Abstract:
This research involves the use of Motor current signature analysis (MCSA) to detect and diagnose induction motor faults. MCSA uses line current spectrum of electromechanical energy conversion system for locating faults frequencies. The occurrence of faults make the line current frequency of faulty motor differs from frequency of healthy motor. Data acquisition system (DAQ) based on dsPIC30F4011 microcontroller was developed to measure and monitor line current, frequency and temperature of induction motor. DAQ recognize fault signature produced in induction motor faults and estimate the severity of faults under different faults conditions. Faults detection and diagnosis was carried out by studying the effect of these faults on the motor current spectra of an induction motor and these were investigated through experiment. In order to perform the analysis on induction motor, a dedicated test bed was developed to experimentally simulate faults. Fast Fourier Transform (FFT) analysis of current signal for each of the various simulated faults was performed using MATLAB software. Experimental results showed that the magnitude of harmonics increases as more fault is been introduced. Considering the magnitude of positive definite peak harmonics between frequencies 4000Hz and 4500Hz of the spectrum; for short circuit faults it increases as 8dB, 10dB, and 13dB for inter-turn, inter-coil and inter-phase respectively, for bearing faults it increases as 7dB and 9dB for one ball and two ball defects respectively and for broken bar the magnitude are 9dB and 4dB for one and two broken bar respectively. Comparative analysis of spectra signatures of stator line current of healthy motor with that of faulty motor showed that MCSA and FFT alongside with data acquisition system based on dsPIC30F4011 microcontroller are effective in detecting and diagnosing electrical and mechanical fault in induction motor
