JOB DESCRIPTION
JOB TITLE:
Research Fellow (Remote monitoring—Data Analysis,
Diagnostics and Prognostics)
DEPARTMENT:
School of Engineering
LOCATION:
Brayford Campus, Lincoln
REPORTS TO: Professor Chris Bingham
POST NUMBER:
ENG5017
GRADE:
7
DATE:
August 2011
DURATION:
Fixed term until January 2013
FTE:
Full-Time 1.0
Context
The School of Engineering at the University of Lincoln was founded in 2009, the first
new School of its kind for more than 20 years. A £37M, 5-year development is
currently underway, with a brand new state-of-the-art engineering building having
been completed this summer. Key to this success is a long-term relationship with
Siemens.
Amongst its broad research base in the areas of Industrial Power and Energy, the
School is now hosting substantial research activity, in collaboration with Siemens
Industrial Turbomachinery Lincoln (SITL), to provide real-time diagnostic and
prognosis tools to facilitate rapid fault prediction and amelioration, and provide
reliability-based maintenance scheduling of its turbomachinery across the globe.
This project represents a key strategic goal for SITL, and as such, is highly prestigious.
Job Purpose
The University of Lincoln has been awarded HEFCE Strategic Development Fund
money to establish a new school of engineering at its Brayford Campus in the centre
of Lincoln. This is a ground-breaking initiative, in partnership with SITL and other
employers, that seeks to develop engineering education for the 21st century. With a
specialism in power and energy, and the scope to develop related areas including
control, combustion and power transmission, the school offers undergraduate,
postgraduate and research degrees as well as engaging in research and knowledge
transfer. The successful candidate will join a fast-growing team with the aspiration to
establish a world-class academic centre with a reputation for engagement and
relevance.
Research Project
This 2-year work-programme focuses on the research, development and application
of computational data-analysis and signal processing techniques for the on-line
determination of a wide-range of unit failure modes, with a view to providing an
integrated, condition-based Predictive Maintenance tool to remotely inform the
technical help-desk of abnormal unit behavioural characteristics. The programme also
aims to develop techniques to provide more accurate real-time estimates of unit
operational status and provide early-warning indicators of unscheduled maintenance
requirements and decision-making, for the global fleet of turbomachinery. To fulfil
the requirements, key technical skills are summarised below.


An excellent grounding in computational data analysis techniques and/or
advanced digital signal processing methodologies e.g. data-fusion, novelty
detection, clustering techniques, decision-making methods based on
accumulated knowledge or ‘learning’, parameter estimation.
Experience with the use of common instrumentation and sensor measurement
systems, e.g. position, vibration, temperature sensors etc. and their
interfacing, for industrial/commercial systems.
Key Responsibilities

Undertake literature surveys and other investigations of the state-of-the-art, and
prepare reports as required.

Design and undertake programme of research under the direction of the Principal
Investigator, demonstrating a significant level of autonomy.

Perform Project Management activities, planning, scheduling, monitoring and
reporting on progress of research projects.

Identify and liaise with internal and external collaborators, and with colleagues in
the Department, maintaining positive and effective working relationships. In
particular, maintaining and raising our standing with Siemens.

Participate in and help to organize internal research activities, including seminars,
research meetings and conferences.

Undertake continuous professional development activities.

Lead in the production of high quality research outputs, including reports, papers
and other publications of national/international standing.

Contribute to the production of grant applications.

Engage in teaching support activities, up to a maximum of six hours per week,
possibly including leading a small number of units (no more than two per
annum).

Aid in the supervision of postgraduate research students.
KEY WORKING RELATIONSHIPS
INTERNAL
Principal Investigator, Head of School, other research and academic staff within
School.
EXTERNAL
Research Collaborators, Sponsors and Clients.
UNIVERSITY OF LINCOLN
PERSON SPECIFICATION
Job Title:
Research Fellow
Post Number:
ENG5017
Date:
August 2011
Selection Criteria
Knowledge, Training and Qualifications:
 PhD or equivalent (good candidates may be
accepted with a PhD pending, subject to
publication record) in a relevant subject area
 Extensive knowledge specific to project/area, for
instance relevant data analysis, novelty detection,
clustering and/or multi-sensor data-fusion
techniques
Experience:
 Extensive experience of relevant research methods
 Authorship of research outputs of
national/international standing
 Experience of sensor, instrumentation and
measurement systems
 Experience of industrial collaborative research
Skills:
 Ability to design, conduct and project manage
original research in the subject area
 Excellent written communication, including the
ability to write reports and research outputs
 Ability to prioritise own workload and work to
specified deadlines under pressure
 Ability to communicate complex subjects orally
 Matlab and other programming skills
Personal Qualities:
 Flexible approach to workload
 Ability to work on own and as part of a team
 Enthusiasm and commitment
Essential
(E) or
Desirable
(D)
Where
Evidenced
Application,
Interview,
References
E
A
E
A/I
E
A/I
D
D
A/I
A/I
D
A/I
E
A/I
E
A/I
E
E
E
A/I
A/I
A/I
E
E
E
I
I
I
Essential Requirements are those, without which, a candidate would not be able to
do the job.
Desirable Requirements are those which would be useful for the postholder to
possess and will be considered when more than one applicant meets the essential
requirements.