Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well

Kombination von Messdaten und wissensbasierter Modellierung zur Fehlerdiagnose bei Weichen / Connecting measurement data and knowledge-based engineering for heavy rail switch fault diagnosis

Die Anwendung Künstlicher Intelligenz (KI) im Bereich Prognostics and Health Management (PHM) der Eisenbahninfrastruktur, insbesondere in der Fehlerdiagnose, wird durch hinsichtlich Umfang und/oder Labelling unzureichende Datenbestände und die Notwendigkeit der Rückverfolgbarkeit aufgrund strenger Sicherheitsvorschriften erschwert. Vielversprechende Ansätze sind Feature Engineering, unüberwachtes Lernen und wissensbasierte Systeme. Vor diesem Hintergrund wird nachfolgend erörtert, wie Stromumlaufkurven von Weichenantrieben ausgewertet und mit einem für den Menschen interpretierbaren Bayes'schen Netzmodell für Diagnosezwecke verbunden werden können. -- The application of AI methods in prognostics and health management, especially fault diagnosis, for railway infrastructure is complicated by the largely unlabelled databases and the necessity for traceability due to strict safety regulations. Promising approaches include feature engineering, unsupervised learning and knowledge-based systems. This article discusses how to treat the current curve measurements of railway point machines and connect them with a human-interpretable Bayesian network model for diagnostic purposes

3-D Dynamic Analysis of High-Speed Railroad Track

High-Speed Rail (HSR) as a fast, reliable and environmentally friendly mode of transportation has received a lot of attention in recent decades. The International Union of Railways reported that there are more than 18600 miles of HSR in operation and about 1.6 billion passengers per year are carried by them. Although there are plans for HSR in many states including Florida, the United States, however, is still hesitant to develop its own HSR network. One of the main barriers to developing high-speed rail is excessive vibration propagation to the media which may cause annoyance to people who live in the track neighborhood. Train induced vibration also contributes to track settlement, developing track flaws, and increasing life cycle cost of track and supporting structures. The aim of this research is to address this problem by conducting a comprehensive investigation into track dynamics. For this purpose, three-dimensional mass-spring-damper models of vehicle, track and supporting structures were developed and matrices of mass, stiffness, and damping of each subsystem were formed. The response of the whole system was, then, determined by coupling the subsystems using Hertz contact theory. The differential equations of the coupled system were solved by the Newmark integration method and the results including vertical and lateral displacements and forces were presented in the time domain. Since the purpose of this dissertation is to quantify the effect of track and vehicle condition on vibration level, rail defects were also taken into account and rail random irregularities for the vertical profile, Gauge, alignment and cross level (super elevation) were incorporated into a numerical solution. The results of the study show the effect of track and vehicle parameters on the response of the vehicle, track, and substructures. Since Florida and some other states in the United States are very prone to hurricanes, an investigation was conducted into the effect of wind speed on vehicle stability. For this purpose, a curved beam was modeled to consider the influence of track curvature, cant deficiency, wind speed and train speed simultaneously. The results from the study show the maximum allowable values of train speed and axle load for different wind speeds. The findings can be used to decide under what circumstances there is a risk of vehicle overturning and how to avoid it

