# Bearing fault diagnosis based on spectrum images of vibration signals

arXiv: https://arxiv.org/abs/1511.02503 · HTML: https://mlsanity.com/paper/1511.02503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2015.

## Built on by (most cited first, 1 of 1)

- [Bearing fault diagnosis based on multi-scale spectral images and convolutional neural network](https://mlsanity.com/paper/2503.21566/) (2025)

## Abstract (arXiv)

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.