scholarly journals Multi‐attribute quantitative bearing fault diagnosis based on convolutional neural network

2021 ◽  
Author(s):  
Shixin Zhang ◽  
Qin Lv ◽  
Shenlin Zhang ◽  
Jianhua Shan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis

Measurement ◽  
2021 ◽  
Vol 186 ◽  
pp. 110236
Author(s):  
Fengqian Zou ◽  
Haifeng Zhang ◽  
Shengtian Sang ◽  
Xiaoming Li ◽  
Wanying He ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
One Dimension ◽  
Neural Network Learning ◽  
Network Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Convolutional Neural Network Considering the Effects of Noise for Bearing Fault Diagnosis

2020 ◽  
Author(s):  
Ilyoung Han ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Taeyun Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
Training Data ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
System Noise ◽  
Novel Method ◽  
Maintenance Activities

Abstract Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image

Measurement ◽  
2020 ◽  
Vol 157 ◽  
pp. 107667 ◽  
Author(s):  
Ying Zhang ◽  
Kangshuo Xing ◽  
Ruxue Bai ◽  
Dengyun Sun ◽  
Zong Meng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 137395-137406 ◽  
Author(s):  
Laohu Yuan ◽  
Dongshan Lian ◽  
Xue Kang ◽  
Yuanqiang Chen ◽  
Kejia Zhai
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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