Thermal and mechanical behavior of railway tracks

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáRailways are infrastructures subject to open weather conditions and also to temperature changes during the day and over the season. Due to this change, internal stresses may appear, whether tensile or compressive depending on the stress-free temperature and the current measure. High compressive stress may lead the track to buckle, meanwhile tensile stress can cause brittle failure. Given the importance of the temperature on railways, many models have been developed to correlate weather conditions and rail temperatures, in order to avoid the occurrence of mechanical instabilities which cause major problems in the operation of railroads. The present work validates one model developed by CNU university by comparing it with nite element solutions and also with experimental data of a rail track in the city of Mirandela-Portugal. A python package was developed to solve the model and is available to download. The model shows a good correlation between measured and simulated rail temperatures. In addition, by utilizing weather information of other locations in Portugal, the maximum expected rail temperatures were determined. Furthermore, mechanical analyses were made to analyze the critical temperature to reach the buckling mode of instability without the e ect of rolling loads and also the important parameters that a ect this phenomenon. The simulations show that the quality of the ballast and the initial miss-alignment of the track are the most important. Keywords:Caminhos de ferro s~ao estruturas expostas a uma grande variedade de condi c~oes clim aticas e, concretamente a varia c~oes de temperatura durante o dia e ao longo das esta c~oes durante o ano. Devido a estas varia c~oes tens~oes internas ocorrem, podendo ser esfor cos de compress~ao ou tra c~ao, dependendo da temperatura neutral do per l. A ocorr^encia de tens~oes de compress~ao elevadas pode causar encurvadura da via, enquanto que os esfor cos de tra c~ao podem ocasionar a fratura fr agil. Devido a import^ancia das temperaturas nas vias f erreas, muitos modelos t^em sido desenvolvidos para correlacionar condi c~oes clim aticas com a temperatura da via tendo em vista o seu uso como ferramenta de preven c~ao de acidentes na opera c~ao. O presente trabalho utiliza um destes modelos, desenvolvido pela universidade CNU e valida-o utilizando solu c~oes com o m etodo dos elementos nitos e, tamb em, com dados experimentais de uma via f errea localizada na cidade de Mirandela-Portugal. Foi ainda desenvolvido um software utilizando a linguagem Python para facilitar a solu c~ao do modelo, estando ainda dispon vel para download. O modelo demonstrou boa correla c~ao entre as temperaturas simuladas e medidas. Al em disso, utilizando informa c~oes meteorol ogicas de outras localidades em Portugal, as temperaturas m aximas esperada das vias foram determinadas. Posteriormente, an alises mec^anicas de encurvadura foram realizadas para determinar em quais temperaturas uma via ferra pode sofrer encurvadura e, tamb em, quais par^ametros que in uenciam este fen^omeno. As simula c~oes mostram que a qualidade do balastro e as imperfei c~oes iniciais da via s~ao os mais importantes

Doctor of Philosophy

dissertationA safe and secure transportation system is critical to providing protection to those who employ it. Safety is being increasingly demanded within the transportation system and transportation facilities and services will need to adapt to change to provide it. This dissertation provides innovate methodologies to identify current shortcomings and provide theoretic frameworks for enhancing the safety and security of the transportation network. This dissertation is designed to provide multilevel enhanced safety and security within the transportation network by providing methodologies to identify, monitor, and control major hazards associated within the transportation network. The risks specifically addressed are: (1) enhancing nuclear materials sensor networks to better deter and interdict smugglers, (2) use game theory as an interdiction model to design better sensor networks and forensically track smugglers, (3) incorporate safety into regional transportation planning to provide decision-makers a basis for choosing safety design alternatives, and (4) use a simplified car-following model that can incorporate errors to predict situational-dependent safety effects of distracted driving in an ITS infrastructure to deploy live-saving countermeasures

Improved Railroad Freight Car Truck Performance and Safety: Phase 1

DTFR5311DThe Federal Railroad Administration contracted Transportation Technology Center, Inc. (TTCI) to explore the development and status of the Improved (or Integrated) Freight Car Truck (IFCT) and trace the development of the modern three-piece truck into the M-976 truck into the IFCT over the last 30 to 40 years. In addition, the report collects thoughts from industry representatives regarding the status of the IFCT. Finally, using parametric simulation results, the report attempts to optimize the design suspension parameters of the IFCT

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation

Application of Track Geometry Deterioration Modelling and Data Mining in Railway Asset Management

