The University of Southampton
University of Southampton Institutional Repository

Proving MEMS technologies for smarter railway infrastructure

Proving MEMS technologies for smarter railway infrastructure
Proving MEMS technologies for smarter railway infrastructure
Quantifying how railway track responds to passing trains in terms of displacement, velocity or acceleration, can provide insights into both the performance and the condition of the track. A number of trackside monitoring technologies have been shown to be capable of providing this information; however these are primarily research tools and tend to be costly hence actual deployments are relatively limited in scope. To assess systematically the changing health of railway track, more cost-effective continuous approaches to monitoring are required. Micro electrical mechanical systems (MEMS) are commonplace sensors in consumer electronics, low cost and can be used to measure acceleration. Thus they have the potential to provide the kind of data required to assess railway track behaviour at a much lower cost and in an environmentally robust small deployment package. However confidence in the quality of the data is required. This paper discusses the criteria for the selection of MEMS devices for this application. Laboratory trials and direct comparison of trackside measurements with well-established monitoring techniques demonstrate the effectiveness of the selected MEMS devices, and show their potential for use in continuous monitoring schemes to evaluate changes in track performance. The paper thus provides evidence that these kinds of low cost technologies are suitable for railway applications, building confidence in their use and enabling their adoption in self-monitoring smart infrastructure.
1077-1084
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Le Pen, Louis
4a38e256-d113-4bba-b0d4-32d41995928a
Watson, Geoffrey
a7b86a0a-9a2c-44d2-99ed-a6c02b2a356d
Thompson, David
bca37fd3-d692-4779-b663-5916b01edae5
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Hayward, Mick
21df87e5-9ddb-4201-ae8b-e3c16c0af86a
Morlery, Simon
e760d04f-4e54-4722-9939-878634b001fd
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Le Pen, Louis
4a38e256-d113-4bba-b0d4-32d41995928a
Watson, Geoffrey
a7b86a0a-9a2c-44d2-99ed-a6c02b2a356d
Thompson, David
bca37fd3-d692-4779-b663-5916b01edae5
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Hayward, Mick
21df87e5-9ddb-4201-ae8b-e3c16c0af86a
Morlery, Simon
e760d04f-4e54-4722-9939-878634b001fd

Milne, David, Le Pen, Louis, Watson, Geoffrey, Thompson, David, Powrie, William, Hayward, Mick and Morlery, Simon (2016) Proving MEMS technologies for smarter railway infrastructure. [in special issue: Advances in Transportation Geotechnics III] Procedia Engineering, 143, 1077-1084. (doi:10.1016/j.proeng.2016.06.222).

Record type: Article

Abstract

Quantifying how railway track responds to passing trains in terms of displacement, velocity or acceleration, can provide insights into both the performance and the condition of the track. A number of trackside monitoring technologies have been shown to be capable of providing this information; however these are primarily research tools and tend to be costly hence actual deployments are relatively limited in scope. To assess systematically the changing health of railway track, more cost-effective continuous approaches to monitoring are required. Micro electrical mechanical systems (MEMS) are commonplace sensors in consumer electronics, low cost and can be used to measure acceleration. Thus they have the potential to provide the kind of data required to assess railway track behaviour at a much lower cost and in an environmentally robust small deployment package. However confidence in the quality of the data is required. This paper discusses the criteria for the selection of MEMS devices for this application. Laboratory trials and direct comparison of trackside measurements with well-established monitoring techniques demonstrate the effectiveness of the selected MEMS devices, and show their potential for use in continuous monitoring schemes to evaluate changes in track performance. The paper thus provides evidence that these kinds of low cost technologies are suitable for railway applications, building confidence in their use and enabling their adoption in self-monitoring smart infrastructure.

Text Proving MEMs technologies for smarter railway infrastructure2.pdf - Accepted Manuscript
Download (686kB)

More information

Accepted/In Press date: 21 April 2016
e-pub ahead of print date: 13 July 2016
Published date: 2016
Venue - Dates: conference; 2016-09-04; 2016-09-07, 2016-09-04 - 2016-09-07
Organisations: Faculty of Engineering and the Environment

Identifiers

Local EPrints ID: 396916
URI: https://eprints.soton.ac.uk/id/eprint/396916
PURE UUID: dc8bc0c6-d282-4bf7-bc8e-3cfa65e3cafc
ORCID for David Milne: ORCID iD orcid.org/0000-0001-6702-3918
ORCID for Louis Le Pen: ORCID iD orcid.org/0000-0002-4362-3895
ORCID for Geoffrey Watson: ORCID iD orcid.org/0000-0003-3074-5196
ORCID for David Thompson: ORCID iD orcid.org/0000-0002-7964-5906
ORCID for William Powrie: ORCID iD orcid.org/0000-0002-2271-0826

Catalogue record

Date deposited: 16 Jun 2016 15:22
Last modified: 14 Apr 2018 04:01

Export record

Altmetrics

Contributors

Author: David Milne ORCID iD
Author: Louis Le Pen ORCID iD
Author: Geoffrey Watson ORCID iD
Author: David Thompson ORCID iD
Author: William Powrie ORCID iD
Author: Mick Hayward
Author: Simon Morlery

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Library staff edit
Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×