Model-Based Fault Analysis for Railway Traction Systems

  1. Olmo, Jon del
  2. Garramiola, Fernando
  3. Poza, Javier
  4. Almandoz, Gaizka
Libro:
Modern Railway Engineering

ISBN: 978-953-51-3860-0,  978-953-51-3859-4,  978-953-51-4024-5

Año de publicación: 2018

Tipo: Capítulo de Libro

DOI: 10.5772/INTECHOPEN.74277 GOOGLE SCHOLAR lock_openAcceso abierto editor

Objetivos de desarrollo sostenible

Resumen

Fault analysis in industrial equipment has been usually performed using classical techniques such as failure modes and effects analysis (FMEA) and fault tree analysis (FTA). Model-based fault analysis has been used during the last several years in order to overcome the limitations of classical methods when complex industrial equipment has to be analyzed. In railway and automotive sectors, the development and validation of new products are based on hardware-in-the-loop (HIL) platforms. In this chapter, a methodology to enhance classical FMEAs is presented. Based on HIL simulations, the objective is to improve the results of the fault analysis with quantitative information about the effects of each fault mode. In this way, the impact of the fault analysis in the design of the traction system, the development of new diagnostic functionalities and in the maintenance tasks will increase.

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Información de financiación

This research work was supported by CAF Power & Automation.

