A Support Vector Machine Based Approach to Real Time Fault Signal Classification for High Speed BLDC Motor

  • Tribeni Prasad Banerjee
  • Ajith Abraham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


In this paper we propose a new methodology for designing an intelligent incipient fault signal classifier. This classifier can classify the fault signal. The design has been validated to a sate observer which indicates the valve controller output signal and communicate the health status of the embedded processor based valve controller in right time without any false alert signal to the actuator through FPGA processor. This has been achieved by using an SVM-based classifier and time duration based state machine modeling. The design methodology of a fault aware controller using one against all strategy is selected for classification tool due to good generalization properties. Performance of the proposed system is validated by applying the system to induction motor faults diagnosis. Experimental result for BLDC motor (which is mostly used for aircraft) valve controller, and computer simulations indicate that the proposed scheme for intelligent control based on signal classification is simple and robust, with good accuracy.


Cyber physical system Short Term Fourier Transform (STFT) Support Vector Machine (SVM) Intelligent control Fault signal classifier 



The author is thankful to Embedded System Lab of CMERI, Durgapur for giving support to his work in experimental setup and funding support to continue his research.


  1. 1.
    Yongming, Y., Bin, W.: A review on induction motor online fault diagnosis. In: The 3rd International Power Electronic and Motion Control Conference 2000 (IEEE IPEMC), pp. 1353–1358 (2000)Google Scholar
  2. 2.
    Matthias, P., Stefan, O., Manfred, G.: Support vector approaches for engine knock detection. In: International Joint Conference on Neural Networks. IEEE Press, Washington, pp. 969–974 (1999)Google Scholar
  3. 3.
    Chiang, L.H., Kotanchek, M.E., Kordon, A.K.: Fault diagnosis based on fisher discriminant analysis and support vector machines. Comput. Chem. Eng. 28, 1389–1401 (2004)CrossRefGoogle Scholar
  4. 4.
    Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A.: Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng. Appl. Artif. Intell. 16, 657–665 (2003)CrossRefGoogle Scholar
  5. 5.
    Schölkopf, B., Smola, A.: Learning With Kernels. MIT Press, Cambridge, MA (2002)zbMATHGoogle Scholar
  6. 6.
    Herbrich, R.: Learning Kernel Classifiers: Theory and Algorithms. MIT Press, Cambridge, MA (2002)Google Scholar
  7. 7.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK (2000)zbMATHGoogle Scholar
  8. 8.
    Sebald, D.J., Bucklew, J.A.: Support vector machine techniques for nonlinear equalization. IEEE Trans. Signal Process. 48(11), 3217–3226 (2000)CrossRefGoogle Scholar
  9. 9.
    Jack, L.B., Nandi, A.K.: Support vector machines for detection and characterization of rolling element bearing faults. J. Mech. Eng. Sci. 9, 1065–1074 (2001)CrossRefGoogle Scholar
  10. 10.
    Bengtsson, J., Yi, W.: Timed automata: semantics, algorithms and tools. In: Lectures on Concurrency and Petri Nets, pp. 87–124. Springer, Heidelberg (2004).Google Scholar
  11. 11.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1999)zbMATHGoogle Scholar
  12. 12.
    Jack, L.B., Nandi, A.K.: Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mech. Syst. Signal Process. 16(2), 373–390 (2002)CrossRefGoogle Scholar
  13. 13.
    Samanta, B.: Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech. Syst. Signal Process. 18(3), 625–644 (2004)CrossRefGoogle Scholar
  14. 14.
    Yang, B.-S., et al.: Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines. Mech. Syst. Signal Process. 19(2), 371–390 (2005)CrossRefGoogle Scholar
  15. 15.
    Yang, B.-S., et al.: Cavitation detection of butterfly valve using support vector machines. J. Sound vib. 287(1), 25–43 (2005)CrossRefGoogle Scholar
  16. 16.
    Yang, B.-S., Han, T., Hwang, W.-W.: Fault diagnosis of rotating machinery based on multi-class support vector machines. J. Mech. Sci. Technol. 19(3), 846–859 (2005)CrossRefGoogle Scholar
  17. 17.
    Alur, R., Dill, D.: The Theory of Timed Automata. Theoret. Comput. Sci. 126, 183–235 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ma, X.-x., Huang, X.-y., Chai, Y.: 2PTMC classification algorithm based on support vector machines and its application to fault diagnosis. Control Decis. 18(3), 272–276 (2003)Google Scholar
  19. 19.
    Getting Started User Guide, MAX II Development Kit, Altera, USA, 2005.
  20. 20.
    Mall, R.: Real Time Systems Theory and Practice. Pearson Publication, India (2007)Google Scholar
  21. 21.
    Matthias, P., Stefan, O., Manfred, G.: Support vector approaches for engine knock detection, In: International Joint Conference on Neural Networks. IEEE Press, Washington, pp. 969–974 (1999)Google Scholar
  22. 22.
    Texas Instruments: TMS320F2812 Digital Signal Processors Data Manual (2005)Google Scholar
  23. 23.
    Texas Instruments: TMS320F28x DSP System Control and Interrupts Reference Guide (2005)Google Scholar
  24. 24.
    Texas Instruments: TMS320F28x DSP Event Manager (EV) Reference Guide (2004)Google Scholar
  25. 25.
    Texas Instruments: TMS320F28x DSP External Interface (XINTF) Reference Guide (2004)Google Scholar
  26. 26.
    Texas Instruments: Running an Application from Internal Flash Memory on the TMS320F28xx DSP (2005)Google Scholar
  27. 27.
    Texas Instruments: TMS320F28x DSP Serial Communication Interface (SCI) Reference Guide (2004)Google Scholar
  28. 28.
    Texas Instruments: TMS320F28x DSP Enhanced Controller Area Network (eCAN) Reference Guide (2005)Google Scholar
  29. 29.
    Texas Instruments: TMS320F28x Analog-to-Digital Converter (ADC) Reference Guide (2002)Google Scholar
  30. 30.
    Simeu-Abazi, Z., Bouredji, Z.: Monitoring and predictive maintenance: Modeling and analyse of fault latency. Comput. Ind. 57(6), 504–515 (2006)CrossRefGoogle Scholar
  31. 31.
    Bouyer, P.: Timed Automata-From Theory to Implementation LSV- CNRS & ENS de Cachan France (2003)Google Scholar
  32. 32.
    Rubežić, V., Djurović, I., Daković, M.: Time–frequency representations-based detector of chaos in oscillatory circuits. Signal Process. 86(9), 2255–2270 (2006)CrossRefzbMATHGoogle Scholar
  33. 33.
    Boashash, B. (ed.): Time frequency Signal Analysis and Applications. Elsevier, Amsterdam (2003)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.B. C. Roy Engineering CollegeDurgapurIndia
  2. 2.Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research ExcellenceAuburnUSA

Personalised recommendations