Course summary
This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens, unknown correlation, and other useful information for diagnosis and prognosis solutions which leads to enhance reliability, maintainability and readiness of the selected system. Big Data analytics has attracted intense interest from both academia and industry recently for its attempt to extract more useful information and knowledge from Big Data. Big Data analytics will help to develop more advance diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine leaning exists as the most promising technologies of big data analytics in industrial problems. The student will have the opportunity to work with experts in the data analytics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University.
Entry requirements
A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.; the potential to engage in innovative research and to complete the phd within a three-year period of study.; a minimum of English language proficiency (IELTS overall minimum score of 6.5).; also, the candidate is expected to:; have excellent analytical, reporting and communication skills; be self-motivated, independent and team player; be genuine enthusiasm for the subject and technology; have the willing to publish research findings in international journals.
Fees and funding
Tuition fees
No fee information has been provided for this course
Additional fee information
No additional fees or cost information has been supplied for this course, please contact the provider directly.
Provider information
Cranfield University
Cranfield
Bedford
MK43 0AL