Influencing factors for condition-based maintenance in railway tracks using knowledge-based approach

Ali Jamshidi, Siamak Hajizadeh, Meysam Naeimi, Alfredo Nunez, Zili Li

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

32 Downloads (Pure)

Abstract

In this paper, we present a condition-based maintenance decision method using
knowledge-based approach for rail surface defects. A railway track may contain a considerable number of surface defects which influence track maintenance decisions. The proposed method is based on two sets of maintenance decision factors i.e. (1) defect detection data and (2) prior knowledge of the track. A defect detection model is proposed to monitor surface defects of the track
including squats. The detection model relies on track images and Axle Box Acceleration (ABA) signals to give both positions of severity and defects. To acquire the prior knowledge, a set of track monitoring data is selected. A fuzzy inference model is proposed relying on the maintenance factors
to give the track health condition in a case study of the Dutch railway network. The proposed condition-based maintenance model enables infrastructure manager to prioritize critical pieces of the track based on the health condition.
Original languageEnglish
Title of host publicationProceedings of the First International Conference on Rail Transportation
Subtitle of host publicationICRT2017
Number of pages6
Publication statusPublished - 2017
Event1st International Conference on Rail Transportation - Chengdu, China
Duration: 10 Jul 201712 Jul 2017
Conference number: 1

Conference

Conference1st International Conference on Rail Transportation
Abbreviated titleICRT 2017
CountryChina
CityChengdu
Period10/07/1712/07/17

Keywords

  • Condition-based maintenance decision
  • Rail surface defects
  • Bayesian model

Fingerprint Dive into the research topics of 'Influencing factors for condition-based maintenance in railway tracks using knowledge-based approach'. Together they form a unique fingerprint.

Cite this