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

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Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

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Abstract

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.

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Acknowledgement

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.

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Authors and Affiliations

  1. B. C. Roy Engineering College, Durgapur, 713209, India

    Tribeni Prasad Banerjee

  2. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, 98071, USA

    Ajith Abraham

Authors
  1. Tribeni Prasad Banerjee
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  2. Ajith Abraham
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Corresponding author

Correspondence to Ajith Abraham .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Banerjee, T.P., Abraham, A. (2018). A Support Vector Machine Based Approach to Real Time Fault Signal Classification for High Speed BLDC Motor. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_80

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_80

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

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