Automatic Speed Control of Motor via WAD Technique for Prevention of Faults in Motor - R. Kabilan; G. Selvakumar (International Journal of Power Electronics and Drive Systems (IJPEDS), 2018)
Many technologies are introduced in monitoring the fault occurrence in the electric motors used in industrial applications. Sound Accusation, current signature analysis, and vibration based motor fault detection systems are widely used in present years. From all these methods analyzing the motor vibration pattern produces more accuracy in finding an occurrence of different faults in the electric motor. The frequency of vibration generated by the MEMS vibration sensor differs for rotor, stator and bearing faults. The signal generated is analyzed using three important techniques namely wavelet analysis, Dyadic Transformation, and Adaptive Neuro-Fuzzy Inference System(WAD Technique). Hardware with ARM microcontroller and ADXL MEMS vibration sensor was used to perform signal acquisition, and the generated signal is processed using the MATLAB software, and the speed of the motor is controlled based on the processed result. The performance of the system with all three algorithms was recorded, and the efficiency of the system is compared.