A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network

2019 ◽  
Vol 30 (5) ◽  
pp. 055402 ◽  
Author(s):  
Ping Ma ◽  
Hongli Zhang ◽  
Wenhui Fan ◽  
Cong Wang ◽  
Guangrui Wen ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Image Representation ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network

2019 ◽  
Vol 13 (3) ◽  
pp. 5689-5702
Author(s):  
N. Fathiah Waziralilah ◽  
Aminudin Abu ◽  
M. H. Lim ◽  
Lee Kee Quen ◽  
Ahmed Elfakarany
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Image Representation ◽  
Gabor Transform ◽  
Spatial Awareness ◽  
Bearing Fault ◽  
Vibration Data ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Model

The vast impact on machinery that is rooted by bearing degradation thus pinpointing bearing fault diagnosis as indubitably very crucial. The research is innovated to diagnose the fault in bearing by implementing deep learning approach which is Convolutional Neural Network (CNN) that has superiority over image processing and pattern recognition. A novel model comprises of Gabor Transform and CNN is proposed whereby Gabor Transform is utilized in representing the raw vibration signals into its image representation. The CNN architecture is augmented for a better accuracy of the bearing fault diagnosis model. To date, the method combination has never been deployed in establishing fault diagnosis model. Plus, the usage of Gabor Transform in mechanical area especially in bearing fault diagnosis is meagrely reported. Scant researches in mechanical diagnosis are dedicated to work on the image representation of the vibration data whereas the CNN works better when fed by images input due to its unique strength of CNN in images processing and spatial awareness. At the end of the research, it is perceived that the proposed model comprises of Gabor Transform and CNN can diagnose the bearing faults with 100% accuracy and perform better than when CNN is fed with raw signals.

scholarly journals A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 345
Author(s):  
Van-Cuong Nguyen ◽  
Duy-Tang Hoang ◽  
Xuan-Toa Tran ◽  
Mien Van ◽  
Hee-Jun Kang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Image Representation ◽  
Vibration Signal ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Multiple Domain

Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.

A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network

Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109088
Author(s):  
Jing Zhao ◽  
Shaopu Yang ◽  
Qiang Li ◽  
Yongqiang Liu ◽  
Xiaohui Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Image Mapping ◽  
Diagnosis Method

scholarly journals Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7319
Author(s):  
Jiajun He ◽  
Ping Wu ◽  
Yizhi Tong ◽  
Xujie Zhang ◽  
Meizhen Lei ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Vibration Signals ◽  
Maintenance Costs ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Method

Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

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