PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Assessment of the track condition using the Gray Relational Analysis method

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Ocena stanu torowiska z wykorzystaniem metody Grey Relational Analysis
Języki publikacji
EN PL
Abstrakty
EN
The article concerns the developed methodology for assessing the technical condition of a tramway track. Thanks to the data collected from multiple tram journeys equipped with an on-board vibration recording system, it was possible to create profiles of crossings through track sections in different technical condition. In order to identify the track condition, an algorithm based on the gray-scale modeling was proposed, and a similarity comparison between the obtained track profiles. A new measure of similarity has been proposed that has not been used so far in gray-scale modeling. The obtained results confirm the applicability of the proposed methodology.
PL
Praca dotyczy opracowanej metodyki do oceny stanu technicznego toru tramwajowego. Dzięki zgromadzonym danym z wielokrotnych przejazdów tramwaju wyposażonego w pokładowy system rejestracji drgań, udało się stworzyć profile przejazdów przez odcinki torów w różnym stanie technicznym. W celu identyfikacji stanu toru zaproponowano algorytm oparty na metodzie modelowania szarych systemów oraz badanie podobieństwa pomiędzy uzyskanymi profilami przejazdów. Zaproponowano także nową miarę podobieństwa nie stosowaną do tej pory w zagadnieniach modelowania szarych systemów. Uzyskane wyniki potwierdzają aplikacyjność zaproponowanej metodyki.
Słowa kluczowe
Rocznik
Strony
147--152
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
  • Institute of Applied Mechanics Poznan University of Technology ul. Jana Pawła II 24, 60-965 Poznań, Poland
autor
  • Department of Rail Vehicles Poznan University of Technology ul. Piotrowo 3, 60-965 Poznań, Poland
Bibliografia
  • 1. Barbera A N, Corradi R, Barilaro P, Li Z, Wacrenier P L. A Track Quality Monitoring System Designed to be Installed on Vehicles in Normal Operation. Proceedings of the First International Conference on Railway Technology: Research, Development and Maintenance 2012; Paper 43.
  • 2. Chen H W, Chang Ni-Bin. Prediction analysis of solid waste generation based on grey fuzzy dynamic modelling. Resources, Conservation and Recycling 2000; 29: 1-18, https://doi.org/10.1016/S0921-3449(99)00052-X.
  • 3. Chen J, Roberts C, Weston P. Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems. Control Engineering Practice 2008; 16(5): 585-596, https://doi.org/10.1016/j.conengprac.2007.06.007.
  • 4. Deng J L. Introduction to grey system theory. The Journal of Grey System 1989; 1(1): 1-24.
  • 5. Firlik B, Czechyra B, Chudzikiewicz A. Condition Monitoring System for Light Rail Vehicle and Track. Key Engineering Materials 2012; 518: 66-75, https://doi.org/10.4028/www.scientific.net/KEM.518.66.
  • 6. Firlik B, Tabaszewski M. Dynamical Problems in Condition Monitoring of a Light Rail Vehicle. Proceedings of 13th Mini Conference on Vehicle System Dynamics, Identification and Anomalies – VSDIA 2012; 293-301.
  • 7. Firlik B, Tabaszewski M, Sowiński B. Vibration-based symptoms in condition monitoring of a light rail vehicle. Key Engineering Materials 2012; 518: 409-417, https://doi.org/10.4028/www.scientific.net/KEM.518.409.
  • 8. Kayacan E, Ulutas B, Kaynak O. Grey system theory-based models in time series Prediction. Expert Systems with Applications 2010; 37: 1784-1789, https://doi.org/10.1016/j.eswa.2009.07.064.
  • 9. Lin S L, Wu S J. Is grey relational analysis superior to the conventional techniques in predicting financial crisis? Expert Systems with Applications 2011; 38: 5119-5124, https://doi.org/10.1016/j.eswa.2010.09.139.
  • 10. Liu S F, Yang Y Y, Xie N M. A summary on the research of GRA models. Grey Systems: Theory and Application 2013; 3(1): 7-15, https://doi.org/10.1108/20439371311293651.
  • 11. Luo M, Kuhnell B T. Forecasting machine condition using grey-system theory. Bulletin of MCCM, Monash Univ. Australia 1990; 2(1).
  • 12. Parkinson H, Iwincki S. An Intelligent Track Monitoring System. AIC conference on Infrastructure Management, London, February 1999.
  • 13. Tabaszewski M, Cempel C. Similarity measures for diagnostic symptom evolution. Grey Systems: Theory and Application 2016; 6: 51-63, https://doi.org/10.1108/GS-10-2015-0072.
  • 14. Wang T C, Liou M C, Hung H H. Application of grey theory on forecasting the exchange rate between TWD and USD. International Conference on Business and Information, Academy of Taiwan Information System Research and Hong Kong Baptist University, July 2005.
  • 15. Yao A W L, Chi S C. Analysis and design of a Taguchi-Grey based electricity demand predictor for energy management systems. Energy Conversion and Management 2004; 45: 1205–1217, https://doi.org/10.1016/j.enconman.2003.08.008.
  • 16. Zhang H, Li Z, Chen Z. Application of grey modelling method to fitting and forecasting wear trend of marine diesel engines. Tribology International 2003; 36: 753-756, https://doi.org/10.1016/S0301-679X(03)00056-2.
  • 17. Zhang L, Wang Z, Zhao S. Short-term fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimizing method. Mechanical Systems and Signal Processing 2007; 21: 856-865, https://doi.org/10.1016/j.ymssp.2005.09.013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8106e21e-e233-4d00-8608-4fcefb85556a
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.