Journal Browser

Journal Browser

Special Issue "Condition Monitoring Systems for Railway Switches and Crossings (S&C)"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editor

Dr. Valeri Markine
E-Mail Website
Guest Editor
Section of Railway Engineering, Department of Engineering Structures, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, 2628CN, The Netherlands
Interests: railway dynamics; switches and crossings (S&C); transition zones; structural optimization

Special Issue Information

Dear Colleagues,

Sensor-based condition monitoring is an important component of proper and timely maintenance. Development and applications of the methods for condition monitoring and smart maintenance for railway switches and crossings (S&C) is lagging behind compared to the rest of railway infrastructure. So, the maintenance actions on railway S&C and especially are mainly reactively performed after detection of visible damage, which is obviously too late. These repair/replacement actions result in unplanned disruptions to railway traffic that dramatically increase the total costs of railway infrastructure.

In this Issue, you are invited to submit contributions describing development and implementation of condition monitoring and smart maintenance solutions for railway switches and crossings.

The Special Issue will cover the following (but not limited to) topics:

  • Sensors and measurements techniques for instrumentation and wayside condition monitoring of railway S&C;
  • Measurement-based methods for condition assessment of railway S&C;
  • Algorithms for defects (in rails, fastenings, ballast bed, soil, etc.) localization and tracking;
  • Modelling of the short- and long-term dynamic behaviour of turnouts and its use in condition monitoring systems;
  • Degradation models for railway S&C;
  • Development of sensor data-based condition assessment indicators for railway S&C;
  • Signal processing, data fusion, and deep learning in condition monitoring systems for railway S&C;
  • Long-term monitoring of railway crossings S&C;
  • Sensor-based maintenance technologies for railway S&C.

Dr. Valeri Markine
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Railway switches and crossings
  • Sensors in railway S&C
  • Condition monitoring of switches and crossings
  • Instrumentation of railway S&C
  • Wayside monitoring of railway S&C
  • Failure detection in railway S&C
  • Predictive maintenance for railway S&C

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


MBS Vehicle–Crossing Model for Crossing Structural Health Monitoring
Sensors 2020, 20(10), 2880; - 19 May 2020
Cited by 2 | Viewed by 923
This paper presents the development of a multi-body system (MBS) vehicle–crossing model and its application in the structural health monitoring (SHM) of railway crossings. The vehicle and track configurations in the model were adjusted to best match the real-life situation. By using the [...] Read more.
This paper presents the development of a multi-body system (MBS) vehicle–crossing model and its application in the structural health monitoring (SHM) of railway crossings. The vehicle and track configurations in the model were adjusted to best match the real-life situation. By using the measurement results obtained from an instrumented crossing and the simulation results from a finite element (FE) model, the MBS model was validated and verified. The results showed that the main outputs of the MBS model correlated reasonably well with those from both the measurements and the FE model. The MBS and FE models formed the basis of an integrated analysis tool, which can be applied to thoroughly study the performance of railway crossings. As part of the SHM system for railway crossings developed at Delft University of Technology, the MBS model was applied to identify the condition stage of a monitored railway crossing. The numerical results confirmed the highly degraded crossing condition. By using the measured degradation as the input in the MBS model, the primary damage sources were further verified. Through identifying the crossing condition stage and verifying the damage source, necessary and timely maintenance can be planned. These actions will help to avoid crossing failure and unexpected traffic interruptions, which will ultimately lead to sustainable railway infrastructure. Full article
(This article belongs to the Special Issue Condition Monitoring Systems for Railway Switches and Crossings (S&C))
Show Figures

Figure 1

Back to TopTop