A Bayesian assessment for railway track geometry degradation prognostics

Juan Chiachio-Ruano, Manuel Chiachio Ruano, Darren Prescott, John Andrews

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian model class methodology for prognostics performance assessment whereby different prognostics algorithms can be rigorously assessed and ranked according to their relative probability to predict the future degradation process. The proposed framework is exemplified and tested for a case study about track degradation prognostics using published data about track settlement, taken from a simulated traffic loading experiment carried out at the Nottingham Railway Test Facility.
LanguageEnglish
Title of host publicationProceedings of the European Conference of the PHM Society
EditorsChetan S. Kulkarni, Tiedo Tinga
Place of PublicationNew York
Number of pages7
Volume4
StatePublished - 30 Jun 2018
EventFourth European Conference of the PHM Society - Utrecht, Netherlands
Duration: 3 Jul 20186 Jul 2018

Publication series

NamePHM Society European Conference
PublisherPHM Society

Conference

ConferenceFourth European Conference of the PHM Society
Abbreviated titlePHME18
CountryNetherlands
CityUtrecht
Period3/07/186/07/18

Fingerprint

Degradation
Geometry
Test facilities
Availability
Costs
Experiments

Keywords

  • railway track prognostics
  • model-based prognostics

Cite this

Chiachio-Ruano, J., Chiachio Ruano, M., Prescott, D., & Andrews, J. (2018). A Bayesian assessment for railway track geometry degradation prognostics. In C. S. Kulkarni, & T. Tinga (Eds.), Proceedings of the European Conference of the PHM Society (Vol. 4). (PHM Society European Conference). New York.
Chiachio-Ruano, Juan ; Chiachio Ruano, Manuel ; Prescott, Darren ; Andrews, John. / A Bayesian assessment for railway track geometry degradation prognostics. Proceedings of the European Conference of the PHM Society. editor / Chetan S. Kulkarni ; Tiedo Tinga. Vol. 4 New York, 2018. (PHM Society European Conference).
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abstract = "Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian model class methodology for prognostics performance assessment whereby different prognostics algorithms can be rigorously assessed and ranked according to their relative probability to predict the future degradation process. The proposed framework is exemplified and tested for a case study about track degradation prognostics using published data about track settlement, taken from a simulated traffic loading experiment carried out at the Nottingham Railway Test Facility.",
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Chiachio-Ruano, J, Chiachio Ruano, M, Prescott, D & Andrews, J 2018, A Bayesian assessment for railway track geometry degradation prognostics. in CS Kulkarni & T Tinga (eds), Proceedings of the European Conference of the PHM Society. vol. 4, PHM Society European Conference, New York, Fourth European Conference of the PHM Society , Utrecht, Netherlands, 3/07/18.

A Bayesian assessment for railway track geometry degradation prognostics. / Chiachio-Ruano, Juan; Chiachio Ruano, Manuel; Prescott, Darren; Andrews, John.

Proceedings of the European Conference of the PHM Society. ed. / Chetan S. Kulkarni; Tiedo Tinga. Vol. 4 New York, 2018. (PHM Society European Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Bayesian assessment for railway track geometry degradation prognostics

AU - Chiachio-Ruano,Juan

AU - Chiachio Ruano,Manuel

AU - Prescott,Darren

AU - Andrews,John

PY - 2018/6/30

Y1 - 2018/6/30

N2 - Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian model class methodology for prognostics performance assessment whereby different prognostics algorithms can be rigorously assessed and ranked according to their relative probability to predict the future degradation process. The proposed framework is exemplified and tested for a case study about track degradation prognostics using published data about track settlement, taken from a simulated traffic loading experiment carried out at the Nottingham Railway Test Facility.

AB - Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian model class methodology for prognostics performance assessment whereby different prognostics algorithms can be rigorously assessed and ranked according to their relative probability to predict the future degradation process. The proposed framework is exemplified and tested for a case study about track degradation prognostics using published data about track settlement, taken from a simulated traffic loading experiment carried out at the Nottingham Railway Test Facility.

KW - railway track prognostics

KW - model-based prognostics

UR - https://phmpapers.org/index.php/phme/article/view/461

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M3 - Conference contribution

VL - 4

T3 - PHM Society European Conference

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Chiachio-Ruano J, Chiachio Ruano M, Prescott D, Andrews J. A Bayesian assessment for railway track geometry degradation prognostics. In Kulkarni CS, Tinga T, editors, Proceedings of the European Conference of the PHM Society. Vol. 4. New York. 2018. (PHM Society European Conference).