Postgraduate Studentships
Proposed Project Title: Semi-supervised learning for soft sensors in advanced manufacturing
Principal Supervisor: Prof Seán McLoone 

Project Description:

With the rapid development in sensing, communication and storage technologies companies are now collecting and storing large quantities of data on their manufacturing processes – temperatures, pressures, flow rates, etc. At the same time, measuring the final product quality is generally only done through infrequent sampling, due to the cost and time involved (e.g. testing product quality may require destructive testing, or take several hours). To achieve better control of manufacturing processes and improved efficiency in terms of waste and energy consumption real-time measurements of product quality are desirable. One approach to solving this problem that is an active area of research in advanced manufacturing, and in the pharmaceutical and semiconductors sectors, in particular, is to develop so called soft sensing models that can predict product quality from the available process measurements.

Soft sensing models are generally quite complex and challenging to develop and require the use of sophisticated machine learning and system identification techniques. One of the major challenges with building them is that datasets are often ill-conditioned with a large number of candidate process variables available as model inputs but only a small number of output training samples, by virtue of the restricted product quality sampling regimes normally employed. The current practice is to discard the large volume of data collected which does not have corresponding product quality information. Discarding this data, referred to as unlabelled in the machine learning community, represents a huge waste of resource and a missed opportunity. As such the objectives of this PhD project is to explore techniques for utilizing unlabelled data to enhance model building and to develop methodologies that can make the best use of all available data in the development of robust soft sensors for advanced manufacturing processes.

Objectives:

(i) Develop novel semi-supervised soft sensor modelling algorithms
(ii) Develop recursive implementations to address scalability challenges posed by working with large data volumes
(iii) Validate algorithms using benchmark industrial case studies

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.

GENERAL INFORMATION

This 3 year PhD studentship, potentially funded by the Department for Employment and Learning (DEL), commences on 1 October 2015.

Eligibility for both fees and maintenance (£13,863 in 2014/15, 2015/16 TBC) depends on the applicants being either an ordinary UK resident or those EU residents who have lived permanently in the UK for the 3 years immediately preceding the start of the studentship. Non UK residents who hold EU residency may also apply but if successful may receive fees only.

Please note: DEL awards are available for Home and EU candidates only.

Applicants should apply electronically through the Queen’s online application portal at:
https://dap.qub.ac.uk/portal/

Further information available at:
http://www.qub.ac.uk/schools/eeecs/StudyattheSchool/PhDProgrammes

Contact detail

Supervisor Name:
Prof. Seán McLoone, Tel: +44 (0)28 9097 4125

QUB Address:
Queens University of Belfast, School of EEECS
Ashby Building, Stranmillis Road,  Belfast  BT9 5AH
Email: s.mcloone<στο>qub.ac.uk;

Deadline for submission of applications is: 27 February, 2015

For further information on Research Area click on link below: http://www.qub.ac.uk/research-centres/EPIC/