFICIENT website, the use of Social Media, the distribution of communication material, publications in scientific and industrial journals, participation in events and organization of dedicated workshops with potential end-users and main outreach events. 

D7.2 Project identity kit and communication material (PDF)

This document will help you understand the essential elements of the AI-PROFICIENT identity. It explains how to use the identity and serves as a source of inspiration for you to continue building a strong brand people love to be a part of.

D1.5: AI-PROFICIENT system architecture

The aim of this document is to provide the architecture of the AI-PROFICIENT platform following the “ethics by design approach”.

D3.1: AI-PROFICIENT hybrid models and digital twins first version (state of the art, designing and specification)

This report presents the state of the art of hybrid modelling and digital twins, covering the techniques from first principles modelling to fully data based surrogate models and digital twins based on these approaches. On that basis, designing of the approach for selected use cases and the specifications in each case are described.

D3.3 System-level proactive maintenance strategy

This report is describing the AI-PROFICIENT service that aims at optimizing maintenance scheduling at system level.

D4.1: Human-machine interaction and feedback mechanisms (Design and specification)

This report incorporates the description of the end user’s requirements related to the use cases in terms of the feedback management and the description for each one to manage it for a reinforcement learning approach.

D4.4 AI-PROFICIENT approach for XA

The report covers current state-of-the-art analysis related to different XAI approaches – exploitation of semantic technologies, transparent machine learning models and post-hoc explainability techniques. Many of those have been utilized for the development of three XAI services explained in this report – surrogate explainable data-driven model, post-hoc explainable analysis module and auditability system. For all of those, the methodology, results, integration and application were explained.

D5.1: Communication middleware and IIoT interoperability – design and specification

The aim of this document is to provide the design and specification of AI-PROFICIENT platform middleware and interface towards smart components and edge AI.

D5.2 Semantic data model for integrated digital twins

This document provides an insight into the process how the sensor measurements and the sensor equipment are ontologically expressed. It documents the challenges and the considerations that were raised during this process. The data model is implemented in data layer for the AI-PROFICIENT AI-services. This integration is outlined in this deliverable

D6.1 AI-PROFICIENT validation methodology (preliminary version)

The aim of this document is to propose a generic methodology to measure the degree of compliance with different AI modules deployed in an industrial facility.

D6.6 AI-PROFICIENT validation methodology (final version)

This report is the final version of deliverable D6.1, and the aim is to provide a complete procedure and ways to validate the initially proposed methodology. This methodology intends to establish objective measurement criteria for the results obtained in the AI-Proficient project. These criteria will enable measuring the results obtained in the different use cases.

D2.3 Predictive AI analytics for component self-diagnostics (PDF)

This deliverable presents the advances made in the context of Work Package 2 Smart components and local AI at system edge that are related to the development of edge systems used for diagnosticate the assets in which they are embedded or run.

D2.4 Local AI for proactive maintenance support (PDF)

This deliverable presents the advances made in the context of Work Package 2 Smart components and local AI at system edge that are related to the development of edge systems used for the prognostics of the assets health in which they are embedded or run

D2.6 Smart components and local AI at system edge (PDF)

This deliverable is a public report that summarizes the activities and achievements reached at Work Package 2 within AI-PROFICIENT H2020 project. This WP has attempted to introduce local AI technologies at the system edge under the ‘smart component’ concept, close to the production lines, considering MEMS and PLCs information outputs. This differentiates from other technologies expected to work at ‘cloud’ level on most scenarios (and developed in other WPs).

D3.2 Predictive AI for process quality assurance (PDF)

This deliverable provides a brief description of how AI technologies are a relevant aspect to ensure the quality of the process and consequently the quality of the product in manufacturing processes. However, the deliverable is mainly focused on describing the algorithms for AI predictive analytics for specific use cases. This analysis has been based on the combination of specific knowledge of the processes, the historical data gathered from the monitoring of the processes and the use of ML algorithms.

D3.5 Future scenario-based decision making (PDF)

This deliverable is a public document of AI-PROFICIENT project delivered in the context of Task 3.5: Future scenario based decision-making and lifelong self-learning as a part of Work Package 3 Platform AI analytics and decision-making support, regarding the description of the AI-PROFICIENT service that aim providing life-long self/reinforcement-learning capabilities.

D5.4 Integration with AI4EU’s AI on-demand platform (PDF)

The deliverable describes AI-PROFICIENT’s actions in terms of Task 5.4 to align activities with the AI4EU platform. The Deliverable also presents the integration with AI-on-Demand platform to i) profit from software modules already available in the platform to download and integrate into AI-PROFICIENT, ii) contribute to the AI-on-Demand platform by submitting AI-PROFICIENT’s results to the platform and make them available for future initiatives to build upon and enhance.

D5.8 System integration and deployment (PDF)

The aim of this document is to provide the description of the activities in relation to system integration and deployment of AI-PROFICIENT platform. More specifically, it provides the summary of the main activities that have been performed in WP5.


AI-PROFICIENT Brochure (version 1)


AI-PROFICIENT Brochure (Version-2)

AI-PROFICIENT factsheet(Version-2)

AI-PROFICIENT Digital info pack



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  • Van-Thai Nguyen, Phuc Do, Alexandre Vosin, Benoit Iung, Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems, Reliability Engineering & System Safety, Volume 228, 2022, 108757, ISSN 0951-8320,

  • Alaaeddine Chaoub, Christophe Cerisara, Alexandre Voisin, Benoît Iung. Towards interpreting deep learning models for industry 4.0 with gated mixture of experts. 30th European Signal Processing Conference, EUSIPCO 2022, Aug 2022, Belgrade, Serbia. (hal-03785546)

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