Deep neural networks-based rolling bearing fault diagnosis

2017 ◽  
Vol 75 ◽  
pp. 327-333 ◽  
Author(s):  
Zhiqiang Chen ◽  
Shengcai Deng ◽  
Xudong Chen ◽  
Chuan Li ◽  
René-Vinicio Sanchez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yaochun Wu ◽  
Rongzhen Zhao ◽  
Wuyin Jin ◽  
Linfeng Deng ◽  
Tianjing He ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Clustering Algorithm ◽  
Rolling Bearing ◽  
Fault Identification ◽  
Layer By Layer ◽  
Bearing Fault Diagnosis ◽  
Novel Method ◽  
Low Dimensional

Deep learning (DL) has been successfully used in fault diagnosis. Training deep neural networks, such as convolutional neural networks (CNNs), require plenty of labeled samples. However, in mechanical fault diagnosis, labeled data are costly and time-consuming to collect. A novel method based on a deep convolutional autoencoding network (DCAEN) and adaptive nonparametric weighted-feature extraction Gustafson–Kessel (ANW-GK) clustering algorithm was developed for the fault diagnosis of bearings. First, the DCAEN that is pretrained layer by layer by unlabeled samples and fine-tuned by a few labeled samples is applied to learn representative features from the vibration signals. Then, the learned representative features are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the low-dimensional main features are obtained. Finally, the low-dimensional features are input ANW-GK clustering for fault identification. Two datasets were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can effectively diagnose different fault types with only a few labeled samples.

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