Fault Diagnosis of Bearings with Adjusted Vibration Spectrum Images

Abstract

In order to diagnose bearing faults under different operating state and limited sample condition, a fault diagnosis method based on adjusted spectrum image of vibration signal is proposed in this paper. Firstly, the Davies–Bouldin index (DBI) is employed to select a proper capture focus (CF) and image size, and the spectrum of vibration signal is computed via fast Fourier transformation (FFT) and adjusted according to the average rotating speed. Then, the spectrum is plotted and captured as a two-dimensional (2D) image with the optimized CF and image size. Two-dimensional principal component analysis (2DPCA) is used to reduce the dimension of images, and finally a nearest neighbour method is applied to classify the faults of bearings. Two experiments are carried out to validate the effectiveness of the proposed method. Besides, a further investigation on the effect of spectrum frequency resolution is conducted and a recommended selection method of frequency resolution is given based on the experimental performances. In our method, the training samples could be from only one operating condition, while the testing samples are from all possible operation conditions. All experiment results have demonstrated that the proposed method could achieve high classification accuracy even with very limited training samples.

Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network

“…For conventional intelligent fault diagnosis approaches, feature extraction from the vibration signals is a very important step. FFT (fast Fourier transform) spectral analysis is a one of the commonly used methods for signal preprocessing and feature extraction [38]. ere are many feature extraction methods involving FFT spectral analysis.…”

“…ere are four types of commonly used vibration images, including 2D rearrangement image of 1D signal [16], time-domain waveform image [37], spectrum image [38,39], and time-frequency image (TFI) [14,15,40]. Since TFI can better uncover the dynamic properties of nonstationary vibration signals, it is used as the input data of the deep network models in this paper.…”

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

“…Huang_2021 [115] Attoui_2020 [116] Wang_2019b [28] Klausen_2019 [117] Dybała_2018 [118] Qiu_2018 [119] Li_2016d [120] Dolenc_2016 [121] Harmouche_2015 [122] Hafeez_2003 [123] Zakhezin_2010 [124] Wang_2019c [125] Feng_2017 [126] Guoji_2014 [127] Gelman_2005 [128] Elbhbah_2013 [129] Cardona-Morales_2014 [130] Time-frequency-based methods…”

“…Klausen and Robbersmyr in [117] developed the whitened cross-correlation spectrum (WCCS) method based on the cross-correlation between the whitened vibration signal and its envelope, with good performances in the bearings damages early detection. In [119], Qiu et al generate two-dimensional (2D) images, starting from the signal FFT-based spectrum, reduce the images dimension through the two-dimensional principal component analysis (2DPCA), and finally apply a k-nearest neighbor method to classify bearing faults. Dolenc et al, in [121], verified that localized and distributed faults could be distinguished by comparing envelope spectra of vibration signals.…”

Demodulation of Vibration Signal Based on Envelope-Kurtogram for Ball Bearing Fault Detection

“…Bearing condition diagnosis consists of several steps aimed at extracting bearing characteristics from a signal [1]. Although there are many types of signals, vibration signals are the most widely used signals because they are non-invasive, real-time, and do not interfere with machine work when sampling the signal [2].…”