QuAre · Question Answering for MonitoRed Fuel Cell systEms

Question Answering for MonitoRed Fuel Cell systEms

In this project, we provide a transparent and explainable AI knowledge-based framework that ensures not only early fault diagnosis and prognosis of polymer electrolyte fuel cell systems systems but also provides the end user with mitigation actions to prevent the failure of the system, enhancing in this way, its reliability and robustness. On top of this, the framework also allows the end-user to make questions in natural language about the functionality and the deployment of the system and hence obtain a better understanding of its operation and its failures and act properly and in time. The significance of this project is twofold: a) A more generic one for any health monitoring system: as it introduces a novel AI knowledge-based methodology that is aligned with the trustworthiness and FAIR (Findable Accessible Interoperability Reuse) principles, b) A more specific one focused on the fuel cell diagnostics: The recently proposed strategic technologies for Europe platform (STEP) includes clean technologies as one of its three priority areas. In terms of energy, these clean technologies include, among others, also fuel cells. Hence, their good performance is vital for climate, economy and eventually society as well. The following set of Research Objectives (RO) are identified: ● RO1: Perform spatio-temporal extensions of the data model developed by previous work of the researcher so that the duration of abnormal measurements can be accurately captured and later assessed (see RO2). ● RO2: To radically extend the existing ontology to encapsulate rules related to degradation effects of cumulative faults, long-term storage, start-stop cycling and environmental conditions. Extend the inference engine respectively to enable inferences on spatiotemporal data. ● RO3: To create KG embeddings of the static and streaming data so that manipulation of the KGs is simplified and hence significantly accelerated while keeping the structure of the graphs. ● RO4: To develop techniques and extend the existing system for answering complex factoid and non-factoid questions effectively (with high precision and recall) and efficiently (with very short response times). ● RO5: To employ NLG techniques so that the developed platform will be able to express the justifications of the answers in natural language hence passing tangible and accurate information to the end user. ● RO6: Working with the Fuel Cell system lab hosted by the Aeronautical and Automotive Engineering (AAE) of Loughborough University (LU) to evaluate our tool and to conduct a user study with service engineers

Data: CORDIS, © European Union