Meet our team

Maria Molodova received the M.Sc. degree in mechanical engineering from N.I. Lobachevsky State University of Nizhny Novgorod, Russia, in 2005, with a thesis work on finite-element simulation of the pulse electro-hydraulic forming process, and the Ph.D. degree from Delft University of Technology, The Netherlands, in 2013, with a thesis on the development of detection algorithms for tracking short-wave defects and their causes in the wheel–track system by means of axle box acceleration measurements. She received a European patent for the Method and Instrumentation for Detection of Rail Defects, in Particular Rail Top Defects. Dr. Molodova was a Postdoctoral Researcher with the Section of Railway Engineering, Delft University of Technology in 2013 – 2015.  Her project was about health condition monitoring of insulated rail joints. She received Best Student Paper Award from IEEE 16th Inteational Intelligent Transportation Systems Conference in 2013, and an Innovation Research Award from Dutch Institute World Class Maintenance in 2014. Dr. Molodova founded a software development company, Okazolab, in 2012. 

  • Axle box acceleration measurements for health condition monitoring of railway infrastructures;
  • Early detection of squats;
  • Health condition monitoring of insulated rail joints;
  • Coupled implicit–explicit finite-element methods for dynamic simulations of wheel–track systems;
  • Modal analysis of the wheel-track systems;
  • Advanced signal processing techniques.

Axle box acceleration system for condition monitoring of railway tracks

In this project, we worked on a new measuring system for condition monitoring of the railway infrastructure, using axle box acceleration (ABA) measurements. The focus of this study was on assessment of the condition of insulated rail joints (IRJ). IRJs are the core of the signalling system; thus, the failure of IRJs has a direct impact on the safety of the railway track operation. In the previous work an innovative approach to enhance the sensitivity of the ABA measurements to short wave track defects, particularly squats, by using longitudinal signals was proposed [1, 2], as well as the automatic detection algorithm was developed [3, 4, 5, 6, 7]. To study the applicability of this ABA system to the health monitoring of IRJs, the conditions of a set of real-life IRJs were assessed for different tracks in the Netherlands [8, 9]. IRJs of different quality were included in the study, such as with joints in good condition or with visible surface degradation of varying degrees, IRJs with cracks in the fastening and IRJs with a damaged insulation layer. Subsequently, the ABA responses were analyzed in the time and frequency domains. Based on the results, different indicators were defined to classify the condition of the IRJ, of which the most relevant were certain characteristic frequencies related to the different types of IRJ damage. The proposed indicators were verified using a hammer test. The application of the results of this study to the health monitoring of IRJs will increase the safety of the railway network and reduce the total yearly IRJ replacement cost by including intelligent preventive maintenance strategies employing the information gathered by this health monitoring system.

Automatic Detection of Squats in Railway Infrastructure
January 1, 2016
by Maria Molodova, Zili Li , Alfredo Núñez, [...]


Validation of a finite element model for axle box acceleration at squats in the high frequency range
January 1, 2016
by Maria Molodova , Zili Li , Alfredo Núñez, [...]


Parameter study of the axle box acceleration at squats
December 1, 2015
by Maria Molodova, Zili Li , Alfredo Núñez, [...]


Experimental investigation into the condition of insulated rail joints by impact excitation
December 1, 2015
by Maider Oregui, Maria Molodova , Alfredo Núñez, [...]


Improvements in axle box acceleration measurements for the detection of light squats in railway infrastructure
August 1, 2015
by Zili Li, Maria Molodova , Alfredo Núñez, [...]


Automated monitoring system for insulated joints: Preliminary results using axle box acceleration measurements
January 1, 2015
by Maria Molodova


Axle Box Acceleration for Health Monitoring of Insulated Joints: A Case Study in The Netherlands
January 1, 2015
by Maria Molodova


Monitoring the railway infrastructure: Detection of surface defects using wavelets
January 1, 2014
by Maria Molodova


Axle box acceleration: Measurement and simulation for detection of short track defects
May 18, 2011
by Maria Molodova, Zili Li , Rolf Dollevoet


Squat growth – Some observations and the validation of numerical predictions
May 18, 2011
by Zili Li, Rolf Dollevoet , Maria Molodova, [...]


Simulation of dynamic responses of vehicle-track system for detection of track short wave defects
January 1, 2010
by Maria Molodova


Differential wear and plastic deformation as causes of squat at track local stiffness change combined with other track short defects
January 1, 2009
by Zili Li, Xin Zhao , Rolf Dollevoet, [...]


An investigation into the causes of squats—Correlation analysis and numerical modelling
October 30, 2008
by Zili Li, Xin Zhao , Coenraad Esveld, [...]


Detection of early squats by axle box acceleration (video): https://www.youtube.com/watch?v=0_BPq38NGF8

Dr. Maria Molodova

Postdoc Researcher

+31 (0)15 278 40 14
M.Molodova@tudelft.nl
Building: 23, room S2 2.35

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