Skip to main content

ANFIS-Based Fault Diagnosis Tool for a Typical Small Aircraft Fuel System

  • Conference paper
  • First Online:
Proceeding of International Conference on Intelligent Communication, Control and Devices

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 479))

Abstract

In the present paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent diagnosis tool for investigating the health of a typical small aircraft fuel system simulation was proposed. The system was designed for identifying the faults present in the aircraft fuel system and to diagnose those conditions with a proper fuel flow to the engine. The ANFIS intelligent tool works based on the logical rules of an expert system, which are developed as per the engine’s fuel consumption and the fuel flow from the tanks. The inputs to train the ANFIS are the fuel flow at the previous instant and the engine’s fuel consumption and the corresponding target is the fuel tank’s control signals. Training of ANFIS, generates the control signals as per the fuel requirement of the engine and the fuel flow to the tanks. The proposed intelligent controller model was implemented in the platform of MATLAB/Simulink and a comparison with the other techniques allowed the effectiveness of the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhi-Ling Yang, Wang Bin, Dong Xing-Hui, L I U Hao.: Expert system of fault diagnosis for gear box in wind turbine. In: Systems Engineering Procedia, Vol. 4, pp. 189–195, 2012

    Google Scholar 

  2. Biswas Gautam, Gyula Simon, Nagabhushan Mahadevan, Sriram Narasimhan, John Ramirez, Gabor Karsai.: A robust method for hybrid diagnosis of complex systems. In: Proceedings of the 5th Symposium on Fault Detection, Supervision and Safety for Technical Processes, pp. 1125–1131, 2003

    Google Scholar 

  3. Robert Breda, Vladimir Beno.: Modeling of Control Circuit of Aircraft fuel system. In: Przegląd Elektrotechniczny, Vol. 89, pp. 172–175, 2013

    Google Scholar 

  4. Bohwa Lee, Sejin Kwon, Poomin Park, Keunbae Kim.: Active power management system for an unmanned aerial vehicle powered by solar cells, a fuel cell, and batteries. In: IEEE Transactions on Aerospace and Electronic Systems, Vol. 50, No. 4, pp. 3167–3177, 2014

    Google Scholar 

  5. Insaurralde Carlos C, Miguel A Seminario, Juan F Jimenez, Jose M Giron-Sierra.: Computer tool with a code generator for avionic distributed fuel control systems with smart sensors and actuators. In: IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 38, No. 3, pp. 431–445, 2008

    Google Scholar 

  6. Timothy G Davis.: Aircraft fuel system simulation. In: Proceedings of the IEEE National Conference in Aerospace and Electronics, pp. 905–911, 1990

    Google Scholar 

  7. Ali Zolghadri.: Advanced model-based FDIR techniques for aerospace systems. Progress in Aerospace Sciences, Vol. 53, pp. 18–29, 2012

    Google Scholar 

  8. Kamal M M, D W Yu, D L Yu.: Fault detection and isolation for PEM fuel cell stack with independent RBF model. Engineering Applications of Artificial Intelligence, Vol. 28, pp. 52–63, 2014

    Google Scholar 

  9. Inseok Hwang, Sungwan Kim, Youdan Kim, Chze Eng Seah.: A survey of fault detection, isolation, and reconfiguration methods. In: IEEE Transactions on Control Systems Technology, Vol. 18, No. 3, pp. 636–653, 2010

    Google Scholar 

  10. Zhang Y M, Jin Jiang.: Active fault-tolerant control system against partial actuator failures. In: IEEE proceedings-Control Theory and applications, Vol. 149, No. 1, pp. 95–104, 2002

    Google Scholar 

  11. De Lillo Liliana, Lee Empringham, Pat W Wheeler, Sudarat Khwan-On, Chris Gerada, M Nazri Othman, Xiaoyan Huang.: Multiphase power converter drive for fault-tolerant machine development in aerospace applications. In: IEEE Transactions on Industrial Electronics, Vol. 57, No. 2, pp. 575–583, 2010

    Google Scholar 

  12. Yu Xiang, Jin Jiang.: Hybrid fault-tolerant flight control system design against partial actuator failures. In: IEEE Transactions on Control Systems Technology, Vol. 20, No. 4, pp. 871–886, 2012

    Google Scholar 

  13. Isermann Rolf, Peter Balle.: Trends in the application of model-based fault detection and diagnosis of technical processes. In: Control engineering practice, Vol. 5, No. 5, pp. 709–719, 1997

