Motor shaft misalignment detection using multiscale entropy with wavelet denoising

摘要

Misalignment of motor shaft (also manifesting as static eccentricity) is a common motor fault resulting from improper installation or damage of the machine components and their support structure. Spectrum analysis is generally used for online detection of such faults. This study presents a novel approach to discover features that distinguish the vibration signals of a normal motor from those of a misaligned one. These features are obtained from the difference of multiscale entropy of a signal, before and after the signal is denoised using wavelet transform. Experimental results show that classifiers based on these features obtain better and more stable accuracy rates than those based on frequency-related features.