scholarly journals A method for constructing automatic rolling bearing fault identification model based on refined composite multi-scale dispersion entropy

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qingfeng Wang ◽  
Yang Xiao ◽  
Shuai Wang ◽  
Wencai Liu ◽  
Xiaojin Liu
Keyword(s):  
Rolling Bearing ◽  
Fault Identification ◽  
Bearing Fault ◽  
Multi Scale ◽  
Model Based ◽  
Identification Model

Bearing Fault Detection Based on Multi-Scale Laplace Wavelet Transform Spectrum

2013 ◽  
Vol 739 ◽  
pp. 413-417
Author(s):  
Ya Ning Wang
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Three Dimensions ◽  
Noise Condition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Integrated Technique ◽  
Envelope Spectrum

Laplace wavelet transform is self-adaptive to non-stationary and non-linear signal, which can detect the singularity characteristic of a signal precisely under strong background noise condition. A new method of bearing fault diagnosis based on multi-scale Laplace wavelet transform spectrum is proposed. The multi scale Laplace wavelet transform spectrum technique combines the advantages of Laplace wavelet transform, envelope spectrum and three dimensions color map into one integrated technique. The bearing fault vibration signal is firstly decomposed using Laplace wavelet transform. In the end, the multi scale Laplace wavelet transform spectrum is obtained and the characteristics of the bearing fault can be recognized according to the multi-scale Laplace wavelet transform spectrum. The proposed method has been verified by vibration signals obtained from rolling bearing with inner race fault.

scholarly journals Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network

2021 ◽  
Vol 1207 (1) ◽  
pp. 012003
Author(s):  
Xukun Hou ◽  
Pengjie Hu ◽  
Wenliao Du ◽  
Xiaoyun Gong ◽  
Hongchao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
One Dimensional ◽  
Bearing Fault ◽  
Multi Scale ◽  
Reconstructed Signal ◽  
Using Data

Abstract Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.

Multi-Scale Hermitian Wavelet Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 132-136 ◽  
Author(s):  
Hui Li
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Three Dimensions ◽  
Noise Condition ◽  
Outer Race ◽  
Bearing Fault ◽  
Multi Scale ◽  
Integrated Technique ◽  
Envelope Spectrum

Hermitian wavelet is a low-oscillation, complex valued wavelet, which can detect the singularity characteristic of a signal precisely under strong background noise condition. A new method of bearing fault diagnosis based on multi-scale Hermitian wavelet envelope spectrum is proposed. The multi scale Hermitian wavelet envelope spectrum technique combines the advantages of Hermitian wavelet transform, envelope spectrum and three dimensions color map into one integrated technique. The bearing fault vibration signal is firstly decomposed using Hermitian continuous wavelet transform. Then the real and imaginary parts are obtained. In the end, the multi scale Hermitian wavelet envelope spectrum is obtained and the characteristics of the bearing fault can be recognized according to the multi-scale Hermitian wavelet envelope spectrum. The proposed method has been proved by vibration signals obtained from rolling bearing with inner or outer race fault. The experimental results verified the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document