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Advanced signal processing and data-mining techniques for proactive railway track maintenance


Key facts

Type of research degree
Application deadline
Friday 17 April 2020
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Competition funded
Source of funding
Research council
Dr David Connolly and Professor Kang Li
School of Civil Engineering, School of Electronic and Electrical Engineering
Research groups/institutes
Institute of Communication and Power Networks
<h2 class="heading hide-accessible">Summary</h2>

The functional requirements of railway tracks are to allow the safe movement of rail traffic while maximising social and economic benefits. To effectively manage and maintain railroad tracks is crucial yet challenging because they constantly experience a combination of elastic and permanent settlement after trains pass. This permanent settlement accumulates over time and fluctuates along the length of the track, due to changes in support conditions and vehicle dynamics. When the track quality indices (which are often functions of casual parameters such as traffic, track type, and maintenance) exceed allowable limits, it becomes a safety risk and requires the track to be reconstructed which is expensive.

<h2 class="heading hide-accessible">Full description</h2>

<p>Currently, laser-based recording devices attached to the train undercarriage are used to record these differential settlements (known as track geometry). These measurements are useful for analysing the existing geometry, however there is great value in using data mining techniques on historical records. Further, combining this with advanced signal processing techniques, it is possible to model and predict track deterioration conditions, thus offering predictive maintenance capabilities and effective track management. In doing this, the future maintenance requirements for the track can be predicted and scheduled, thus increasing safety while reducing maintenance costs.</p> <p>To achieve this goal, this project will develop an advanced signal processing and data mining methodology to analyse the geometry signals collected by the rail industry and to build track deterioration models for track maintenance planning. The methodology will use a combination of statistical analysis based on wavelet methods, machine learning techniques and engineering analysis to model and predict the locations and severity of future track problems.</p> <h5>Aims</h5> <ol> <li>Develop an advanced signal processing and data mining method to analyse railway track geometry data</li> <li>Use the new method to understand and model the relationship between track support conditions, vehicle characteristics and track geometry deterioration based on engineering analysis</li> <li>Use the new method to predict the location and date of future maintenance requirements</li> </ol> <p>The University of Leeds has a large breadth of railway research expertise and is currently developing a new <a href="">Institute for High Speed Railway and Systems Integration</a>.&nbsp;The Institute has exciting opportunities for the signal processing and data mining of huge datasets actively being collected on operational railways.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &ldquo;Advanced signal processing and data-mining techniques for proactive railway track maintenance&ldquo; as well as&nbsp;<a href="">Professor Kang Li</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<h2 class="heading">Funding on offer</h2>

<p><strong>UK/EU</strong>&nbsp;&ndash;&nbsp;Engineering &amp; Physical Sciences Research Council Studentship&nbsp;for 3.5 years. A full standard studentship consists of academic fees (&pound;4,600 in Session 2020/21), together with a maintenance grant (&pound;15, 285&nbsp;in Session 2020/21) paid at standard Research Council rates. UK applicants will be eligible for a full award paying tuition fees and maintenance. European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course.&nbsp;&nbsp;Funding is awarded on a competitive basis.</p>

<h2 class="heading">Contact details</h2>

<p>For further information please contact Doctoral College Admissions:<br /> e:&nbsp;<a href=""></a>, t: +44 (0)113 343 5057.</p>

<h3 class="heading heading--sm">Linked funding opportunities</h3>
<h3 class="heading heading--sm">Linked research areas</h3>