Modernin rautatiejärjestelmän hallinnassa rahankäyttö kohdistuu valtaosin nykyisen rataverkon korjauksiin ja parannuksiin ennemmin kuin uusien ratojen rakentamiseen. Nykyisen rataverkon kunnossapitotyöt aiheuttavat suurten kustannusten lisäksi myös usein liikennerajoitteita tai yhteyksien väliaikaisia sulkemisia, jotka heikentävät rataverkon käytettävyyttä Siispä oikea-aikainen ja pitkäaikaisia parannuksia aikaansaava kunnossapito ovat edellytyksiä kilpailukykyisille ja täsmällisille rautatiekuljetuksille. Tällainen kunnossapito vaatii vankan tietopohjan radan nykyisestä kunnosta päätöksenteon tueksi. Ratainfran omistajat teettävät päätöksenteon tueksi useita erilaisia radan kuntoa kuvaavia mittauksia ja ylläpitävät kattavia omaisuustietorekistereitä. Kenties tärkein näistä datalähteistä on koneellisen radantarkastuksen tuottamat mittaustulokset, jotka kuvastavat radan geometrian kuntoa. Nämä mittaustulokset ovat tärkeitä, koska ne tuottavat luotettavaa kuntotietoa: mittaukset tehdään toistuvasti, 2–6 kertaa vuodessa Suomessa rataosasta riippuen, mittausvaunu pysyy useita vuosia samana, tulokset ovat hyvin toistettavia ja ne antavat hyvän yleiskuvan radan kunnosta. Vaikka laadukasta dataa on paljon saatavilla, käytännön omaisuudenhallinnassa on merkittäviä haasteita datan analysoinnissa, sillä vakiintuneita menetelmiä siihen on vähän. Käytännössä seurataan usein vain mittaustulosten raja-arvojen ylittymistä ja pyritään subjektiivisesti arvioimaan rakenteiden kunnon kehittymistä ja korjaustarpeita. Kehittyneen analytiikan puutteet estävät kuntotietojen laajamittaisen hyödyntämisen kunnossapidon suunnittelussa, mikä vaikeuttaa päätöksentekoa. Tämän väitöskirjatutkimuksen päätavoitteita olivat kehittää ratageometrian heikkenemiseen mallintamismenetelmiä, soveltaa tiedonlouhintaa saatavilla olevan omaisuusdatan analysointiin sekä jalkauttaa kyseiset tutkimustulokset käytännön rataomaisuudenhallintaan. Ratageometrian heikkenemisen mallintamismenetelmien kehittämisessä keskityttiin tuottamaan nykyisin saatavilla olevasta datasta uutta tietoa radan kunnon kehityksestä, tehdyn kunnossapidon tehokkuudesta sekä tulevaisuuden kunnossapitotarpeista. Tiedonlouhintaa sovellettiin ratageometrian heikkenemisen juurisyiden selvittämiseen rataomaisuusdatan perusteella. Lopuksi hyödynnettiin kypsyysmalleja perustana ratageometrian heikkenemisen mallinnuksen ja rataomaisuusdatan analytiikan käytäntöön viennille. Tutkimustulosten perusteella suomalainen radantarkastus- ja rataomaisuusdata olivat riittäviä tavoiteltuihin analyyseihin. Tulokset osoittivat, että robusti lineaarinen optimointi soveltuu hyvin suomalaisen rataverkon ratageometrian heikkenemisen mallinnukseen. Mallinnuksen avulla voidaan tuottaa tunnuslukuja, jotka kuvaavat rakenteen kuntoa, kunnossapidon tehokkuutta ja tulevaa kunnossapitotarvetta, sekä muodostaa havainnollistavia visualisointeja datasta. Rataomaisuusdatan eksploratiiviseen tiedonlouhintaan käytetyn GUHA-menetelmän avulla voitiin selvittää mielenkiintoisia ja vaikeasti havaittavia korrelaatioita datasta. Näiden tulosten avulla saatiin uusia havaintoja ongelmallisista ratarakennetyypeistä. Havaintojen avulla voitiin kohdentaa jatkotutkimuksia näihin rakenteisiin, mikä ei olisi ollut mahdollista, jollei tiedonlouhinnan avulla olisi ensin tunnistettu näitä rakennetyyppejä. Kypsyysmallin soveltamisen avulla luotiin puitteet ratageometrian heikkenemisen mallintamisen ja rataomaisuusdatan analytiikan kehitykselle Suomen rataomaisuuden hallinnassa. Kypsyysmalli tarjosi käytännöllisen tavan lähestyä tarvittavaa kehitystyötä, kun eteneminen voitiin jaotella neljään eri kypsyystasoon, jotka loivat selkeitä välitavoitteita. Kypsyysmallin ja asetettujen välitavoitteiden avulla kehitys on suunniteltua ja edistystä voidaan jaotella, mikä antaa edellytykset tämän laajamittaisen kehityksen onnistuneelle läpiviennille. Tämän väitöskirjatutkimuksen tulokset osoittavat, miten nykyisin saatavilla olevasta datasta saadaan täysin uutta ja merkityksellistä tietoa, kun sitä käsitellään kehittyneen analytiikan avulla. Tämä väitöskirja tarjoaa datankäsittelyratkaisujen luomisen ja soveltamisen lisäksi myös keinoja niiden käytäntöönpanolle, sillä tietopohjaisen päätöksenteon todelliset hyödyt saavutetaan vasta käytännön radanpidossa.In the management of a modern European railway system, spending is predominantly allocated to maintaining