Search

A hysteresis current controller (HCC) is commonly used in high-performance AC motor drives to control the current directly. Recently, the predictive current controller (PCC) is also used as an alternative to the classical current controller for speed and torque regulation of induction motor (IM) drives. However, PCC has drawbacks of large flux and torque ripples, large total harmonic distortions (THDs) in current and voltage and dependency on parameters. This paper proposes current control with artificial neural network (ANN) for a field-oriented controlled induction motor (FOCIM) drive. The ANN has input current error between the reference and the measured stator currents. The output function of neuron is a hyperbolic tan (or tan-sigmoid) function to apply error Levenberg–Marquardt (L–M) back propagation as learning rule because of its fast convergence. The proposed method is based on a new approach in which hysteresis band is replaced by ANN comparator to improve the performance of the FOCIM drive. It minimizes torque ripples, flux ripples, voltage and current THDs over the existing HCC and PCC methods. The superiority of the proposed method compared to existing methods is established by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%. [ABSTRACT FROM AUTHOR]