Rebecca Peters is a PhD student working under the supervision of Penny Holborn exploring how a major multinational company can utilize techniques from data science.
Rebecca joined the University in 2013 as an undergraduate Mathematics Student, and after achieving a first class honours degree in 2016 she obtained a sought after Knowledge Economy Skills Scholarship (KESS 2) to study for a PhD. After completing a final year dissertation in Big Data Analytics, Rebecca became extremely interested in the real-world applications of Big Data due to its extensiveness and varied uses across sectors. Rebecca firmly believes that furthering understanding in this domain is integral to advancing in other areas of Computing and Mathematics, such as Artificial Intelligence, Statistical Learning and Data Science.
Rebecca’s research, in collaboration with Tata Steel UK, focuses on exploring the applications of Big Data within the steel manufacturing industry. At the Port Talbot steel plant, huge amounts of data surrounding the production process are collected daily, in particular around the production of steel slabs from liquid iron governed by the performance of the continuous casters. Today Industry 4.0 is driving advances in innovative technologies to help digitise manufacturing processes, enabling business to make more ‘informed’ decisions. Sensors play a fundamental role in either monitoring or controlling the continuous casting process, collecting and transmitting measurements that contain rich information about the condition of equipment. Rebecca’s research explores the latest Data Science to generate failure prediction models where the pre-failure conditions are learnt from historical sensor data. Ultimately, this information will be applied in real time to predict future failures and to help optimise the scheduling of continuous caster maintenance.