Referencias bibliográficas

  • Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing. 2006;20(7):1483-1510
  • Farnsworth M, Tomiyama T. Capturing, classification and concept generation for automated maintenance tasks. CIRP Annals - Manufacturing Technology. 2014;63(1):149-152
  • Le Mortellec A. Proposition d’une architecture de surveillance active à base d’agents intelligents pour l’aide à la maintenance de systèmes mobiles-Application au domaine ferroviaire. Valenciennes: Université de Valenciennes et du Hainaut-Cambrésis; 2014
  • Gandibleux J. Contribution a l’evaluation de surete de fonctionnement des architectures de surveillance diagnostic embarquees Application au transport ferroviaire. Valenciennes: Université de Valenciennes et du Hainaut-Cambresis; 2013
  • Xue F, Yan W, Roddy N, Varma A. Operational data based anomaly detection for locomotive diagnostics. In: International Conference on Machine Learning, Models, Technologies and Applications. MLMTA. 2006. pp. 236-241
  • European Commission, “Shift2Rail Joint Undertaking Multi-Annual Action Plan,” Shift2Rail Joint Undertaking. Brussels. 2015
  • Kabir S. An overview of fault tree analysis and its application in model based dependability analysis. Expert Systems with Applications. 2017;77:114-135
  • Sharvia S, Kabir S, Walker M, Papadopoulos Y. Chapter 12 - Model-based dependability analysis: State-of-the-art, challenges, and future outlook. In: Software Quality Assurance. Boston: Morgan Kaufmann. 2016. pages 251-278
  • Joshi A, Heimdahl MPE. Model-based safety analysis of simulink models using SCADE design verifier. In: SAFECOMP 2005 – International Conference on Computer Safety, Reliability and Security. LNCS. Vol. 3688. Berlin, Heidelberg: Springer Berlin Heidelberg; 2005. pp. 122-135
  • Lisagor O, Pumfrey DJ, Mcdermid JA. Towards a practicable process for automated safety analysis. In: 24th International System Safety Conference (ISSC) organized by The International System Safety Society. 2006. pp. 596-607
  • Villacourt M. Failure mode and effects analysis (FMEA): A guide for continuous improvement for the semiconductor equipment industry. SEMATECH. 1992
  • Liu H, Liu L, Liu N. Expert systems with applications risk evaluation approaches in failure mode and effects analysis : A literature review. Expert Systems with Applications. 2013;40(2):828-838
  • Mikulak RJ, McDermott R, Beauregard M. The Basics of FMEA. 2nd ed. New York: CRC Press; 2008
  • David P, Idasiak V, Kratz F. Reliability study of complex physical systems using SysML. Reliability Engineering & System Safety. 2010;95(4):431-450
  • Sharvia S, Papadopoulos Y. Integrated application of compositional and behavioural safety analysis. In: Dependable Computer Systems. Vol. 97. Berlin, Heidelberg: Springer; 2011. pp. 179-192
  • Belmonte F, Soubiran E. A model based approach for safety analysis. In: SAFECOMP 2012 – International Conference on Computer Safety, Reliability and Security. LNCS. Vol. 7613. Berlin: Springer; 2012. pp. 50-63
  • Grunske L, Winter K, Ytapanage N, Zafar S, Lindsay PA. Experience with fault injection experiments for FMEA. Software – Practice and Experience. 2011;41:1231-1258
  • Aizpurua JI, Muxika E. Model-based design of dependable systems: Limitations and evolution of analysis and verification approaches. International Journal on Advances in Security. 2013;6(1):12-31
  • Sharvia S, Papadopoulos Y. Integrating model checking with HiP-HOPS in model-based safety analysis. Reliability Engineering & System Safety. 2015;135:64-80
  • Lisagor O. Failure Logic Modelling: A Pragmatic Approach. York: University of York; 2010
  • Papadopoulos Y, Maruhn M. Model-based synthesis of fault trees from Matlab-Simulink models. In: 2001 International Conference on Dependable Systems and Networks. Vol. 36. 2001. pp. 77-82
  • Papadopoulos Y. Safety-Directed System Monitoring Using Safety Cases. York: The University of York; 2000
  • Kabir S. Compositional Dependability Analysis of Dynamic Systems with Uncertainty. Hull: The University of Hull; 2016
  • Bozzano M et al. ESACS: An integrated methodology for design and safety analysis of complex systems. In: Proceedings of the European Safety and Reliability Conference 2003, ESREL2003. 2003
  • Akerlund O, Bieber P, Böde E. ISAAC, a framework for integrated safety analysis of functional, geometrical and human aspects. In: 3rd European Congress on Embedded Real Time System (ERTS). 2006
  • Estefan JA. Survey of model-based systems engineering (MBSE) methodologies. In: International Council on Systems Engineering (INCOSE). 2008
  • Terwiesch P, Keller T, Scheiben E. Rail vehicle control system integration testing using digital hardware-in-the-loop simulation. IEEE Transactions on Control Systems Technology. 1999;7(3):352-362
  • Wang L, Zhang Y, Yin C, Zhang H, Wang C. Hardware-in-the-loop simulation for the design and verification of the control system of a series-parallel hybrid electric city-bus. Simulation Modelling Practice and Theory. 2012;25:148-162
  • Isermann R, Schaffnit J, Sinsel S. Hardware-in-the-loop simulation for the design and testing of engine-control systems. Control Engineering Practice. 1999;7(5):643-653
  • Poon JJ, Kinsy MA, Pallo NA, Devadas S, Celanovic IL. Hardware-in-the-loop testing for electric vehicle drive applications. In: Conference Proceedings – IEEE Applied Power Electronics Conference and Exposition – APEC. 2012. pp. 2576-2582
  • Wu J, Dufour C, Sun L. Hardware-in-the-loop testing of hybrid vehicle motor drives at Ford Motor Company. In: 2010 IEEE Vehicle Power and Propulsion Conference(VPPC 2010); 2010
  • Baccari S et al. Real-time hardware-in-the-loop in railway: Simulations for testing control software of electromechanical train components. In: Railway Safety, Reliability and Security: Technologies and Systems. 2012. p. 487
  • Alvarez-gonzalez F, Member S, Griffo A, Wang J, Member S. Real-time hardware-in-the-loop simulation of permanent magnet synchronous motor drives under stator faults. IEEE Transactions on Industrial Electronics. 2017;64(9):6960-6969
  • Chung D-WCD-W, Sul S-KSS-K. Analysis and compensation of current measurement error in vector-controlled AC motor drives. IEEE Transactions on Industry Applications. 1998;34(2):340-345
  • Jung H, Kim J, Kim C, Choi C. Diminution of current measurement error for vector controlled AC motor drives. IEEE Transactions on Industry Applications. 2006;42(5):1249-1256