This paper reports experimental studies to detect two faults in a 3-phase 1.5hp induction motor using intrinsic mode functions from Hilbert-Huang transform. The faults studied are the eccentricity of the air-gap between the rotor and stator and damage to the outer race of bearings. The experiments are conducted under four conditions: the normal no-fault condition, two single fault conditions and the multiple faults condition. Two microphones, one vibration sensor and one current sensor are used to collect sound, vibration and current data respectively. The data is analyzed using the Hilbert-Huang transform and Fast Fourier Transform. Features are extracted from the spectrum of intrinsic mode functions and the average value of their envelope. Three simple classifiers are used to classify these four experimental conditions. The results demonstrate that the multiple sensors do improve the classification rate and that the Intrinsic Mode Functions obtained by the Hilbert-Huang transform are more effective than FFT in classifying multiple faults.

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