scholarly journals A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 244
Author(s):  
Duy Tang Hoang ◽  
Xuan Toa Tran ◽  
Mien Van ◽  
Hee Jun Kang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Superior Performance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Sensor Signals ◽  
Multiple Sensor ◽  
Single Sensor

This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.

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.

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