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Automatic Engineering Knowledge Extraction from Automotive Field Failure Data


   Faculty of Engineering and Informatics

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  Prof Felician Campean, Dr Sohag Kabir, Dr Dhaval Thakker  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

This project, carried out in conjunction with a major global automotive manufacturer, aims to develop, implement and validate a model-based systems engineering (MBSE) based knowledge engineering approach to underpin data-driven learning from automotive operational reliability data, specifically warranty data. This research will seek to develop and validate Machine Learning for learning based on empirical causal reasoning integrated with engineering knowledge analyses (including Fault Tree Analysis - FTA, and Failure Modes and Effects Analysis - FMEA) and models (MBSE). The ultimate aim is to generate field updated FMEAs and FTAs, including the automatic data-driven discovery and validation of new failure modes, providing updated knowledge and insight for future product development and validation. The validation of the methods and algorithms developed will be carried out in conjunction with real world data available from the automotive Company partner.

The ideal candidate will have a background in knowledge engineering, systems engineering and machine learning applied to an engineering context.

The project will be based in the interdisciplinary Advanced Automotive Analytics Research Laboratory, part of the University of Bradford Automotive Research Centre, which has a strong track record of collaborations with the global automotive industry spanning over 25 years. The project is expected to start no later than June 2022.


Funding Notes

This project comes with full funding for UK students, including tuition fees and a stipend at UKRI rates.
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