Velocity measurement-based friction estimation for railway vehicles running on adhesion limit: swarm intelligence-based multiple models approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F20%3A39914476" target="_blank" >RIV/00216275:25510/20:39914476 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/abs/10.1080/15472450.2018.1542305?journalCode=gits20" target="_blank" >https://www.tandfonline.com/doi/abs/10.1080/15472450.2018.1542305?journalCode=gits20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/15472450.2018.1542305" target="_blank" >10.1080/15472450.2018.1542305</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Velocity measurement-based friction estimation for railway vehicles running on adhesion limit: swarm intelligence-based multiple models approach

Popis výsledku v původním jazyce

Model-based condition monitoring is an increasingly important area for rail transportation. The key elements of such condition monitoring methodologies are low-cost vehicle sensors and intelligent algorithms. In this study, a swarm intelligence-based multiple models approach is proposed to detect different friction conditions by using velocity measurements of a railway vehicle. In this case of application, estimated parameter is the maximum friction coefficient. Additionally, proposed methodology is tested experimentally by using the measurements taken from a tram wheel test stand. Multiple mathematical models of the test stand are created with different maximum friction coefficients, whereas all initial conditions and other system parameters are same for each model. Therefore, comparison of the output of each model with measurements is considered to interpret the parameter value of the model, which best represents the system, is selected as parameter estimate. Unlike the traditional multiple models approach, a swarm intelligence-based evolution of the models is proposed. Experiments carried out on the test stand reveal that the proposed methodology is promising to be used as an on-board friction condition monitoring tool for railway vehicles with traction.

Název v anglickém jazyce

Velocity measurement-based friction estimation for railway vehicles running on adhesion limit: swarm intelligence-based multiple models approach

Popis výsledku anglicky

Model-based condition monitoring is an increasingly important area for rail transportation. The key elements of such condition monitoring methodologies are low-cost vehicle sensors and intelligent algorithms. In this study, a swarm intelligence-based multiple models approach is proposed to detect different friction conditions by using velocity measurements of a railway vehicle. In this case of application, estimated parameter is the maximum friction coefficient. Additionally, proposed methodology is tested experimentally by using the measurements taken from a tram wheel test stand. Multiple mathematical models of the test stand are created with different maximum friction coefficients, whereas all initial conditions and other system parameters are same for each model. Therefore, comparison of the output of each model with measurements is considered to interpret the parameter value of the model, which best represents the system, is selected as parameter estimate. Unlike the traditional multiple models approach, a swarm intelligence-based evolution of the models is proposed. Experiments carried out on the test stand reveal that the proposed methodology is promising to be used as an on-board friction condition monitoring tool for railway vehicles with traction.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Intelligent Transportation Systems

ISSN

1547-2450

e-ISSN

—

Svazek periodika

24

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

93-107

Kód UT WoS článku

000489989200001

EID výsledku v databázi Scopus

2-s2.0-85059559288