    Google Scholar 

  14. Zhang Xiaodong, Thomas Parisini, Marios M Polycarpou.: Sensor bias fault isolation in a class of nonlinear systems. In: IEEE Transactions on Automatic Control, Vol. 50, No. 3, pp. 370–376, 2005

    Google Scholar 

  15. Xu Yufei, Bin Jiang, Zhifeng Gao, Ke Zhang.: Fault tolerant control for near space vehicle: a survey and some new results. In: Journal of Systems Engineering and Electronics, Vol. 22, No. 1, pp. 88–94, 2011

    Google Scholar 

  16. Talebi H A, K Khorasani.: A neural network-based multiplicative actuator fault detection and isolation of nonlinear systems. In: IEEE Transactions on Control Systems Technology, Vol. 21, No. 3, pp. 842–851, 2013

    Google Scholar 

  17. Muenchhof Marco, Mark Beck, Rolf Isermann.: Fault-tolerant actuators and drives-Structures, fault detection principles and applications. In: Annual Reviews in Control, Vol. 33, No. 2, pp. 136–148, 2009

    Google Scholar 

  18. Kelly Emma M, L M Bartlett.: Improved fault diagnostics of a dynamic aircraft fuel system using the digraph approach. In: Proceedings of the European Safety and Reliability Conference, Risk, Reliability and Societal Safety, pp. 801–808, 2007

    Google Scholar 

  19. Yousef Shatnawi, Mahmood Al-Khassaweneh.: Fault Diagnosis in Internal Combustion Engines Using Extension Neural Network. In: IEEE Transactions on Industrial Electronics, Vol. 61, No. 3, pp. 1434–1443, 2014

    Google Scholar 

  20. Tayarani-Bathaie Seyed Sina, Zakieh Nasim Sadough Vanini, Khashayar Khorasani.: Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing, Vol. 125, No. 11, pp. 153–165, 2014

    Google Scholar 

  21. Papadopoulos Yiannis.: Model-based system monitoring and diagnosis of failures using state charts and fault trees. In: Reliability Engineering & System Safety, Vol. 81, No. 3, pp. 325–341, 2003

    Google Scholar 

  22. Jimenez Juan F, Jose M Giron-Sierra, C Insaurralde, M Seminario.: A simulation of aircraft fuel management system. In: Simulation Modelling Practice and Theory, Vol. 15, No. 5, pp. 544–564, 2007

    Google Scholar 

  23. Narasimhan Sriram, Gautam Biswas.: Model-based diagnosis of hybrid systems. In: IEEE Transactions on Systems, Man, and Cybernetics, Vol. 37, No. 3, pp. 348–361, 2007

    Google Scholar 

  24. Bartlett Lisa M, E E Hurdle, Emma M Kelly.: Integrated system fault diagnostics utilising digraph and fault tree-based approaches. In: Reliability Engineering & System Safety, Vol. 94, No. 6, pp. 1107–1115, 2009

    Google Scholar 

  25. Shen Ting, Fangyi Wan, Weimin Cui, Bifeng Song.: Application of prognostic and health management technology on aircraft fuel system. In: Prognostics and Health Management Conference of IEEE, pp. 1–7, 2010

    Google Scholar 

  26. T.R. Sumithira, A. Nirmal Kumar.: Elimination of Harmonics in Multilevel Inverters Connected to Solar Photovoltaic Systems Using ANFIS: An Experimental Case Study. In: Journal of Applied Research and Technology, vol. 11, pp. 124–132, February 2013

    Google Scholar 

  27. J.J. Mora, G. Carrillo, L. Perez.: Fault Location in Power Distribution Systems using ANFIS Nets and Current Patterns. In: Transmission and Distribution Conference and Exposition Latin America, pp. 1–6, 2006

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vijaylakshmi S. Jigajinni .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Singapore

About this paper

Cite this paper

Jigajinni, V.S., Vanam Upendranath (2017). ANFIS-Based Fault Diagnosis Tool for a Typical Small Aircraft Fuel System. In: Singh, R., Choudhury, S. (eds) Proceeding of International Conference on Intelligent Communication, Control and Devices . Advances in Intelligent Systems and Computing, vol 479. Springer, Singapore. https://doi.org/10.1007/978-981-10-1708-7_45

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-1708-7_45

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1707-0

  • Online ISBN: 978-981-10-1708-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics