  Bearing fault diagnosis has been a challenge in the monitoring activities of
rotating machinery, and it's receiving more and more attention. The
conventional fault diagnosis methods usually extract features from the
waveforms or spectrums of vibration signals in order to realize fault
classification. In this paper, a novel feature in the form of images is
presented, namely the spectrum images of vibration signals. The spectrum images
are simply obtained by doing fast Fourier transformation. Such images are
processed with two-dimensional principal component analysis (2DPCA) to reduce
the dimensions, and then a minimum distance method is applied to classify the
faults of bearings. The effectiveness of the proposed method is verified with
experimental data.
