Bearing fault diagnosis based on spectrum images of vibration signals
This addresses fault diagnosis in rotating machinery, but it is incremental as it adapts existing methods to a new feature type.
The paper tackled bearing fault diagnosis by proposing a novel feature using spectrum images from vibration signals, achieving effective fault classification verified with experimental data.
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.