and renewing the existing rail network rather than constructing completely new lines. In addition to major costs, the maintenance and renewals of the existing rail network often cause traffic restrictions or line closures, which decrease the usability of the rail network. Therefore, timely maintenance that achieves long-lasting improvements is imperative for achieving competitive and punctual rail traffic. This kind of maintenance requires a strong knowledge base for decision making regarding the current condition of track structures. Track owners commission several different measurements that depict the condition of track structures and have comprehensive asset management data repositories. Perhaps one of the most important data sources is the track recording car measurement history, which depicts the condition of track geometry at different times. These measurement results are important because they offer a reliable condition database; the measurements are done recurrently, two to six times a year in Finland depending on the track section; the same recording car is used for many years; the results are repeatable; and they provide a good overall idea of the condition of track structures. However, although high-quality data is available, there are major challenges in analysing the data in practical asset management because there are few established methods for analytics. Practical asset management typically only monitors whether given threshold values are exceeded and subjectively assesses maintenance needs and development in the condition of track structures. The lack of advanced analytics prevents the full utilisation of the available data in maintenance planning which hinders decision making. The main goals of this dissertation study were to develop track geometry deterioration modelling methods, apply data mining in analysing currently available railway asset data, and implement the results from these studies into practical railway asset management. The development of track geometry deterioration modelling methods focused on utilising currently available data for producing novel information on the development in the condition of track structures, past maintenance effectiveness, and future maintenance needs. Data mining was applied in investigating the root causes of track geometry deterioration based on asset data. Finally, maturity models were applied as the basis for implementing track geometry deterioration modelling and track asset data analytics into practice. Based on the research findings, currently available Finnish measurement and asset data was sufficient for the desired analyses. For the Finnish track inspection data, robust linear optimisation was developed for track geometry deterioration modelling. The modelling provided key figures, which depict the condition of structures, maintenance effectiveness, and future maintenance needs. Moreover, visualisations were created from the modelling to enable the practical use of the modelling results. The applied exploratory data mining method, General Unary Hypotheses Automaton (GUHA), could find interesting and hard-to-detect correlations within asset data. With these correlations, novel observations on problematic track structure types were made. The observations could be utilised for allocating further research for problematic track structures, which would not have been possible without using data mining to identify these structures. The implementation of track geometry deterioration and asset data analytics into practice was approached by applying maturity models. The use of maturity models offered a practical way of approaching future development, as the development could be divided into four maturity levels, which created clear incremental goals for development. The maturity model and the incremental goals enabled wide-scale development planning, in which the progress can be segmented and monitored, which enhances successful project completion. The results from these studies demonstrate how currently available data can be used to provide completely new and meaningful information, when advanced analytics are used. In addition to novel solutions for data analytics, this dissertation research also provided methods for implementing the solutions, as the true benefits of knowledge-based decision making are obtained in only practical railway asset management