Bearing fault diagnosis based on spectrum images of vibration signals

TL;DR

This paper introduces a novel bearing fault diagnosis method using spectrum images of vibration signals, processed with 2DPCA and classification techniques, demonstrating effectiveness through experimental validation.

Contribution

The paper presents a new image-based feature extraction approach for bearing fault diagnosis, combining spectrum images and 2DPCA for improved classification accuracy.

Findings

Effective fault classification achieved

Spectrum images improve feature representation

Method validated with experimental data

Abstract

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.