Use of LMS Amesim® model and a bond graph support to predict behavior impacts of typical failures in an aircraft hydraulic brake system

  • Mário Maia NetoEmail author
  • Luiz Carlos Sandoval Góes
Technical Paper


Due to the increase in aircraft systems complexity along the decades and the continuous certification requirement improvements for safer operations, the safety assessment accomplished by systems engineers has been demanding more effort from the specialists to make a complete evaluation of the system and respective interfaces. The capability of predicting the real effects of components failures in the system behavior to make better assessments of their severities and to support troubleshooting processes during aircraft operation has also represented a challenging activity. In that context, the development of computational models and simulation has become a common practice in the industry. Therefore, the aim of the present work was to demonstrate the benefits of working in a cohesive manner with two particular modeling techniques: a physical modeling based computational software and the bond graph concepts, to enhance the specialist’s comprehension about the impacts of particular failures in system performance. As a case study, an aircraft hydraulic brake system has been chosen since it performs important, safety-related functions in aircraft operation. For that purpose, a computational model parameterized in LMS Amesim® software is used, after a deep validation process, to assess the behavior of system relevant variables in normal and faulty operating conditions. In parallel, a bond graph diagram representative of a system component is applied as a support tool to assess typical failure modes and help selection of relevant ones for simulation.


Brake system Systems modeling Bond graph Amesim Failure condition 


  1. 1.
    Currey NS (1998) Aircraft landing gear design: principles and practices. AIAA Education Series, Washington, DC. ISBN 0930403-41-XGoogle Scholar
  2. 2.
    Moir I, Seabridge A (2001) Aircraft systems: mechanical, electrical, and avionics subsystems integration, vol 2. Professional Engineering Publishing, Bury St Edmunds, pp 91–124. ISBN 1-86058-289-3Google Scholar
  3. 3.
    Khapane P (2008) Simulation of landing gear dynamics and brake-gear interaction. Thesis (Doctor of Engineering), Technischen Universität Carolo-Wilhelmina zu Braunschweig, BraunschweigGoogle Scholar
  4. 4.
    Frank PM (1990) Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results. Automatica 26(3):459–474MathSciNetCrossRefGoogle Scholar
  5. 5.
    Sheppard JW, Butcher SGW (2007) A formal analysis of fault diagnosis with D-matrices. J Electron Test Theory Appl United States 23(4):309–322CrossRefGoogle Scholar
  6. 6.
    Isermann R (2005) Model-based fault-detection and diagnosis—status and applications. Annu Rev Control 29(1):71–85CrossRefGoogle Scholar
  7. 7.
    Medjaher K (2011) A bond graph model-based fault detection and isolation. In: Andrews J, Bérenguer CH, Jackson L (eds) Maintenance modelling and applications. Det Norske Veritas, pp 503–512.
  8. 8.
    da Silva JC, Saxena A, Balaban E, Goebel K (2012) A knowledge-based system approach for sensor fault modeling, detection and mitigation. Expert Syst Appl Int J 39(12):10977–10989CrossRefGoogle Scholar
  9. 9.
    Feenstra PJ, Mosterman PJ, Biswas G, Breedveld PC (2001) Bond graph modeling procedures for fault detection and isolation of complex flow processes. In: Proc. of international conference on bond graph modelling (ICBM’01), vol 33. SCS Publishing, pp 77–82Google Scholar
  10. 10.
    Wahi MK, Warren SM, Straub HH (1977) An extended prediction model for airplane braking distance and a specification for a total braking prediction system, vol I. Ohio. (ASD-TR-77-6 Vol. I)Google Scholar
  11. 11.
    Wahi MK, Warren SM, Straub HH (1977). An extended prediction model for airplane braking distance and a specification for a total braking prediction system, vol II. Ohio. (ASD-TR-77-6 Vol. II)Google Scholar
  12. 12.
    Doebelin EO (1998) System dynamics: modeling, analysis, simulation, design. Marcel Dekker, New York, pp 54–75, 206–255. ISBN 0-8247-0126-7Google Scholar
  13. 13.
    LMS (2013) AMESim help. AMEHelpGoogle Scholar
  14. 14.
    Merritt HE (1967) Hydraulic control systems. Wiley, Cincinnati. ISBN 0-471-59617-5Google Scholar
  15. 15.
    Bailey DA (2004) Investigation of improvements in aircraft braking design. Thesis (Doctor of Philosophy), Cranfield University, CranfieldGoogle Scholar
  16. 16.
    Society of Automotive Engineers (1996) ARP4761: guidelines and methods for conducting the safety assessment process on civil airborne systems and equipment. SAE aerospace, WarrendaleGoogle Scholar
  17. 17.
    Joshi A, Heimdahl MPE, Miller SP, Whalen MW (2006) Model-based safety analysis. National Aeronautics and Space Administration, Hampton (NASA/CR-2006-213953) Google Scholar
  18. 18.
    Maia Neto M (2017) Método para análise de falhas por grafos de ligação e simulação dinâmica de um sistema de freios de aeronave. Thesis (Doctor of Philosophy), Instituto Tecnológico de Aeronáutica, São José dos CamposGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  • Mário Maia Neto
    • 1
    Email author
  • Luiz Carlos Sandoval Góes
    • 1
  1. 1.Department of Mechanical EngineeringAeronautical Institute of TechnologySão José Dos CamposBrazil

Personalised recommendations