Implementation of a New Hybrid Methodology for Fault Signal Classification Using Short -Time Fourier Transform and Support Vector Machines

  • Tribeni Prasad Banerjee
  • Swagatam Das
  • Joydeb Roychoudhury
  • Ajith Abraham
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)


Increasing the safety of a high-speed motor used in aerospace application is a critical issue. So an intelligent fault aware control methodology is highly research motivated area, which can effectively identify the early fault of a motor from its signal characteristics. The signal classification and the control strategy with a hybrid technique are proposed in this paper. This classifier can classify the original signal and the fault signal. The performance of the system is validated by applying the system to induction motor faults diagnosis. According to our experiments in BLDC motor controller results, the system has potential to serve as an intelligent fault diagnosis system in other hard real time system application. To make the system more robust we make the controller more adaptive that give the system response more reliable.


Real time system Support Vector Machine Sort Time Fourier Transform embedded system Brash less Direct Current Motor Intelligent interactive control supervisory control signal classification Fault Classifier Mechatronics fault diagnosis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tribeni Prasad Banerjee
    • 1
  • Swagatam Das
    • 2
  • Joydeb Roychoudhury
    • 1
  • Ajith Abraham
    • 3
  1. 1.Embedded System LaboratoryCentral Mechanical Engineering Research InstituteDurgapurIndia
  2. 2.Electronics and Telecommunication Engineering DepartmentJadavpur UniversityJadavpurIndia
  3. 3.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceUSA

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