About the Project
This exciting PhD project will contribute to the ongoing research effort to make the technology viable by developing assurance methods to analyse proposed multi-robot systems for the inspection of nuclear fusion infrastructure. The project will focus on Common Cause Failure (CCF) vectors associated with robotic systems, the effects of these vectors and how to provide assurance that as a result the (safety) risk associated with maintenance action using robots can be minimised. It will involve secondments to RACE facilities.
CCFs arise as a result of dependencies between multiple elements of a system. An initiating event leads through one or more coupling factors to a failure of multiple components and sub-systems. Possible coupling factors include
• Maintenance - Same “robotic actor” maintains supposedly independent equipment.
• Data Sources - Independent equipment relies on information from the same channel such as a camera, radar or lidar
• Design - Physically redundant components have same systematic fault.
• Physical Proximity of Equipment – Event removes multiple layers of “independent” protection
• Common Vulnerability - Similar technologies with unanticipated weakness, e.g. software operating systems, data security, Electromagnetic Interference
You will be working alongside PhD and post-doctoral researchers working on the assurance of RAS (https://www.york.ac.uk/assuring-autonomy/), Inspection Robotics (https://rimanetwork.eu) and EMC (https://etn-peter.eu) and will thus form part of a vibrant research community.
The project is jointly funded by the UK Atomic Energy Authority’s Remote Applications in Challenging Environments programme (http://www.race.ukaea.uk/ ), and by the University of York-led Assuring Autonomy International Programme.
The successful candidate will conduct your research under the supervision of Dr Mark Nicholson https://www.cs.york.ac.uk/people/mark) and Prof John McDermid (https://www.cs.york.ac.uk/people/jam).
Apply for this studentship
1. You must apply online for a full-time PhD (https://www.cs.york.ac.uk/postgraduate/research-degrees/phd/#tab-4)
2. You must quote the project title (Assuring Robotic Autonomous Systems for Inspection of Fusion Reactors) in your application.
3. There is no need to write a full formal research proposal (2,000-3,000 words) in your application to study as this studentship is for a specific project.
4. Provide a personal statement. As part of your application please provide a personal statement of 500-1,000 words with your initial thoughts on the research topic.
This project is being interviewed for and filled on a rolling basis. Please submit your application at your earliest opportunity.
The studentship will begin in October 2020.
Dr. Mark Nicholson: [email protected]
• £15,009 (2019/20 rate) per year stipend
• Tuition fees
• RTSG (training/consumables/travel) provision
We are seeking a highly motivated candidate who has, or expects to be awarded, a first-class or 2.1 degree or a master’s degree in a STEM based discipline. The work involves analysis of engineered artefacts for failure properties and is therefore suitable for those with an interest in dependability and assurance.
The studentship is open to UK/EU nationals.
Management, iiw.kuleuven.be/onderzoek/peter (3 places are at York)
2. Assuring Autonomy International Programme, www.york.ac.uk/assuring-autonomy/
3. RIMA - https://rimanetwork.eu
4. RACE - www.race.ukaea.uk
5. IAEA, “Protecting against Common Cause Failures in Digital I&C Systems of Nuclear Power Plants
6. Marshall, F. M., A. Mosleh, and D. M. Rasmuson. "Common-Cause Failure Database and Analysis
System: Event Definition and Classification, NUREG/CR-6268 (INEEL/EXT-97-00696)." (1998).
7. Le Duy, Tu Duong, and Dominique Vasseur. "A practical methodology for modeling and
estimation of Common Cause Failure Parameters in multi-unit nuclear PSA model." Reliability
Engineering & System Safety 170 (2018): 159-174.
8. Leonardi, Fabio, Fabrizio Messina, and Corrado Santoro. "A Risk-Based Approach to Automate
Preventive Maintenance Tasks Generation by Exploiting Autonomous Robot Inspections in Wind
Farms." IEEE Access 7 (2019): 49568-49579.
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