“Development of Prescriptive Analytics Based on Artificial Intelligence for IAMS”
The overall DAYDREAMS objective is to move forward the integration and use of data and artificial/human trustworthy intelligence together with context-driven HMI for prescriptive Intelligent Asset Management Systems (IAMS) in railway by (i) advancing in maintenance approach towards prescriptive asset management, (ii) improving the decision-making process by developing multi-objective decision optimisation approaches taking into account all implications of IAMS decisions in the railway environment, and (iii) reinforcing the role of the person-in-the-loop by designing and developing advanced context-driven HMIs to allow context- and risk-aware multiple-options decision-making processes.
The HMI will allow the person-in-the-loop to: (i) properly access and visualise predictions/metrics and models, (ii) assess why and how the model predicts something, (iii) Steer models by setting parameters, and (iv) evaluate alternatives using parameter steering and extending this process through speculative execution.
The DAYDREAMS objective will be assessed by validating the proposed solution using the following two-step approach: (i) A first validation at TRL4 of the approaches (developed prescriptive asset management) using several scenarios; (ii) A further validation of the DAYDREAMS methodologies integrated in a TRL5 prototype using at least two scenarios. The validation will cover both the performances of the prototypes and its trust for future adoption in multi-actors environments.
The validation will be carried out by defining evaluation and validation metrics and KPIs: (i) linked to asset management problems to be solved by the involved Infrastructure Managers (IMs) and related baselines, (ii) quantified and measurable; (iii) referred to high-level KPIs, and (iv) useful to address multi-objective optimisation.
The DAYDREAMS project is coordinated by UNIFE (Belgium) and ZenaByte is leader of the Work Package WP5 “Requirements definition, validation and evaluation” aimed at defining the criteria, metrics, and the related KPIs for the evaluation of the DAYDREAMS prototype and at validating it on selected scenarios. ZenaByte is also responsible for the design and implementation of the prescriptive analytics tools.
This project has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008913